Cancer survival analysis using semi-supervised learning method based on Cox and AFT models with L1/2 regularization.

PubMed

Liang, Yong; Chai, Hua; Liu, Xiao-Ying; Xu, Zong-Ben; Zhang, Hai; Leung, Kwong-Sak

2016-03-01

One of the most important objectives of the clinical cancer research is to diagnose cancer more accurately based on the patients' gene expression profiles. Both Cox proportional hazards model (Cox) and accelerated failure time model (AFT) have been widely adopted to the high risk and low risk classification or survival time prediction for the patients' clinical treatment. Nevertheless, two main dilemmas limit the accuracy of these prediction methods. One is that the small sample size and censored data remain a bottleneck for training robust and accurate Cox classification model. In addition to that, similar phenotype tumours and prognoses are actually completely different diseases at the genotype and molecular level. Thus, the utility of the AFT model for the survival time prediction is limited when such biological differences of the diseases have not been previously identified. To try to overcome these two main dilemmas, we proposed a novel semi-supervised learning method based on the Cox and AFT models to accurately predict the treatment risk and the survival time of the patients. Moreover, we adopted the efficient L1/2 regularization approach in the semi-supervised learning method to select the relevant genes, which are significantly associated with the disease. The results of the simulation experiments show that the semi-supervised learning model can significant improve the predictive performance of Cox and AFT models in survival analysis. The proposed procedures have been successfully applied to four real microarray gene expression and artificial evaluation datasets. The advantages of our proposed semi-supervised learning method include: 1) significantly increase the available training samples from censored data; 2) high capability for identifying the survival risk classes of patient in Cox model; 3) high predictive accuracy for patients' survival time in AFT model; 4) strong capability of the relevant biomarker selection. Consequently, our proposed semi-supervised learning model is one more appropriate tool for survival analysis in clinical cancer research.

A Risk Stratification Model for Lung Cancer Based on Gene Coexpression Network and Deep Learning

PubMed Central

2018-01-01

Risk stratification model for lung cancer with gene expression profile is of great interest. Instead of previous models based on individual prognostic genes, we aimed to develop a novel system-level risk stratification model for lung adenocarcinoma based on gene coexpression network. Using multiple microarray, gene coexpression network analysis was performed to identify survival-related networks. A deep learning based risk stratification model was constructed with representative genes of these networks. The model was validated in two test sets. Survival analysis was performed using the output of the model to evaluate whether it could predict patients' survival independent of clinicopathological variables. Five networks were significantly associated with patients' survival. Considering prognostic significance and representativeness, genes of the two survival-related networks were selected for input of the model. The output of the model was significantly associated with patients' survival in two test sets and training set (p < 0.00001, p < 0.0001 and p = 0.02 for training and test sets 1 and 2, resp.). In multivariate analyses, the model was associated with patients' prognosis independent of other clinicopathological features. Our study presents a new perspective on incorporating gene coexpression networks into the gene expression signature and clinical application of deep learning in genomic data science for prognosis prediction. PMID:29581968

Reporting and methodological quality of survival analysis in articles published in Chinese oncology journals.

PubMed

Zhu, Xiaoyan; Zhou, Xiaobin; Zhang, Yuan; Sun, Xiao; Liu, Haihua; Zhang, Yingying

2017-12-01

Survival analysis methods have gained widespread use in the filed of oncology. For achievement of reliable results, the methodological process and report quality is crucial. This review provides the first examination of methodological characteristics and reporting quality of survival analysis in articles published in leading Chinese oncology journals.To examine methodological and reporting quality of survival analysis, to identify some common deficiencies, to desirable precautions in the analysis, and relate advice for authors, readers, and editors.A total of 242 survival analysis articles were included to be evaluated from 1492 articles published in 4 leading Chinese oncology journals in 2013. Articles were evaluated according to 16 established items for proper use and reporting of survival analysis.The application rates of Kaplan-Meier, life table, log-rank test, Breslow test, and Cox proportional hazards model (Cox model) were 91.74%, 3.72%, 78.51%, 0.41%, and 46.28%, respectively, no article used the parametric method for survival analysis. Multivariate Cox model was conducted in 112 articles (46.28%). Follow-up rates were mentioned in 155 articles (64.05%), of which 4 articles were under 80% and the lowest was 75.25%, 55 articles were100%. The report rates of all types of survival endpoint were lower than 10%. Eleven of 100 articles which reported a loss to follow-up had stated how to treat it in the analysis. One hundred thirty articles (53.72%) did not perform multivariate analysis. One hundred thirty-nine articles (57.44%) did not define the survival time. Violations and omissions of methodological guidelines included no mention of pertinent checks for proportional hazard assumption; no report of testing for interactions and collinearity between independent variables; no report of calculation method of sample size. Thirty-six articles (32.74%) reported the methods of independent variable selection. The above defects could make potentially inaccurate, misleading of the reported results, or difficult to interpret.There are gaps in the conduct and reporting of survival analysis in studies published in Chinese oncology journals, severe deficiencies were noted. More endorsement by journals of the report guideline for survival analysis may improve articles quality, and the dissemination of reliable evidence to oncology clinicians. We recommend authors, readers, reviewers, and editors to consider survival analysis more carefully and cooperate more closely with statisticians and epidemiologists. Copyright © 2017 The Authors. Published by Wolters Kluwer Health, Inc. All rights reserved.

[Survival analysis with competing risks: estimating failure probability].

PubMed

Llorca, Javier; Delgado-Rodríguez, Miguel

2004-01-01

To show the impact of competing risks of death on survival analysis. We provide an example of survival time without chronic rejection after heart transplantation, where death before rejection acts as a competing risk. Using a computer simulation, we compare the Kaplan-Meier estimator and the multiple decrement model. The Kaplan-Meier method overestimated the probability of rejection. Next, we illustrate the use of the multiple decrement model to analyze secondary end points (in our example: death after rejection). Finally, we discuss Kaplan-Meier assumptions and why they fail in the presence of competing risks. Survival analysis should be adjusted for competing risks of death to avoid overestimation of the risk of rejection produced with the Kaplan-Meier method.

Multilevel mixed effects parametric survival models using adaptive Gauss-Hermite quadrature with application to recurrent events and individual participant data meta-analysis.

PubMed

Crowther, Michael J; Look, Maxime P; Riley, Richard D

2014-09-28

Multilevel mixed effects survival models are used in the analysis of clustered survival data, such as repeated events, multicenter clinical trials, and individual participant data (IPD) meta-analyses, to investigate heterogeneity in baseline risk and covariate effects. In this paper, we extend parametric frailty models including the exponential, Weibull and Gompertz proportional hazards (PH) models and the log logistic, log normal, and generalized gamma accelerated failure time models to allow any number of normally distributed random effects. Furthermore, we extend the flexible parametric survival model of Royston and Parmar, modeled on the log-cumulative hazard scale using restricted cubic splines, to include random effects while also allowing for non-PH (time-dependent effects). Maximum likelihood is used to estimate the models utilizing adaptive or nonadaptive Gauss-Hermite quadrature. The methods are evaluated through simulation studies representing clinically plausible scenarios of a multicenter trial and IPD meta-analysis, showing good performance of the estimation method. The flexible parametric mixed effects model is illustrated using a dataset of patients with kidney disease and repeated times to infection and an IPD meta-analysis of prognostic factor studies in patients with breast cancer. User-friendly Stata software is provided to implement the methods. Copyright © 2014 John Wiley & Sons, Ltd.

The comparison of proportional hazards and accelerated failure time models in analyzing the first birth interval survival data

NASA Astrophysics Data System (ADS)

Faruk, Alfensi

2018-03-01

Survival analysis is a branch of statistics, which is focussed on the analysis of time- to-event data. In multivariate survival analysis, the proportional hazards (PH) is the most popular model in order to analyze the effects of several covariates on the survival time. However, the assumption of constant hazards in PH model is not always satisfied by the data. The violation of the PH assumption leads to the misinterpretation of the estimation results and decreasing the power of the related statistical tests. On the other hand, the accelerated failure time (AFT) models do not assume the constant hazards in the survival data as in PH model. The AFT models, moreover, can be used as the alternative to PH model if the constant hazards assumption is violated. The objective of this research was to compare the performance of PH model and the AFT models in analyzing the significant factors affecting the first birth interval (FBI) data in Indonesia. In this work, the discussion was limited to three AFT models which were based on Weibull, exponential, and log-normal distribution. The analysis by using graphical approach and a statistical test showed that the non-proportional hazards exist in the FBI data set. Based on the Akaike information criterion (AIC), the log-normal AFT model was the most appropriate model among the other considered models. Results of the best fitted model (log-normal AFT model) showed that the covariates such as women’s educational level, husband’s educational level, contraceptive knowledge, access to mass media, wealth index, and employment status were among factors affecting the FBI in Indonesia.

[Use of bivariate survival curves for analyzing mortality of heart failure and sudden death in dilated cardiomiopathy].

PubMed

Gregori, Dario; Rosato, Rosalba; Zecchin, Massimo; Di Lenarda, Andrea

2005-01-01

This paper discusses the use of bivariate survival curves estimators within the competing risk framework. Competing risks models are used for the analysis of medical data with more than one cause of death. The case of dilated cardiomiopathy is explored. Bivariate survival curves plot the conjoint mortality processes. The different graphic representation of bivariate survival analysis is the major contribute of this methodology to the competing risks analysis.

Jointly modeling longitudinal proportional data and survival times with an application to the quality of life data in a breast cancer trial.

PubMed

Song, Hui; Peng, Yingwei; Tu, Dongsheng

2017-04-01

Motivated by the joint analysis of longitudinal quality of life data and recurrence free survival times from a cancer clinical trial, we present in this paper two approaches to jointly model the longitudinal proportional measurements, which are confined in a finite interval, and survival data. Both approaches assume a proportional hazards model for the survival times. For the longitudinal component, the first approach applies the classical linear mixed model to logit transformed responses, while the second approach directly models the responses using a simplex distribution. A semiparametric method based on a penalized joint likelihood generated by the Laplace approximation is derived to fit the joint model defined by the second approach. The proposed procedures are evaluated in a simulation study and applied to the analysis of breast cancer data motivated this research.

Neyman, Markov processes and survival analysis.

PubMed

Yang, Grace

2013-07-01

J. Neyman used stochastic processes extensively in his applied work. One example is the Fix and Neyman (F-N) competing risks model (1951) that uses finite homogeneous Markov processes to analyse clinical trials with breast cancer patients. We revisit the F-N model, and compare it with the Kaplan-Meier (K-M) formulation for right censored data. The comparison offers a way to generalize the K-M formulation to include risks of recovery and relapses in the calculation of a patient's survival probability. The generalization is to extend the F-N model to a nonhomogeneous Markov process. Closed-form solutions of the survival probability are available in special cases of the nonhomogeneous processes, like the popular multiple decrement model (including the K-M model) and Chiang's staging model, but these models do not consider recovery and relapses while the F-N model does. An analysis of sero-epidemiology current status data with recurrent events is illustrated. Fix and Neyman used Neyman's RBAN (regular best asymptotic normal) estimates for the risks, and provided a numerical example showing the importance of considering both the survival probability and the length of time of a patient living a normal life in the evaluation of clinical trials. The said extension would result in a complicated model and it is unlikely to find analytical closed-form solutions for survival analysis. With ever increasing computing power, numerical methods offer a viable way of investigating the problem.

A comparative study of mixture cure models with covariate

NASA Astrophysics Data System (ADS)

Leng, Oh Yit; Khalid, Zarina Mohd

2017-05-01

In survival analysis, the survival time is assumed to follow a non-negative distribution, such as the exponential, Weibull, and log-normal distributions. In some cases, the survival time is influenced by some observed factors. The absence of these observed factors may cause an inaccurate estimation in the survival function. Therefore, a survival model which incorporates the influences of observed factors is more appropriate to be used in such cases. These observed factors are included in the survival model as covariates. Besides that, there are cases where a group of individuals who are cured, that is, not experiencing the event of interest. Ignoring the cure fraction may lead to overestimate in estimating the survival function. Thus, a mixture cure model is more suitable to be employed in modelling survival data with the presence of a cure fraction. In this study, three mixture cure survival models are used to analyse survival data with a covariate and a cure fraction. The first model includes covariate in the parameterization of the susceptible individuals survival function, the second model allows the cure fraction to depend on covariate, and the third model incorporates covariate in both cure fraction and survival function of susceptible individuals. This study aims to compare the performance of these models via a simulation approach. Therefore, in this study, survival data with varying sample sizes and cure fractions are simulated and the survival time is assumed to follow the Weibull distribution. The simulated data are then modelled using the three mixture cure survival models. The results show that the three mixture cure models are more appropriate to be used in modelling survival data with the presence of cure fraction and an observed factor.

Comparison of Cox's Regression Model and Parametric Models in Evaluating the Prognostic Factors for Survival after Liver Transplantation in Shiraz during 2000-2012.

PubMed

Adelian, R; Jamali, J; Zare, N; Ayatollahi, S M T; Pooladfar, G R; Roustaei, N

2015-01-01

Identification of the prognostic factors for survival in patients with liver transplantation is challengeable. Various methods of survival analysis have provided different, sometimes contradictory, results from the same data. To compare Cox's regression model with parametric models for determining the independent factors for predicting adults' and pediatrics' survival after liver transplantation. This study was conducted on 183 pediatric patients and 346 adults underwent liver transplantation in Namazi Hospital, Shiraz, southern Iran. The study population included all patients undergoing liver transplantation from 2000 to 2012. The prognostic factors sex, age, Child class, initial diagnosis of the liver disease, PELD/MELD score, and pre-operative laboratory markers were selected for survival analysis. Among 529 patients, 346 (64.5%) were adult and 183 (34.6%) were pediatric cases. Overall, the lognormal distribution was the best-fitting model for adult and pediatric patients. Age in adults (HR=1.16, p<0.05) and weight (HR=2.68, p<0.01) and Child class B (HR=2.12, p<0.05) in pediatric patients were the most important factors for prediction of survival after liver transplantation. Adult patients younger than the mean age and pediatric patients weighing above the mean and Child class A (compared to those with classes B or C) had better survival. Parametric regression model is a good alternative for the Cox's regression model.

Joint modelling of longitudinal CEA tumour marker progression and survival data on breast cancer

NASA Astrophysics Data System (ADS)

Borges, Ana; Sousa, Inês; Castro, Luis

2017-06-01

This work proposes the use of Biostatistics methods to study breast cancer in patients of Braga's Hospital Senology Unit, located in Portugal. The primary motivation is to contribute to the understanding of the progression of breast cancer, within the Portuguese population, using a more complex statistical model assumptions than the traditional analysis that take into account a possible existence of a serial correlation structure within a same subject observations. We aim to infer which risk factors aect the survival of Braga's Hospital patients, diagnosed with breast tumour. Whilst analysing risk factors that aect a tumour markers used on the surveillance of disease progression the Carcinoembryonic antigen (CEA). As survival and longitudinal processes may be associated, it is important to model these two processes together. Hence, a joint modelling of these two processes to infer on the association of these was conducted. A data set of 540 patients, along with 50 variables, was collected from medical records of the Hospital. A joint model approach was used to analyse these data. Two dierent joint models were applied to the same data set, with dierent parameterizations which give dierent interpretations to model parameters. These were used by convenience as the ones implemented in R software. Results from the two models were compared. Results from joint models, showed that the longitudinal CEA values were signicantly associated with the survival probability of these patients. A comparison between parameter estimates obtained in this analysis and previous independent survival[4] and longitudinal analysis[5][6], lead us to conclude that independent analysis brings up bias parameter estimates. Hence, an assumption of association between the two processes in a joint model of breast cancer data is necessary. Results indicate that the longitudinal progression of CEA is signicantly associated with the probability of survival of these patients. Hence, an assumption of association between the two processes in a joint model of breast cancer data is necessary.

Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis.

PubMed

Attiyeh, Marc A; Chakraborty, Jayasree; Doussot, Alexandre; Langdon-Embry, Liana; Mainarich, Shiana; Gönen, Mithat; Balachandran, Vinod P; D'Angelica, Michael I; DeMatteo, Ronald P; Jarnagin, William R; Kingham, T Peter; Allen, Peter J; Simpson, Amber L; Do, Richard K

2018-04-01

Pancreatic cancer is a highly lethal cancer with no established a priori markers of survival. Existing nomograms rely mainly on post-resection data and are of limited utility in directing surgical management. This study investigated the use of quantitative computed tomography (CT) features to preoperatively assess survival for pancreatic ductal adenocarcinoma (PDAC) patients. A prospectively maintained database identified consecutive chemotherapy-naive patients with CT angiography and resected PDAC between 2009 and 2012. Variation in CT enhancement patterns was extracted from the tumor region using texture analysis, a quantitative image analysis tool previously described in the literature. Two continuous survival models were constructed, with 70% of the data (training set) using Cox regression, first based only on preoperative serum cancer antigen (CA) 19-9 levels and image features (model A), and then on CA19-9, image features, and the Brennan score (composite pathology score; model B). The remaining 30% of the data (test set) were reserved for independent validation. A total of 161 patients were included in the analysis. Training and test sets contained 113 and 48 patients, respectively. Quantitative image features combined with CA19-9 achieved a c-index of 0.69 [integrated Brier score (IBS) 0.224] on the test data, while combining CA19-9, imaging, and the Brennan score achieved a c-index of 0.74 (IBS 0.200) on the test data. We present two continuous survival prediction models for resected PDAC patients. Quantitative analysis of CT texture features is associated with overall survival. Further work includes applying the model to an external dataset to increase the sample size for training and to determine its applicability.

Temporal profile of inflammatory response to fracture and hemorrhagic shock: Proposal of a novel long-term survival murine multiple trauma model.

PubMed

Kleber, Christian; Becker, Christopher A; Malysch, Tom; Reinhold, Jens M; Tsitsilonis, Serafeim; Duda, Georg N; Schmidt-Bleek, Katharina; Schaser, Klaus D

2015-07-01

Hemorrhagic shock (hS) interacts with the posttraumatic immune response and fracture healing in multiple trauma. Due to the lack of a long-term survival multiple trauma animal models, no standardized analysis of fracture healing referring the impact of multiple trauma on fracture healing was performed. We propose a new long-term survival (21 days) murine multiple trauma model combining hS (microsurgical cannulation of carotid artery, withdrawl of blood and continuously blood pressure measurement), femoral (osteotomy/external fixation) and tibial fracture (3-point bending technique/antegrade nail). The posttraumatic immune response was measured via IL-6, sIL-6R ELISA. The hS was investigated via macrohemodynamics, blood gas analysis, wet-dry lung ration and histologic analysis of the shock organs. We proposed a new murine long-term survival (21 days) multiple trauma model mimicking clinical relevant injury patterns and previously published human posttraumatic immune response. Based on blood gas analysis and histologic analysis of shock organs we characterized and standardized our murine multiple trauma model. Furthermore, we revealed hemorrhagic shock as a causative factor that triggers sIL-6R formation underscoring the fundamental pathophysiologic role of the transsignaling mechanism in multiple trauma. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc.

Interaction Analysis of Longevity Interventions Using Survival Curves.

PubMed

Nowak, Stefan; Neidhart, Johannes; Szendro, Ivan G; Rzezonka, Jonas; Marathe, Rahul; Krug, Joachim

2018-01-06

A long-standing problem in ageing research is to understand how different factors contributing to longevity should be expected to act in combination under the assumption that they are independent. Standard interaction analysis compares the extension of mean lifespan achieved by a combination of interventions to the prediction under an additive or multiplicative null model, but neither model is fundamentally justified. Moreover, the target of longevity interventions is not mean life span but the entire survival curve. Here we formulate a mathematical approach for predicting the survival curve resulting from a combination of two independent interventions based on the survival curves of the individual treatments, and quantify interaction between interventions as the deviation from this prediction. We test the method on a published data set comprising survival curves for all combinations of four different longevity interventions in Caenorhabditis elegans . We find that interactions are generally weak even when the standard analysis indicates otherwise.

Interaction Analysis of Longevity Interventions Using Survival Curves

PubMed Central

Nowak, Stefan; Neidhart, Johannes; Szendro, Ivan G.; Rzezonka, Jonas; Marathe, Rahul; Krug, Joachim

2018-01-01

A long-standing problem in ageing research is to understand how different factors contributing to longevity should be expected to act in combination under the assumption that they are independent. Standard interaction analysis compares the extension of mean lifespan achieved by a combination of interventions to the prediction under an additive or multiplicative null model, but neither model is fundamentally justified. Moreover, the target of longevity interventions is not mean life span but the entire survival curve. Here we formulate a mathematical approach for predicting the survival curve resulting from a combination of two independent interventions based on the survival curves of the individual treatments, and quantify interaction between interventions as the deviation from this prediction. We test the method on a published data set comprising survival curves for all combinations of four different longevity interventions in Caenorhabditis elegans. We find that interactions are generally weak even when the standard analysis indicates otherwise. PMID:29316622

Rethinking plant functional types in Earth System Models: pan-tropical analysis of tree survival across environmental gradients

NASA Astrophysics Data System (ADS)

Johnson, D. J.; Needham, J.; Xu, C.; Davies, S. J.; Bunyavejchewin, S.; Giardina, C. P.; Condit, R.; Cordell, S.; Litton, C. M.; Hubbell, S.; Kassim, A. R. B.; Shawn, L. K. Y.; Nasardin, M. B.; Ong, P.; Ostertag, R.; Sack, L.; Tan, S. K. S.; Yap, S.; McDowell, N. G.; McMahon, S.

2016-12-01

Terrestrial carbon cycling is a function of the growth and survival of trees. Current model representations of tree growth and survival at a global scale rely on coarse plant functional traits that are parameterized very generally. In view of the large biodiversity in the tropical forests, it is important that we account for the functional diversity in order to better predict tropical forest responses to future climate changes. Several next generation Earth System Models are moving towards a size-structured, trait-based approach to modelling vegetation globally, but the challenge of which and how many traits are necessary to capture forest complexity remains. Additionally, the challenge of collecting sufficient trait data to describe the vast species richness of tropical forests is enormous. We propose a more fundamental approach to these problems by characterizing forests by their patterns of survival. We expect our approach to distill real-world tree survival into a reasonable number of functional types. Using 10 large-area tropical forest plots that span geographic, edaphic and climatic gradients, we model tree survival as a function of tree size for hundreds of species. We found surprisingly few categories of size-survival functions emerge. This indicates some fundamental strategies at play across diverse forests to constrain the range of possible size-survival functions. Initial cluster analysis indicates that four to eight functional forms are necessary to describe variation in size-survival relations. Temporal variation in size-survival functions can be related to local environmental variation, allowing us to parameterize how demographically similar groups of species respond to perturbations in the ecosystem. We believe this methodology will yield a synthetic approach to classifying forest systems that will greatly reduce uncertainty and complexity in global vegetation models.

Application of a Non-Mixture Cure Rate Model for Analyzing Survival of Patients with Breast Cancer.

PubMed

Baghestani, Ahmad Reza; Moghaddam, Sahar Saeedi; Majd, Hamid Alavi; Akbari, Mohammad Esmaeil; Nafissi, Nahid; Gohari, Kimiya

2015-01-01

As a result of significant progress made in treatment of many types of cancers during the last few decades, there have been an increased number of patients who do not experience mortality. We refer to these observations as cure or immune and models for survival data which include cure fraction are known as cure rate models or long-term survival models. In this study we used the data collected from 438 female patients with breast cancer registered in the Cancer Research Center in Shahid Beheshti University of Medical Sciences, Tehran, Iran. The patients had been diagnosed from 1992 to 2012 and were followed up until October 2014. We had to exclude some because of incomplete information. Phone calls were made to confirm whether the patients were still alive or not. Deaths due to breast cancer were regarded as failure. To identify clinical, pathological, and biological characteristics of patients that might have had an effect on survival of the patients we used a non-mixture cure rate model; in addition, a Weibull distribution was proposed for the survival time. Analyses were performed using STATA version 14. The significance level was set at P ≤ 0.05. A total of 75 patients (17.1%) died due to breast cancer during the study, up to the last follow-up. Numbers of metastatic lymph nodes and histologic grade were significant factors. The cure fraction was estimated to be 58%. When a cure fraction is not available, the analysis will be changed to standard approaches of survival analysis; however when the data indicate that the cure fraction is available, we suggest analysis of survival data via cure models.

Simulation of parametric model towards the fixed covariate of right censored lung cancer data

NASA Astrophysics Data System (ADS)

Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Ridwan Olaniran, Oyebayo; Enera Amran, Syahila

2017-09-01

In this study, simulation procedure was applied to measure the fixed covariate of right censored data by using parametric survival model. The scale and shape parameter were modified to differentiate the analysis of parametric regression survival model. Statistically, the biases, mean biases and the coverage probability were used in this analysis. Consequently, different sample sizes were employed to distinguish the impact of parametric regression model towards right censored data with 50, 100, 150 and 200 number of sample. R-statistical software was utilised to develop the coding simulation with right censored data. Besides, the final model of right censored simulation was compared with the right censored lung cancer data in Malaysia. It was found that different values of shape and scale parameter with different sample size, help to improve the simulation strategy for right censored data and Weibull regression survival model is suitable fit towards the simulation of survival of lung cancer patients data in Malaysia.

Formulation of the Multi-Hit Model With a Non-Poisson Distribution of Hits

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vassiliev, Oleg N., E-mail: Oleg.Vassiliev@albertahealthservices.ca

2012-07-15

Purpose: We proposed a formulation of the multi-hit single-target model in which the Poisson distribution of hits was replaced by a combination of two distributions: one for the number of particles entering the target and one for the number of hits a particle entering the target produces. Such an approach reflects the fact that radiation damage is a result of two different random processes: particle emission by a radiation source and interaction of particles with matter inside the target. Methods and Materials: Poisson distribution is well justified for the first of the two processes. The second distribution depends on howmore » a hit is defined. To test our approach, we assumed that the second distribution was also a Poisson distribution. The two distributions combined resulted in a non-Poisson distribution. We tested the proposed model by comparing it with previously reported data for DNA single- and double-strand breaks induced by protons and electrons, for survival of a range of cell lines, and variation of the initial slopes of survival curves with radiation quality for heavy-ion beams. Results: Analysis of cell survival equations for this new model showed that they had realistic properties overall, such as the initial and high-dose slopes of survival curves, the shoulder, and relative biological effectiveness (RBE) In most cases tested, a better fit of survival curves was achieved with the new model than with the linear-quadratic model. The results also suggested that the proposed approach may extend the multi-hit model beyond its traditional role in analysis of survival curves to predicting effects of radiation quality and analysis of DNA strand breaks. Conclusions: Our model, although conceptually simple, performed well in all tests. The model was able to consistently fit data for both cell survival and DNA single- and double-strand breaks. It correctly predicted the dependence of radiation effects on parameters of radiation quality.« less

Comparison of Cox’s Regression Model and Parametric Models in Evaluating the Prognostic Factors for Survival after Liver Transplantation in Shiraz during 2000–2012

PubMed Central

Adelian, R.; Jamali, J.; Zare, N.; Ayatollahi, S. M. T.; Pooladfar, G. R.; Roustaei, N.

2015-01-01

Background: Identification of the prognostic factors for survival in patients with liver transplantation is challengeable. Various methods of survival analysis have provided different, sometimes contradictory, results from the same data. Objective: To compare Cox’s regression model with parametric models for determining the independent factors for predicting adults’ and pediatrics’ survival after liver transplantation. Method: This study was conducted on 183 pediatric patients and 346 adults underwent liver transplantation in Namazi Hospital, Shiraz, southern Iran. The study population included all patients undergoing liver transplantation from 2000 to 2012. The prognostic factors sex, age, Child class, initial diagnosis of the liver disease, PELD/MELD score, and pre-operative laboratory markers were selected for survival analysis. Result: Among 529 patients, 346 (64.5%) were adult and 183 (34.6%) were pediatric cases. Overall, the lognormal distribution was the best-fitting model for adult and pediatric patients. Age in adults (HR=1.16, p<0.05) and weight (HR=2.68, p<0.01) and Child class B (HR=2.12, p<0.05) in pediatric patients were the most important factors for prediction of survival after liver transplantation. Adult patients younger than the mean age and pediatric patients weighing above the mean and Child class A (compared to those with classes B or C) had better survival. Conclusion: Parametric regression model is a good alternative for the Cox’s regression model. PMID:26306158

Applications of statistics to medical science, IV survival analysis.

PubMed

Watanabe, Hiroshi

2012-01-01

The fundamental principles of survival analysis are reviewed. In particular, the Kaplan-Meier method and a proportional hazard model are discussed. This work is the last part of a series in which medical statistics are surveyed.

On comparison of net survival curves.

PubMed

Pavlič, Klemen; Perme, Maja Pohar

2017-05-02

Relative survival analysis is a subfield of survival analysis where competing risks data are observed, but the causes of death are unknown. A first step in the analysis of such data is usually the estimation of a net survival curve, possibly followed by regression modelling. Recently, a log-rank type test for comparison of net survival curves has been introduced and the goal of this paper is to explore its properties and put this methodological advance into the context of the field. We build on the association between the log-rank test and the univariate or stratified Cox model and show the analogy in the relative survival setting. We study the properties of the methods using both the theoretical arguments as well as simulations. We provide an R function to enable practical usage of the log-rank type test. Both the log-rank type test and its model alternatives perform satisfactory under the null, even if the correlation between their p-values is rather low, implying that both approaches cannot be used simultaneously. The stratified version has a higher power in case of non-homogeneous hazards, but also carries a different interpretation. The log-rank type test and its stratified version can be interpreted in the same way as the results of an analogous semi-parametric additive regression model despite the fact that no direct theoretical link can be established between the test statistics.

Comparing of Cox model and parametric models in analysis of effective factors on event time of neuropathy in patients with type 2 diabetes.

PubMed

Kargarian-Marvasti, Sadegh; Rimaz, Shahnaz; Abolghasemi, Jamileh; Heydari, Iraj

2017-01-01

Cox proportional hazard model is the most common method for analyzing the effects of several variables on survival time. However, under certain circumstances, parametric models give more precise estimates to analyze survival data than Cox. The purpose of this study was to investigate the comparative performance of Cox and parametric models in a survival analysis of factors affecting the event time of neuropathy in patients with type 2 diabetes. This study included 371 patients with type 2 diabetes without neuropathy who were registered at Fereydunshahr diabetes clinic. Subjects were followed up for the development of neuropathy between 2006 to March 2016. To investigate the factors influencing the event time of neuropathy, significant variables in univariate model ( P < 0.20) were entered into the multivariate Cox and parametric models ( P < 0.05). In addition, Akaike information criterion (AIC) and area under ROC curves were used to evaluate the relative goodness of fitted model and the efficiency of each procedure, respectively. Statistical computing was performed using R software version 3.2.3 (UNIX platforms, Windows and MacOS). Using Kaplan-Meier, survival time of neuropathy was computed 76.6 ± 5 months after initial diagnosis of diabetes. After multivariate analysis of Cox and parametric models, ethnicity, high-density lipoprotein and family history of diabetes were identified as predictors of event time of neuropathy ( P < 0.05). According to AIC, "log-normal" model with the lowest Akaike's was the best-fitted model among Cox and parametric models. According to the results of comparison of survival receiver operating characteristics curves, log-normal model was considered as the most efficient and fitted model.

Quantifying discrimination of Framingham risk functions with different survival C statistics.

PubMed

Pencina, Michael J; D'Agostino, Ralph B; Song, Linye

2012-07-10

Cardiovascular risk prediction functions offer an important diagnostic tool for clinicians and patients themselves. They are usually constructed with the use of parametric or semi-parametric survival regression models. It is essential to be able to evaluate the performance of these models, preferably with summaries that offer natural and intuitive interpretations. The concept of discrimination, popular in the logistic regression context, has been extended to survival analysis. However, the extension is not unique. In this paper, we define discrimination in survival analysis as the model's ability to separate those with longer event-free survival from those with shorter event-free survival within some time horizon of interest. This definition remains consistent with that used in logistic regression, in the sense that it assesses how well the model-based predictions match the observed data. Practical and conceptual examples and numerical simulations are employed to examine four C statistics proposed in the literature to evaluate the performance of survival models. We observe that they differ in the numerical values and aspects of discrimination that they capture. We conclude that the index proposed by Harrell is the most appropriate to capture discrimination described by the above definition. We suggest researchers report which C statistic they are using, provide a rationale for their selection, and be aware that comparing different indices across studies may not be meaningful. Copyright © 2012 John Wiley & Sons, Ltd.

Effects of habitat disturbance on survival rates of softshell turtles (Apalone spinifera) in an urban stream

USGS Publications Warehouse

Plummer, M.V.; Krementz, D.G.; Powell, L.A.; Mills, N.E.

2008-01-01

We monitored Spiny Softshell Turtles (Apalone spinifera) using mark-recapture during 1994-2005 in Gin Creek, Searcy, Arkansas. In 1997-2000 the creek bed and riparian zone were bulldozed in an effort to remove debris and improve water flow. This disturbance appeared to reduce the quantity and quality of turtle habitat. We tested for the potential effect of this habitat disturbance on the survival rates of marked turtles. We estimated annual survival rates for the population using models that allowed for variation in survival by state of maturation, year, and effects of the disturbance; we evaluated two different models of the disturbance impact. The first disturbance model incorporated a single change in survival rates, following the disturbance, whereas the second disturbance model incorporated three survival rates: pre- and postdisturbance, as well as a short-term decline during the disturbance. We used a state-transition model for our mark-recapture analysis, as softshells transition from juveniles to adults in a variable period of time. Our analysis indicated that survival varied by maturation state and was independent of a time trend or the disturbance. Annual survival rates were lower for juveniles (S?? = 0.717, SE = 0.039) than for adults (S?? = 0.836, SE = 0.025). Despite the dramatic habitat disturbance, we found no negative effects on survival rates. Our results demonstrate that, like a few other freshwater turtle species known to thrive in urban environments, populations of A. spinifera are resilient and can persist in urban environments despite periodic habitat disturbances. Copyright 2008 Society for the Study of Amphibians and Reptiles.

THE INFLUENCE OF MODEL TIME STEP ON THE RELATIVE SENSITIVITY OF POPULATION GROWTH TO SURVIVAL, GROWTH AND REPRODUCTION

EPA Science Inventory

Matrix population models are often used to extrapolate from life stage-specific stressor effects on survival and reproduction to population-level effects. Demographic elasticity analysis of a matrix model allows an evaluation of the relative sensitivity of population growth rate ...

Evaluating disease management program effectiveness: an introduction to survival analysis.

PubMed

Linden, Ariel; Adams, John L; Roberts, Nancy

2004-01-01

Currently, the most widely used method in the disease management industry for evaluating program effectiveness is the "total population approach." This model is a pretest-posttest design, with the most basic limitation being that without a control group, there may be sources of bias and/or competing extraneous confounding factors that offer plausible rationale explaining the change from baseline. Survival analysis allows for the inclusion of data from censored cases, those subjects who either "survived" the program without experiencing the event (e.g., achievement of target clinical levels, hospitalization) or left the program prematurely, due to disenrollement from the health plan or program, or were lost to follow-up. Additionally, independent variables may be included in the model to help explain the variability in the outcome measure. In order to maximize the potential of this statistical method, validity of the model and research design must be assured. This paper reviews survival analysis as an alternative, and more appropriate, approach to evaluating DM program effectiveness than the current total population approach.

An artificial neural network improves prediction of observed survival in patients with laryngeal squamous carcinoma.

PubMed

Jones, Andrew S; Taktak, Azzam G F; Helliwell, Timothy R; Fenton, John E; Birchall, Martin A; Husband, David J; Fisher, Anthony C

2006-06-01

The accepted method of modelling and predicting failure/survival, Cox's proportional hazards model, is theoretically inferior to neural network derived models for analysing highly complex systems with large datasets. A blinded comparison of the neural network versus the Cox's model in predicting survival utilising data from 873 treated patients with laryngeal cancer. These were divided randomly and equally into a training set and a study set and Cox's and neural network models applied in turn. Data were then divided into seven sets of binary covariates and the analysis repeated. Overall survival was not significantly different on Kaplan-Meier plot, or with either test model. Although the network produced qualitatively similar results to Cox's model it was significantly more sensitive to differences in survival curves for age and N stage. We propose that neural networks are capable of prediction in systems involving complex interactions between variables and non-linearity.

Parametric Model Based On Imputations Techniques for Partly Interval Censored Data

NASA Astrophysics Data System (ADS)

Zyoud, Abdallah; Elfaki, F. A. M.; Hrairi, Meftah

2017-12-01

The term ‘survival analysis’ has been used in a broad sense to describe collection of statistical procedures for data analysis. In this case, outcome variable of interest is time until an event occurs where the time to failure of a specific experimental unit might be censored which can be right, left, interval, and Partly Interval Censored data (PIC). In this paper, analysis of this model was conducted based on parametric Cox model via PIC data. Moreover, several imputation techniques were used, which are: midpoint, left & right point, random, mean, and median. Maximum likelihood estimate was considered to obtain the estimated survival function. These estimations were then compared with the existing model, such as: Turnbull and Cox model based on clinical trial data (breast cancer data), for which it showed the validity of the proposed model. Result of data set indicated that the parametric of Cox model proved to be more superior in terms of estimation of survival functions, likelihood ratio tests, and their P-values. Moreover, based on imputation techniques; the midpoint, random, mean, and median showed better results with respect to the estimation of survival function.

Lipid emulsion improves survival in animal models of local anesthetic toxicity: a meta-analysis.

PubMed

Fettiplace, Michael R; McCabe, Daniel J

2017-08-01

The Lipid Emulsion Therapy workgroup, organized by the American Academy of Clinical Toxicology, recently conducted a systematic review, which subjectively evaluated lipid emulsion as a treatment for local anesthetic toxicity. We re-extracted data and conducted a meta-analysis of survival in animal models. We extracted survival data from 26 publications and conducted a random-effect meta-analysis based on odds ratio weighted by inverse variance. We assessed the benefit of lipid emulsion as an independent variable in resuscitative models (16 studies). We measured Cochran's Q for heterogeneity and I 2 to determine variance contributed by heterogeneity. Finally, we conducted a funnel plot analysis and Egger's test to assess for publication bias in studies. Lipid emulsion reduced the odds of death in resuscitative models (OR =0.24; 95%CI: 0.1-0.56, p = .0012). Heterogeneity analysis indicated a homogenous distribution. Funnel plot analysis did not indicate publication bias in experimental models. Meta-analysis of animal data supports the use of lipid emulsion (in combination with other resuscitative measures) for the treatment of local anesthetic toxicity, specifically from bupivacaine. Our conclusion differed from the original review. Analysis of outliers reinforced the need for good life support measures (securement of airway and chest compressions) along with prompt treatment with lipid.

Nonparametric Bayesian inference for mean residual life functions in survival analysis.

PubMed

Poynor, Valerie; Kottas, Athanasios

2018-01-19

Modeling and inference for survival analysis problems typically revolves around different functions related to the survival distribution. Here, we focus on the mean residual life (MRL) function, which provides the expected remaining lifetime given that a subject has survived (i.e. is event-free) up to a particular time. This function is of direct interest in reliability, medical, and actuarial fields. In addition to its practical interpretation, the MRL function characterizes the survival distribution. We develop general Bayesian nonparametric inference for MRL functions built from a Dirichlet process mixture model for the associated survival distribution. The resulting model for the MRL function admits a representation as a mixture of the kernel MRL functions with time-dependent mixture weights. This model structure allows for a wide range of shapes for the MRL function. Particular emphasis is placed on the selection of the mixture kernel, taken to be a gamma distribution, to obtain desirable properties for the MRL function arising from the mixture model. The inference method is illustrated with a data set of two experimental groups and a data set involving right censoring. The supplementary material available at Biostatistics online provides further results on empirical performance of the model, using simulated data examples. © The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

An exploratory discrete-time multilevel analysis of the effect of social support on the survival of elderly people in China

PubMed Central

Feng, Zhixin; Jones, Kelvyn; Wang, Wenfei Winnie

2015-01-01

This study undertakes a survival analysis of elderly persons in China using Chinese Longitudinal Healthy Longevity Survey 2002–2008. Employing discrete-time multilevel models, we explored the effect of social support on the survival of elderly people in China. This study focuses on objective (living arrangements and received support) and subjective activities (perceived support) of social support, finding that the effect of different activities of social support on the survival of elderly people varies according to the availability of different support resources. Specifically, living with a spouse, financial independence, perceiving care support from any resource is associated with higher survival rates for elderly people. Separate analysis focusing on urban elderly and rural elderly revealed broadly similar results. There is a larger difference between those perceiving care support from family or social service and not perceiving care support in urban areas comparing to those in rural areas. Those who cannot pay medical expenses are the least likely to survive. The higher level of economic development in province has no significant effect on the survival of elderly people for the whole sample model and the elderly people in urban areas; however, there is a negative influence on the survival of the rural elderly people. PMID:25703671

Using cure models for analyzing the influence of pathogens on salmon survival

USGS Publications Warehouse

Ray, Adam R; Perry, Russell W.; Som, Nicholas A.; Bartholomew, Jerri L

2014-01-01

Parasites and pathogens influence the size and stability of wildlife populations, yet many population models ignore the population-level effects of pathogens. Standard survival analysis methods (e.g., accelerated failure time models) are used to assess how survival rates are influenced by disease. However, they assume that each individual is equally susceptible and will eventually experience the event of interest; this assumption is not typically satisfied with regard to pathogens of wildlife populations. In contrast, mixture cure models, which comprise logistic regression and survival analysis components, allow for different covariates to be entered into each part of the model and provide better predictions of survival when a fraction of the population is expected to survive a disease outbreak. We fitted mixture cure models to the host–pathogen dynamics of Chinook Salmon Oncorhynchus tshawytscha and Coho Salmon O. kisutch and the myxozoan parasite Ceratomyxa shasta. Total parasite concentration, water temperature, and discharge were used as covariates to predict the observed parasite-induced mortality in juvenile salmonids collected as part of a long-term monitoring program in the Klamath River, California. The mixture cure models predicted the observed total mortality well, but some of the variability in observed mortality rates was not captured by the models. Parasite concentration and water temperature were positively associated with total mortality and the mortality rate of both Chinook Salmon and Coho Salmon. Discharge was positively associated with total mortality for both species but only affected the mortality rate for Coho Salmon. The mixture cure models provide insights into how daily survival rates change over time in Chinook Salmon and Coho Salmon after they become infected with C. shasta.

Parametric versus Cox's model: an illustrative analysis of divorce in Canada.

PubMed

Balakrishnan, T R; Rao, K V; Krotki, K J; Lapierre-adamcyk, E

1988-06-01

Recent demographic literature clearly recognizes the importance of survival modes in the analysis of cross-sectional event histories. Of the various survival models, Cox's (1972) partial parametric model has been very popular due to its simplicity, and readily available computer software for estimation, sometimes at the cost of precision and parsimony of the model. This paper focuses on parametric failure time models for event history analysis such as Weibell, lognormal, loglogistic, and exponential models. The authors also test the goodness of fit of these parametric models versus the Cox's proportional hazards model taking Kaplan-Meier estimate as base. As an illustration, the authors reanalyze the Canadian Fertility Survey data on 1st marriage dissolution with parametric models. Though these parametric model estimates were not very different from each other, there seemed to be a slightly better fit with loglogistic. When 8 covariates were used in the analysis, it was found that the coefficients were similar in the models, and the overall conclusions about the relative risks would not have been different. The findings reveal that in marriage dissolution, the differences according to demographic and socioeconomic characteristics may be far more important than is generally found in many studies. Therefore, one should not treat the population as homogeneous in analyzing survival probabilities of marriages, other than for cursory analysis of overall trends.

A general framework for parametric survival analysis.

PubMed

Crowther, Michael J; Lambert, Paul C

2014-12-30

Parametric survival models are being increasingly used as an alternative to the Cox model in biomedical research. Through direct modelling of the baseline hazard function, we can gain greater understanding of the risk profile of patients over time, obtaining absolute measures of risk. Commonly used parametric survival models, such as the Weibull, make restrictive assumptions of the baseline hazard function, such as monotonicity, which is often violated in clinical datasets. In this article, we extend the general framework of parametric survival models proposed by Crowther and Lambert (Journal of Statistical Software 53:12, 2013), to incorporate relative survival, and robust and cluster robust standard errors. We describe the general framework through three applications to clinical datasets, in particular, illustrating the use of restricted cubic splines, modelled on the log hazard scale, to provide a highly flexible survival modelling framework. Through the use of restricted cubic splines, we can derive the cumulative hazard function analytically beyond the boundary knots, resulting in a combined analytic/numerical approach, which substantially improves the estimation process compared with only using numerical integration. User-friendly Stata software is provided, which significantly extends parametric survival models available in standard software. Copyright © 2014 John Wiley & Sons, Ltd.

Markov chains and semi-Markov models in time-to-event analysis.

PubMed

Abner, Erin L; Charnigo, Richard J; Kryscio, Richard J

2013-10-25

A variety of statistical methods are available to investigators for analysis of time-to-event data, often referred to as survival analysis. Kaplan-Meier estimation and Cox proportional hazards regression are commonly employed tools but are not appropriate for all studies, particularly in the presence of competing risks and when multiple or recurrent outcomes are of interest. Markov chain models can accommodate censored data, competing risks (informative censoring), multiple outcomes, recurrent outcomes, frailty, and non-constant survival probabilities. Markov chain models, though often overlooked by investigators in time-to-event analysis, have long been used in clinical studies and have widespread application in other fields.

Markov chains and semi-Markov models in time-to-event analysis

PubMed Central

Abner, Erin L.; Charnigo, Richard J.; Kryscio, Richard J.

2014-01-01

A variety of statistical methods are available to investigators for analysis of time-to-event data, often referred to as survival analysis. Kaplan-Meier estimation and Cox proportional hazards regression are commonly employed tools but are not appropriate for all studies, particularly in the presence of competing risks and when multiple or recurrent outcomes are of interest. Markov chain models can accommodate censored data, competing risks (informative censoring), multiple outcomes, recurrent outcomes, frailty, and non-constant survival probabilities. Markov chain models, though often overlooked by investigators in time-to-event analysis, have long been used in clinical studies and have widespread application in other fields. PMID:24818062

Mechanisms and mediation in survival analysis: towards an integrated analytical framework.

PubMed

Pratschke, Jonathan; Haase, Trutz; Comber, Harry; Sharp, Linda; de Camargo Cancela, Marianna; Johnson, Howard

2016-02-29

A wide-ranging debate has taken place in recent years on mediation analysis and causal modelling, raising profound theoretical, philosophical and methodological questions. The authors build on the results of these discussions to work towards an integrated approach to the analysis of research questions that situate survival outcomes in relation to complex causal pathways with multiple mediators. The background to this contribution is the increasingly urgent need for policy-relevant research on the nature of inequalities in health and healthcare. The authors begin by summarising debates on causal inference, mediated effects and statistical models, showing that these three strands of research have powerful synergies. They review a range of approaches which seek to extend existing survival models to obtain valid estimates of mediation effects. They then argue for an alternative strategy, which involves integrating survival outcomes within Structural Equation Models via the discrete-time survival model. This approach can provide an integrated framework for studying mediation effects in relation to survival outcomes, an issue of great relevance in applied health research. The authors provide an example of how these techniques can be used to explore whether the social class position of patients has a significant indirect effect on the hazard of death from colon cancer. The results suggest that the indirect effects of social class on survival are substantial and negative (-0.23 overall). In addition to the substantial direct effect of this variable (-0.60), its indirect effects account for more than one quarter of the total effect. The two main pathways for this indirect effect, via emergency admission (-0.12), on the one hand, and hospital caseload, on the other, (-0.10) are of similar size. The discrete-time survival model provides an attractive way of integrating time-to-event data within the field of Structural Equation Modelling. The authors demonstrate the efficacy of this approach in identifying complex causal pathways that mediate the effects of a socio-economic baseline covariate on the hazard of death from colon cancer. The results show that this approach has the potential to shed light on a class of research questions which is of particular relevance in health research.

Estimation of the cure rate in Iranian breast cancer patients.

PubMed

Rahimzadeh, Mitra; Baghestani, Ahmad Reza; Gohari, Mahmood Reza; Pourhoseingholi, Mohamad Amin

2014-01-01

Although the Cox's proportional hazard model is the popular approach for survival analysis to investigate significant risk factors of cancer patient survival, it is not appropriate in the case of log-term disease free survival. Recently, cure rate models have been introduced to distinguish between clinical determinants of cure and variables associated with the time to event of interest. The aim of this study was to use a cure rate model to determine the clinical associated factors for cure rates of patients with breast cancer (BC). This prospective cohort study covered 305 patients with BC, admitted at Shahid Faiazbakhsh Hospital, Tehran, during 2006 to 2008 and followed until April 2012. Cases of patient death were confirmed by telephone contact. For data analysis, a non-mixed cure rate model with Poisson distribution and negative binomial distribution were employed. All analyses were carried out using a developed Macro in WinBugs. Deviance information criteria (DIC) were employed to find the best model. The overall 1-year, 3-year and 5-year relative survival rates were 97%, 89% and 74%. Metastasis and stage of BC were the significant factors, but age was significant only in negative binomial model. The DIC also showed that the negative binomial model had a better fit. This study indicated that, metastasis and stage of BC were identified as the clinical criteria for cure rates. There are limited studies on BC survival which employed these cure rate models to identify the clinical factors associated with cure. These models are better than Cox, in the case of long-term survival.

The use of simple reparameterizations to improve the efficiency of Markov chain Monte Carlo estimation for multilevel models with applications to discrete time survival models.

PubMed

Browne, William J; Steele, Fiona; Golalizadeh, Mousa; Green, Martin J

2009-06-01

We consider the application of Markov chain Monte Carlo (MCMC) estimation methods to random-effects models and in particular the family of discrete time survival models. Survival models can be used in many situations in the medical and social sciences and we illustrate their use through two examples that differ in terms of both substantive area and data structure. A multilevel discrete time survival analysis involves expanding the data set so that the model can be cast as a standard multilevel binary response model. For such models it has been shown that MCMC methods have advantages in terms of reducing estimate bias. However, the data expansion results in very large data sets for which MCMC estimation is often slow and can produce chains that exhibit poor mixing. Any way of improving the mixing will result in both speeding up the methods and more confidence in the estimates that are produced. The MCMC methodological literature is full of alternative algorithms designed to improve mixing of chains and we describe three reparameterization techniques that are easy to implement in available software. We consider two examples of multilevel survival analysis: incidence of mastitis in dairy cattle and contraceptive use dynamics in Indonesia. For each application we show where the reparameterization techniques can be used and assess their performance.

Tools for Economic Analysis of Patient Management Interventions in Heart Failure Cost-Effectiveness Model: A Web-based program designed to evaluate the cost-effectiveness of disease management programs in heart failure.

PubMed

Reed, Shelby D; Neilson, Matthew P; Gardner, Matthew; Li, Yanhong; Briggs, Andrew H; Polsky, Daniel E; Graham, Felicia L; Bowers, Margaret T; Paul, Sara C; Granger, Bradi B; Schulman, Kevin A; Whellan, David J; Riegel, Barbara; Levy, Wayne C

2015-11-01

Heart failure disease management programs can influence medical resource use and quality-adjusted survival. Because projecting long-term costs and survival is challenging, a consistent and valid approach to extrapolating short-term outcomes would be valuable. We developed the Tools for Economic Analysis of Patient Management Interventions in Heart Failure Cost-Effectiveness Model, a Web-based simulation tool designed to integrate data on demographic, clinical, and laboratory characteristics; use of evidence-based medications; and costs to generate predicted outcomes. Survival projections are based on a modified Seattle Heart Failure Model. Projections of resource use and quality of life are modeled using relationships with time-varying Seattle Heart Failure Model scores. The model can be used to evaluate parallel-group and single-cohort study designs and hypothetical programs. Simulations consist of 10,000 pairs of virtual cohorts used to generate estimates of resource use, costs, survival, and incremental cost-effectiveness ratios from user inputs. The model demonstrated acceptable internal and external validity in replicating resource use, costs, and survival estimates from 3 clinical trials. Simulations to evaluate the cost-effectiveness of heart failure disease management programs across 3 scenarios demonstrate how the model can be used to design a program in which short-term improvements in functioning and use of evidence-based treatments are sufficient to demonstrate good long-term value to the health care system. The Tools for Economic Analysis of Patient Management Interventions in Heart Failure Cost-Effectiveness Model provides researchers and providers with a tool for conducting long-term cost-effectiveness analyses of disease management programs in heart failure. Copyright © 2015 Elsevier Inc. All rights reserved.

[Study on factors influencing survival in patients with advanced gastric carcinoma after resection by Cox's proportional hazard model].

PubMed

Wang, S; Sun, Z; Wang, S

1996-11-01

A prospective follow-up study of 539 advanced gastric carcinoma patients after resection was undertaken between 1 January 1980 and 31 December 1989, with a follow-up rate of 95.36%. A multivariate analysis of possible factors influencing survival of these patients was performed, and their predicting models of survival rates was established by Cox proportional hazard model. The results showed that the major significant prognostic factors influencing survival of these patients were rate and station of lymph node metastases, type of operation, hepatic metastases, size of tumor, age and location of tumor. The most important factor was the rate of lymph node metastases. According to their regression coefficients, the predicting value (PV) of each patient was calculated, then all patients were divided into five risk groups according to PV, their predicting models of survival rates after resection were established in groups. The goodness-fit of estimated predicting models of survival rates were checked by fitting curve and residual plot, and the estimated models tallied with the actual situation. The results suggest that the patients with advanced gastric cancer after resection without lymph node metastases and hepatic metastases had a better prognosis, and their survival probability may be predicted according to the predicting model of survival rates.

Flexible and structured survival model for a simultaneous estimation of non-linear and non-proportional effects and complex interactions between continuous variables: Performance of this multidimensional penalized spline approach in net survival trend analysis.

PubMed

Remontet, Laurent; Uhry, Zoé; Bossard, Nadine; Iwaz, Jean; Belot, Aurélien; Danieli, Coraline; Charvat, Hadrien; Roche, Laurent

2018-01-01

Cancer survival trend analyses are essential to describe accurately the way medical practices impact patients' survival according to the year of diagnosis. To this end, survival models should be able to account simultaneously for non-linear and non-proportional effects and for complex interactions between continuous variables. However, in the statistical literature, there is no consensus yet on how to build such models that should be flexible but still provide smooth estimates of survival. In this article, we tackle this challenge by smoothing the complex hypersurface (time since diagnosis, age at diagnosis, year of diagnosis, and mortality hazard) using a multidimensional penalized spline built from the tensor product of the marginal bases of time, age, and year. Considering this penalized survival model as a Poisson model, we assess the performance of this approach in estimating the net survival with a comprehensive simulation study that reflects simple and complex realistic survival trends. The bias was generally small and the root mean squared error was good and often similar to that of the true model that generated the data. This parametric approach offers many advantages and interesting prospects (such as forecasting) that make it an attractive and efficient tool for survival trend analyses.

Cost-effectiveness Analysis in R Using a Multi-state Modeling Survival Analysis Framework: A Tutorial.

PubMed

Williams, Claire; Lewsey, James D; Briggs, Andrew H; Mackay, Daniel F

2017-05-01

This tutorial provides a step-by-step guide to performing cost-effectiveness analysis using a multi-state modeling approach. Alongside the tutorial, we provide easy-to-use functions in the statistics package R. We argue that this multi-state modeling approach using a package such as R has advantages over approaches where models are built in a spreadsheet package. In particular, using a syntax-based approach means there is a written record of what was done and the calculations are transparent. Reproducing the analysis is straightforward as the syntax just needs to be run again. The approach can be thought of as an alternative way to build a Markov decision-analytic model, which also has the option to use a state-arrival extended approach. In the state-arrival extended multi-state model, a covariate that represents patients' history is included, allowing the Markov property to be tested. We illustrate the building of multi-state survival models, making predictions from the models and assessing fits. We then proceed to perform a cost-effectiveness analysis, including deterministic and probabilistic sensitivity analyses. Finally, we show how to create 2 common methods of visualizing the results-namely, cost-effectiveness planes and cost-effectiveness acceptability curves. The analysis is implemented entirely within R. It is based on adaptions to functions in the existing R package mstate to accommodate parametric multi-state modeling that facilitates extrapolation of survival curves.

Model for End-Stage Liver Disease, Model for Liver Transplantation Survival and Donor Risk Index as predictive models of survival after liver transplantation in 1,006 patients.

PubMed

Aranzana, Elisa Maria de Camargo; Coppini, Adriana Zuolo; Ribeiro, Maurício Alves; Massarollo, Paulo Celso Bosco; Szutan, Luiz Arnaldo; Ferreira, Fabio Gonçalves

2015-06-01

Liver transplantation has not increased with the number of patients requiring this treatment, increasing deaths among those on the waiting list. Models predicting post-transplantation survival, including the Model for Liver Transplantation Survival and the Donor Risk Index, have been created. Our aim was to compare the performance of the Model for End-Stage Liver Disease, the Model for Liver Transplantation Survival and the Donor Risk Index as prognostic models for survival after liver transplantation. We retrospectively analyzed the data from 1,270 patients who received a liver transplant from a deceased donor in the state of São Paulo, Brazil, between July 2006 and July 2009. All data obtained from the Health Department of the State of São Paulo at the 15 registered transplant centers were analyzed. Patients younger than 13 years of age or with acute liver failure were excluded. The majority of the recipients had Child-Pugh class B or C cirrhosis (63.5%). Among the 1,006 patients included, 274 (27%) died. Univariate survival analysis using a Cox proportional hazards model showed hazard ratios of 1.02 and 1.43 for the Model for End-Stage Liver Disease and the Model for Liver Transplantation Survival, respectively (p<0.001). The areas under the ROC curve for the Donor Risk Index were always less than 0.5, whereas those for the Model for End-Stage Liver Disease and the Model for Liver Transplantation Survival were significantly greater than 0.5 (p<0.001). The cutoff values for the Model for End-Stage Liver Disease (≥29.5; sensitivity: 39.1%; specificity: 75.4%) and the Model for Liver Transplantation Survival (≥1.9; sensitivity 63.9%, specificity 54.5%), which were calculated using data available before liver transplantation, were good predictors of survival after liver transplantation (p<0.001). The Model for Liver Transplantation Survival displayed similar death prediction performance to that of the Model for End-Stage Liver Disease. A simpler model involving fewer variables, such as the Model for End-Stage Liver Disease, is preferred over a complex model involving more variables, such as the Model for Liver Transplantation Survival. The Donor Risk Index had no significance in post-transplantation survival in our patients.

Modeling the survival kinetics of Salmonella in tree nuts for use in risk assessment.

PubMed

Santillana Farakos, Sofia M; Pouillot, Régis; Anderson, Nathan; Johnson, Rhoma; Son, Insook; Van Doren, Jane

2016-06-16

Salmonella has been shown to survive in tree nuts over long periods of time. This survival capacity and its variability are key elements for risk assessment of Salmonella in tree nuts. The aim of this study was to develop a mathematical model to predict survival of Salmonella in tree nuts at ambient storage temperatures that considers variability and uncertainty separately and can easily be incorporated into a risk assessment model. Data on Salmonella survival on raw almonds, pecans, pistachios and walnuts were collected from the peer reviewed literature. The Weibull model was chosen as the baseline model and various fixed effect and mixed effect models were fit to the data. The best model identified through statistical analysis testing was then used to develop a hierarchical Bayesian model. Salmonella in tree nuts showed slow declines at temperatures ranging from 21°C to 24°C. A high degree of variability in survival was observed across tree nut studies reported in the literature. Statistical analysis results indicated that the best applicable model was a mixed effect model that included a fixed and random variation of δ per tree nut (which is the time it takes for the first log10 reduction) and a fixed variation of ρ per tree nut (parameter which defines the shape of the curve). Higher estimated survival rates (δ) were obtained for Salmonella on pistachios, followed in decreasing order by pecans, almonds and walnuts. The posterior distributions obtained from Bayesian inference were used to estimate the variability in the log10 decrease levels in survival for each tree nut, and the uncertainty of these estimates. These modeled uncertainty and variability distributions of the estimates can be used to obtain a complete exposure assessment of Salmonella in tree nuts when including time-temperature parameters for storage and consumption data. The statistical approach presented in this study may be applied to any studies that aim to develop predictive models to be implemented in a probabilistic exposure assessment or a quantitative microbial risk assessment. Published by Elsevier B.V.

A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications

PubMed Central

Austin, Peter C.

2017-01-01

Summary Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster-specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster-specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow-up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log–log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within-cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata). PMID:29307954

A Tutorial on Multilevel Survival Analysis: Methods, Models and Applications.

PubMed

Austin, Peter C

2017-08-01

Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster-specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow-up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster-specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow-up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log-log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within-cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata).

Association between obesity with disease-free survival and overall survival in triple-negative breast cancer: A meta-analysis.

PubMed

Mei, Lin; He, Lin; Song, Yuhua; Lv, Yang; Zhang, Lijiu; Hao, Fengxi; Xu, Mengmeng

2018-05-01

To investigate the relationship between obesity and disease-free survival (DFS) and overall survival (OS) of triple-negative breast cancer. Citations were searched in PubMed, Cochrane Library, and Web of Science. Random effect model meta-analysis was conducted by using Revman software version 5.0, and publication bias was evaluated by creating Egger regression with STATA software version 12. Nine studies (4412 patients) were included for DFS meta-analysis, 8 studies (4392 patients) include for OS meta-analysis. There were no statistical significances between obesity with DFS (P = .60) and OS (P = .71) in triple-negative breast cancer (TNBC) patients. Obesity has no impact on DFS and OS in patients with TNBC.

Using Survival Analysis to Improve Estimates of Life Year Gains in Policy Evaluations.

PubMed

Meacock, Rachel; Sutton, Matt; Kristensen, Søren Rud; Harrison, Mark

2017-05-01

Policy evaluations taking a lifetime horizon have converted estimated changes in short-term mortality to expected life year gains using general population life expectancy. However, the life expectancy of the affected patients may differ from the general population. In trials, survival models are commonly used to extrapolate life year gains. The objective was to demonstrate the feasibility and materiality of using parametric survival models to extrapolate future survival in health care policy evaluations. We used our previous cost-effectiveness analysis of a pay-for-performance program as a motivating example. We first used the cohort of patients admitted prior to the program to compare 3 methods for estimating remaining life expectancy. We then used a difference-in-differences framework to estimate the life year gains associated with the program using general population life expectancy and survival models. Patient-level data from Hospital Episode Statistics was utilized for patients admitted to hospitals in England for pneumonia between 1 April 2007 and 31 March 2008 and between 1 April 2009 and 31 March 2010, and linked to death records for the period from 1 April 2007 to 31 March 2011. In our cohort of patients, using parametric survival models rather than general population life expectancy figures reduced the estimated mean life years remaining by 30% (9.19 v. 13.15 years, respectively). However, the estimated mean life year gains associated with the program are larger using survival models (0.380 years) compared to using general population life expectancy (0.154 years). Using general population life expectancy to estimate the impact of health care policies can overestimate life expectancy but underestimate the impact of policies on life year gains. Using a longer follow-up period improved the accuracy of estimated survival and program impact considerably.

Survival from colorectal cancer in Victoria: 10-year follow up of the 1987 management survey.

PubMed

McLeish, John A; Thursfield, Vicky J; Giles, Graham G

2002-05-01

In 1987, the Victorian Cancer Registry identified a population-based sample of patients who underwent surgery for colorectal cancer for an audit of management following resection. Over 10 years have passed since this survey, and data on the survival of these patients (incorporating various prognostic indicators collected at the time of the survey) are now discussed in the present report. Relative survival analysis was conducted for each prognostic indicator separately and then combined in a multivariate model. Relative survival at 5 years for patients undergoing curative resections was 76% compared with 7% for those whose treatment was considered palliative. Survival at 10 years was little changed (73% and 7% respectively). Survival did not differ significantly by sex or age irrespective of treatment intention. In the curative group, only stage was a significant predictor of survival. Multivariate analysis was performed only for the curative group. Adjusting for all variables simultaneously,stage was the only -significant predictor of survival. Patients with Dukes' stage C disease were at a significantly greater risk (OR 5.5 (1.7-17.6)) than those with Dukes' A. Neither tumour site, sex, age, surgeon activity level nor adjuvant therapies made a significant contribution to the model.

Modeling the airborne survival of influenza virus in a residential setting: the impacts of home humidification.

PubMed

Myatt, Theodore A; Kaufman, Matthew H; Allen, Joseph G; MacIntosh, David L; Fabian, M Patricia; McDevitt, James J

2010-09-03

Laboratory research studies indicate that aerosolized influenza viruses survive for longer periods at low relative humidity (RH) conditions. Further analysis has shown that absolute humidity (AH) may be an improved predictor of virus survival in the environment. Maintaining airborne moisture levels that reduce survival of the virus in the air and on surfaces could be another tool for managing public health risks of influenza. A multi-zone indoor air quality model was used to evaluate the ability of portable humidifiers to control moisture content of the air and the potential related benefit of decreasing survival of influenza viruses in single-family residences. We modeled indoor AH and influenza virus concentrations during winter months (Northeast US) using the CONTAM multi-zone indoor air quality model. A two-story residential template was used under two different ventilation conditions - forced hot air and radiant heating. Humidity was evaluated on a room-specific and whole house basis. Estimates of emission rates for influenza virus were particle-size specific and derived from published studies and included emissions during both tidal breathing and coughing events. The survival of the influenza virus was determined based on the established relationship between AH and virus survival. The presence of a portable humidifier with an output of 0.16 kg water per hour in the bedroom resulted in an increase in median sleeping hours AH/RH levels of 11 to 19% compared to periods without a humidifier present. The associated percent decrease in influenza virus survival was 17.5 - 31.6%. Distribution of water vapor through a residence was estimated to yield 3 to 12% increases in AH/RH and 7.8-13.9% reductions in influenza virus survival. This modeling analysis demonstrates the potential benefit of portable residential humidifiers in reducing the survival of aerosolized influenza virus by controlling humidity indoors.

Modeling the airborne survival of influenza virus in a residential setting: the impacts of home humidification

PubMed Central

2010-01-01

Background Laboratory research studies indicate that aerosolized influenza viruses survive for longer periods at low relative humidity (RH) conditions. Further analysis has shown that absolute humidity (AH) may be an improved predictor of virus survival in the environment. Maintaining airborne moisture levels that reduce survival of the virus in the air and on surfaces could be another tool for managing public health risks of influenza. Methods A multi-zone indoor air quality model was used to evaluate the ability of portable humidifiers to control moisture content of the air and the potential related benefit of decreasing survival of influenza viruses in single-family residences. We modeled indoor AH and influenza virus concentrations during winter months (Northeast US) using the CONTAM multi-zone indoor air quality model. A two-story residential template was used under two different ventilation conditions - forced hot air and radiant heating. Humidity was evaluated on a room-specific and whole house basis. Estimates of emission rates for influenza virus were particle-size specific and derived from published studies and included emissions during both tidal breathing and coughing events. The survival of the influenza virus was determined based on the established relationship between AH and virus survival. Results The presence of a portable humidifier with an output of 0.16 kg water per hour in the bedroom resulted in an increase in median sleeping hours AH/RH levels of 11 to 19% compared to periods without a humidifier present. The associated percent decrease in influenza virus survival was 17.5 - 31.6%. Distribution of water vapor through a residence was estimated to yield 3 to 12% increases in AH/RH and 7.8-13.9% reductions in influenza virus survival. Conclusion This modeling analysis demonstrates the potential benefit of portable residential humidifiers in reducing the survival of aerosolized influenza virus by controlling humidity indoors. PMID:20815876

Association of Adjuvant Chemotherapy With Survival in Patients With Stage II or III Gastric Cancer

PubMed Central

Jiang, Yuming; Li, Tuanjie; Liang, Xiaoling; Hu, Yanfeng; Huang, Lei; Liao, Zhenchen; Zhao, Liying; Han, Zhen; Zhu, Shuguang; Wang, Menglan; Xu, Yangwei; Qi, Xiaolong; Liu, Hao; Yang, Yang; Yu, Jiang; Liu, Wei; Cai, Shirong

2017-01-01

Importance The current staging system of gastric cancer is not adequate for defining a prognosis and predicting the patients most likely to benefit from chemotherapy. Objective To construct a survival prediction model based on specific tumor and patient characteristics that enables individualized predictions of the net survival benefit of adjuvant chemotherapy for patients with stage II or stage III gastric cancer. Design, Setting, and Participants In this multicenter retrospective analysis, a survival prediction model was constructed using data from a training cohort of 746 patients with stage II or stage III gastric cancer who satisfied the study’s inclusion criteria and underwent surgery between January 1, 2004, and December 31, 2012, at Nanfang Hospital in Guangzhou, China. Patient and tumor characteristics were included as covariates, and their association with overall survival and disease-free survival with and without adjuvant chemotherapy was assessed. The model was internally validated for discrimination and calibration using bootstrap resampling. To externally validate the model, data were included from a validation cohort of 973 patients with stage II or stage III gastric cancer who met the inclusion criteria and underwent surgery at First Affiliated Hospital in Guangzhou, China, and at West China Hospital of Sichuan Hospital in Chendu, China, between January 1, 2000, and June 30, 2009. Data were analyzed from July 10, 2016, to September 1, 2016. Main Outcomes and Measures Concordance index and decision curve analysis for each measure associated with postoperative overall survival and disease-free survival. Results Of the 1719 patients analyzed, 1183 (68.8%) were men and 536 (31.2%) were women and the median (interquartile range) age was 57 (49-66) years. Age, location, differentiation, carcinoembryonic antigen, cancer antigen 19-9, depth of invasion, lymph node metastasis, and adjuvant chemotherapy were significantly associated with overall survival and disease-free survival, with P < .05. The survival prediction model demonstrated good calibration and discrimination, with relatively high bootstrap-corrected concordance indexes in the training and validation cohorts. In the validation cohort, the concordance index for overall survival was 0.693 (95% CI, 0.671-0.715) and for disease-free survival was 0.704 (95% CI, 0.681-0.728). Two nomograms and a calculating tool were built on the basis of specific input variables to estimate an individual’s net survival gain attributable to adjuvant chemotherapy. Conclusions and Relevance The survival prediction model can be used to make individualized predictions of the expected survival benefit from the addition of adjuvant chemotherapy for patients with stage II or stage III gastric cancer. PMID:28538950

[The survival prediction model of advanced gallbladder cancer based on Bayesian network: a multi-institutional study].

PubMed

Tang, Z H; Geng, Z M; Chen, C; Si, S B; Cai, Z Q; Song, T Q; Gong, P; Jiang, L; Qiu, Y H; He, Y; Zhai, W L; Li, S P; Zhang, Y C; Yang, Y

2018-05-01

Objective: To investigate the clinical value of Bayesian network in predicting survival of patients with advanced gallbladder cancer(GBC)who underwent curative intent surgery. Methods: The clinical data of patients with advanced GBC who underwent curative intent surgery in 9 institutions from January 2010 to December 2015 were analyzed retrospectively.A median survival time model based on a tree augmented naïve Bayes algorithm was established by Bayesia Lab software.The survival time, number of metastatic lymph nodes(NMLN), T stage, pathological grade, margin, jaundice, liver invasion, age, sex and tumor morphology were included in this model.Confusion matrix, the receiver operating characteristic curve and area under the curve were used to evaluate the accuracy of the model.A priori statistical analysis of these 10 variables and a posterior analysis(survival time as the target variable, the remaining factors as the attribute variables)was performed.The importance rankings of each variable was calculated with the polymorphic Birnbaum importance calculation based on the posterior analysis results.The survival probability forecast table was constructed based on the top 4 prognosis factors. The survival curve was drawn by the Kaplan-Meier method, and differences in survival curves were compared using the Log-rank test. Results: A total of 316 patients were enrolled, including 109 males and 207 females.The ratio of male to female was 1.0∶1.9, the age was (62.0±10.8)years.There was 298 cases(94.3%) R0 resection and 18 cases(5.7%) R1 resection.T staging: 287 cases(90.8%) T3 and 29 cases(9.2%) T4.The median survival time(MST) was 23.77 months, and the 1, 3, 5-year survival rates were 67.4%, 40.8%, 32.0%, respectively.For the Bayesian model, the number of correctly predicted cases was 121(≤23.77 months) and 115(>23.77 months) respectively, leading to a 74.86% accuracy of this model.The prior probability of survival time was 0.503 2(≤23.77 months) and 0.496 8(>23.77 months), the importance ranking showed that NMLN(0.366 6), margin(0.350 1), T stage(0.319 2) and pathological grade(0.258 9) were the top 4 prognosis factors influencing the postoperative MST.These four factors were taken as observation variables to get the probability of patients in different survival periods.Basing on these results, a survival prediction score system including NMLN, margin, T stage and pathological grade was designed, the median survival time(month) of 4-9 points were 66.8, 42.4, 26.0, 9.0, 7.5 and 2.3, respectively, there was a statistically significant difference in the different points( P <0.01). Conclusions: The survival prediction model of GBC based on Bayesian network has high accuracy.NMLN, margin, T staging and pathological grade are the top 4 risk factors affecting the survival of patients with advanced GBC who underwent curative resection.The survival prediction score system based on these four factors could be used to predict the survival and to guide the decision making of patients with advanced GBC.

Adjuvant radiation therapy and lymphadenectomy in esophageal cancer: a SEER database analysis.

PubMed

Shridhar, Ravi; Weber, Jill; Hoffe, Sarah E; Almhanna, Khaldoun; Karl, Richard; Meredith, Kenneth

2013-08-01

This study seeks to determine the effects of postoperative radiation therapy and lymphadenectomy on survival in esophageal cancer. An analysis of patients with surgically resected esophageal cancer from the SEER database between 2004 and 2008 was performed to determine association of adjuvant radiation and lymph node dissection on survival. Survival curves were calculated according to the Kaplan-Meier method and log-rank analysis. Multivariate analysis (MVA) was performed by the Cox proportional hazard model. We identified 2109 patients who met inclusion criteria. Radiation was associated with increased survival in stage III patients (p = 0.005), no benefit in stage II (p = 0.075) and IV (p = 0.913) patients, and decreased survival in stage I patients (p < 0.0001). Univariate analysis revealed that radiation therapy was associated with a survival benefit node positive (N1) patients while it was associated with a detriment in survival for node negative (N0) patients. Removing >12 and >15 lymph nodes was associated with increased survival in N0 patients, while removing >8, >10, >12, >15, and >20 lymph nodes was associated with a survival benefit in N1 patients. MVA revealed that age, gender, tumor and nodal stage, tumor location, and number of lymph nodes removed were prognostic for survival in N0 patients. In N1 patients, MVA showed the age, tumor stage, number of lymph nodes removed, and radiation were prognostic for survival. The number of lymph nodes removed in esophageal cancer is associated with increased survival. The benefit of adjuvant radiation therapy on survival in esophageal cancer is limited to N1 patients.

Support vector methods for survival analysis: a comparison between ranking and regression approaches.

PubMed

Van Belle, Vanya; Pelckmans, Kristiaan; Van Huffel, Sabine; Suykens, Johan A K

2011-10-01

To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data. The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a problem which can be solved efficiently in a context of structural risk minimization and convex optimization techniques. In a second approach, one uses a regression approach, dealing with censoring by means of inequality constraints. The goal of this paper is then twofold: (i) introducing a new model combining the ranking and regression strategy, which retains the link with existing survival models such as the proportional hazards model via transformation models; and (ii) comparison of the three techniques on 6 clinical and 3 high-dimensional datasets and discussing the relevance of these techniques over classical approaches fur survival data. We compare svm-based survival models based on ranking constraints, based on regression constraints and models based on both ranking and regression constraints. The performance of the models is compared by means of three different measures: (i) the concordance index, measuring the model's discriminating ability; (ii) the logrank test statistic, indicating whether patients with a prognostic index lower than the median prognostic index have a significant different survival than patients with a prognostic index higher than the median; and (iii) the hazard ratio after normalization to restrict the prognostic index between 0 and 1. Our results indicate a significantly better performance for models including regression constraints above models only based on ranking constraints. This work gives empirical evidence that svm-based models using regression constraints perform significantly better than svm-based models based on ranking constraints. Our experiments show a comparable performance for methods including only regression or both regression and ranking constraints on clinical data. On high dimensional data, the former model performs better. However, this approach does not have a theoretical link with standard statistical models for survival data. This link can be made by means of transformation models when ranking constraints are included. Copyright © 2011 Elsevier B.V. All rights reserved.

Analysis of survival data from telemetry projects

USGS Publications Warehouse

Bunck, C.M.; Winterstein, S.R.; Pollock, K.H.

1985-01-01

Telemetry techniques can be used to study the survival rates of animal populations and are particularly suitable for species or settings for which band recovery models are not. Statistical methods for estimating survival rates and parameters of survival distributions from observations of radio-tagged animals will be described. These methods have been applied to medical and engineering studies and to the study of nest success. Estimates and tests based on discrete models, originally introduced by Mayfield, and on continuous models, both parametric and nonparametric, will be described. Generalizations, including staggered entry of subjects into the study and identification of mortality factors will be considered. Additional discussion topics will include sample size considerations, relocation frequency for subjects, and use of covariates.

Application of random survival forests in understanding the determinants of under-five child mortality in Uganda in the presence of covariates that satisfy the proportional and non-proportional hazards assumption.

PubMed

Nasejje, Justine B; Mwambi, Henry

2017-09-07

Uganda just like any other Sub-Saharan African country, has a high under-five child mortality rate. To inform policy on intervention strategies, sound statistical methods are required to critically identify factors strongly associated with under-five child mortality rates. The Cox proportional hazards model has been a common choice in analysing data to understand factors strongly associated with high child mortality rates taking age as the time-to-event variable. However, due to its restrictive proportional hazards (PH) assumption, some covariates of interest which do not satisfy the assumption are often excluded in the analysis to avoid mis-specifying the model. Otherwise using covariates that clearly violate the assumption would mean invalid results. Survival trees and random survival forests are increasingly becoming popular in analysing survival data particularly in the case of large survey data and could be attractive alternatives to models with the restrictive PH assumption. In this article, we adopt random survival forests which have never been used in understanding factors affecting under-five child mortality rates in Uganda using Demographic and Health Survey data. Thus the first part of the analysis is based on the use of the classical Cox PH model and the second part of the analysis is based on the use of random survival forests in the presence of covariates that do not necessarily satisfy the PH assumption. Random survival forests and the Cox proportional hazards model agree that the sex of the household head, sex of the child, number of births in the past 1 year are strongly associated to under-five child mortality in Uganda given all the three covariates satisfy the PH assumption. Random survival forests further demonstrated that covariates that were originally excluded from the earlier analysis due to violation of the PH assumption were important in explaining under-five child mortality rates. These covariates include the number of children under the age of five in a household, number of births in the past 5 years, wealth index, total number of children ever born and the child's birth order. The results further indicated that the predictive performance for random survival forests built using covariates including those that violate the PH assumption was higher than that for random survival forests built using only covariates that satisfy the PH assumption. Random survival forests are appealing methods in analysing public health data to understand factors strongly associated with under-five child mortality rates especially in the presence of covariates that violate the proportional hazards assumption.

Prognostic and survival analysis of 837 Chinese colorectal cancer patients.

PubMed

Yuan, Ying; Li, Mo-Dan; Hu, Han-Guang; Dong, Cai-Xia; Chen, Jia-Qi; Li, Xiao-Fen; Li, Jing-Jing; Shen, Hong

2013-05-07

To develop a prognostic model to predict survival of patients with colorectal cancer (CRC). Survival data of 837 CRC patients undergoing surgery between 1996 and 2006 were collected and analyzed by univariate analysis and Cox proportional hazard regression model to reveal the prognostic factors for CRC. All data were recorded using a standard data form and analyzed using SPSS version 18.0 (SPSS, Chicago, IL, United States). Survival curves were calculated by the Kaplan-Meier method. The log rank test was used to assess differences in survival. Univariate hazard ratios and significant and independent predictors of disease-specific survival and were identified by Cox proportional hazard analysis. The stepwise procedure was set to a threshold of 0.05. Statistical significance was defined as P < 0.05. The survival rate was 74% at 3 years and 68% at 5 years. The results of univariate analysis suggested age, preoperative obstruction, serum carcinoembryonic antigen level at diagnosis, status of resection, tumor size, histological grade, pathological type, lymphovascular invasion, invasion of adjacent organs, and tumor node metastasis (TNM) staging were positive prognostic factors (P < 0.05). Lymph node ratio (LNR) was also a strong prognostic factor in stage III CRC (P < 0.0001). We divided 341 stage III patients into three groups according to LNR values (LNR1, LNR ≤ 0.33, n = 211; LNR2, LNR 0.34-0.66, n = 76; and LNR3, LNR ≥ 0.67, n = 54). Univariate analysis showed a significant statistical difference in 3-year survival among these groups: LNR1, 73%; LNR2, 55%; and LNR3, 42% (P < 0.0001). The multivariate analysis results showed that histological grade, depth of bowel wall invasion, and number of metastatic lymph nodes were the most important prognostic factors for CRC if we did not consider the interaction of the TNM staging system (P < 0.05). When the TNM staging was taken into account, histological grade lost its statistical significance, while the specific TNM staging system showed a statistically significant difference (P < 0.0001). The overall survival of CRC patients has improved between 1996 and 2006. LNR is a powerful factor for estimating the survival of stage III CRC patients.

Indiana Emergent Bilingual Student Time to Reclassification: A Survival Analysis

ERIC Educational Resources Information Center

Burke, April M.; Morita-Mullaney, Trish; Singh, Malkeet

2016-01-01

In this study, we employed a discrete-time survival analysis model to examine Indiana emergent bilingual time to reclassification as fluent English proficient. The data consisted of five years of statewide English language proficiency scores. Indiana has a large and rapidly growing Spanish-speaking emergent bilingual population, and these students…

Advantage of the modified Lunn-McNeil technique over Kalbfleisch-Prentice technique in competing risks

NASA Astrophysics Data System (ADS)

Lukman, Iing; Ibrahim, Noor A.; Daud, Isa B.; Maarof, Fauziah; Hassan, Mohd N.

2002-03-01

Survival analysis algorithm is often applied in the data mining process. Cox regression is one of the survival analysis tools that has been used in many areas, and it can be used to analyze the failure times of aircraft crashed. Another survival analysis tool is the competing risks where we have more than one cause of failure acting simultaneously. Lunn-McNeil analyzed the competing risks in the survival model using Cox regression with censored data. The modified Lunn-McNeil technique is a simplify of the Lunn-McNeil technique. The Kalbfleisch-Prentice technique is involving fitting models separately from each type of failure, treating other failure types as censored. To compare the two techniques, (the modified Lunn-McNeil and Kalbfleisch-Prentice) a simulation study was performed. Samples with various sizes and censoring percentages were generated and fitted using both techniques. The study was conducted by comparing the inference of models, using Root Mean Square Error (RMSE), the power tests, and the Schoenfeld residual analysis. The power tests in this study were likelihood ratio test, Rao-score test, and Wald statistics. The Schoenfeld residual analysis was conducted to check the proportionality of the model through its covariates. The estimated parameters were computed for the cause-specific hazard situation. Results showed that the modified Lunn-McNeil technique was better than the Kalbfleisch-Prentice technique based on the RMSE measurement and Schoenfeld residual analysis. However, the Kalbfleisch-Prentice technique was better than the modified Lunn-McNeil technique based on power tests measurement.

A multi-year analysis of spillway survival for juvenile salmonids as a function of spill bay operations at McNary Dam, Washington and Oregon, 2004-09

USGS Publications Warehouse

Adams, Noah S.; Hansel, Hal C.; Perry, Russell W.; Evans, Scott D.

2012-01-01

We analyzed 6 years (2004-09) of passage and survival data collected at McNary Dam to examine how spill bay operations affect survival of juvenile salmonids passing through the spillway at McNary Dam. We also examined the relations between spill bay operations and survival through the juvenile fish bypass in an attempt to determine if survival through the bypass is influenced by spill bay operations. We used a Cormack-Jolly-Seber release-recapture model (CJS model) to determine how the survival of juvenile salmonids passing through McNary Dam relates to spill bay operations. Results of these analyses, while not designed to yield predictive models, can be used to help develop dam-operation strategies that optimize juvenile salmonid survival. For example, increasing total discharge typically had a positive effect on both spillway and bypass survival for all species except sockeye salmon (Oncorhynchus nerka). Likewise, an increase in spill bay discharge improved spillway survival for yearling Chinook salmon (Oncorhynchus tshawytscha), and an increase in spillway discharge positively affected spillway survival for juvenile steelhead (Oncorhynchus mykiss). The strong linear relation between increased spill and increased survival indicates that increasing the amount of water through the spillway is one strategy that could be used to improve spillway survival for yearling Chinook salmon and juvenile steelhead. However, increased spill did not improve spillway survival for subyearling Chinook salmon and sockeye salmon. Our results indicate that a uniform spill pattern would provide the highest spillway survival and bypass survival for subyearling Chinook salmon. Conversely, a predominantly south spill pattern provided the highest spillway survival for yearling Chinook salmon and juvenile steelhead. Although spill pattern was not a factor for spillway survival of sockeye salmon, spill bay operations that optimize passage through the north and south spill bays maximized spillway survival for this species. Bypass survival of yearling Chinook salmon could be improved by optimizing conditions to facilitate bypass passage at night, but the method to do so is not apparent from this analysis because photoperiod was the only factor affecting bypass survival based on the best and only supported model. Bypass survival of juvenile steelhead would benefit from lower water temperatures and increased total and spillway discharge. Likewise, subyearling Chinook salmon bypass survival would improve with lower water temperatures, increased total discharge, and a uniform spill pattern.

Demisability and survivability sensitivity to design-for-demise techniques

NASA Astrophysics Data System (ADS)

Trisolini, Mirko; Lewis, Hugh G.; Colombo, Camilla

2018-04-01

The paper is concerned with examining the effects that design-for-demise solutions can have not only on the demisability of components, but also on their survivability that is their capability to withstand impacts from space debris. First two models are introduced. A demisability model to predict the behaviour of spacecraft components during the atmospheric re-entry and a survivability model to assess the vulnerability of spacecraft structures against space debris impacts. Two indices that evaluate the level of demisability and survivability are also proposed. The two models are then used to study the sensitivity of the demisability and of the survivability indices as a function of typical design-for-demise options. The demisability and the survivability can in fact be influenced by the same design parameters in a competing fashion that is while the demisability is improved, the survivability is worsened and vice versa. The analysis shows how the design-for-demise solutions influence the demisability and the survivability independently. In addition, the effect that a solution has simultaneously on the two criteria is assessed. Results shows which, among the design-for-demise parameters mostly influence the demisability and the survivability. For such design parameters maps are presented, describing their influence on the demisability and survivability indices. These maps represent a useful tool to quickly assess the level of demisability and survivability that can be expected from a component, when specific design parameters are changed.

Pretreatment ADC histogram analysis is a predictive imaging biomarker for bevacizumab treatment but not chemotherapy in recurrent glioblastoma.

PubMed

Ellingson, B M; Sahebjam, S; Kim, H J; Pope, W B; Harris, R J; Woodworth, D C; Lai, A; Nghiemphu, P L; Mason, W P; Cloughesy, T F

2014-04-01

Pre-treatment ADC characteristics have been shown to predict response to bevacizumab in recurrent glioblastoma multiforme. However, no studies have examined whether ADC characteristics are specific to this particular treatment. The purpose of the current study was to determine whether ADC histogram analysis is a bevacizumab-specific or treatment-independent biomarker of treatment response in recurrent glioblastoma multiforme. Eighty-nine bevacizumab-treated and 43 chemotherapy-treated recurrent glioblastoma multiformes never exposed to bevacizumab were included in this study. In all patients, ADC values in contrast-enhancing ROIs from MR imaging examinations performed at the time of recurrence, immediately before commencement of treatment for recurrence, were extracted and the resulting histogram was fitted to a mixed model with a double Gaussian distribution. Mean ADC in the lower Gaussian curve was used as the primary biomarker of interest. The Cox proportional hazards model and log-rank tests were used for survival analysis. Cox multivariate regression analysis accounting for the interaction between bevacizumab- and non-bevacizumab-treated patients suggested that the ability of the lower Gaussian curve to predict survival is dependent on treatment (progression-free survival, P = .045; overall survival, P = .003). Patients with bevacizumab-treated recurrent glioblastoma multiforme with a pretreatment lower Gaussian curve > 1.2 μm(2)/ms had a significantly longer progression-free survival and overall survival compared with bevacizumab-treated patients with a lower Gaussian curve < 1.2 μm(2)/ms. No differences in progression-free survival or overall survival were observed in the chemotherapy-treated cohort. Bevacizumab-treated patients with a mean lower Gaussian curve > 1.2 μm(2)/ms had a significantly longer progression-free survival and overall survival compared with chemotherapy-treated patients. The mean lower Gaussian curve from ADC histogram analysis is a predictive imaging biomarker for bevacizumab-treated, not chemotherapy-treated, recurrent glioblastoma multiforme. Patients with recurrent glioblastoma multiforme with a mean lower Gaussian curve > 1.2 μm(2)/ms have a survival advantage when treated with bevacizumab.

Interdependent Multi-Layer Networks: Modeling and Survivability Analysis with Applications to Space-Based Networks

PubMed Central

Castet, Jean-Francois; Saleh, Joseph H.

2013-01-01

This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the results highlight the importance of the reliability of the wireless links between spacecraft (nodes) to enable any survivability improvements for space-based networks. PMID:23599835

Interdependent multi-layer networks: modeling and survivability analysis with applications to space-based networks.

PubMed

Castet, Jean-Francois; Saleh, Joseph H

2013-01-01

This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the results highlight the importance of the reliability of the wireless links between spacecraft (nodes) to enable any survivability improvements for space-based networks.

Model description and evaluation of the mark-recapture survival model used to parameterize the 2012 status and threats analysis for the Florida manatee (Trichechus manatus latirostris)

USGS Publications Warehouse

Langtimm, Catherine A.; Kendall, William L.; Beck, Cathy A.; Kochman, Howard I.; Teague, Amy L.; Meigs-Friend, Gaia; Peñaloza, Claudia L.

2016-11-30

This report provides supporting details and evidence for the rationale, validity and efficacy of a new mark-recapture model, the Barker Robust Design, to estimate regional manatee survival rates used to parameterize several components of the 2012 version of the Manatee Core Biological Model (CBM) and Threats Analysis (TA).  The CBM and TA provide scientific analyses on population viability of the Florida manatee subspecies (Trichechus manatus latirostris) for U.S. Fish and Wildlife Service’s 5-year reviews of the status of the species as listed under the Endangered Species Act.  The model evaluation is presented in a standardized reporting framework, modified from the TRACE (TRAnsparent and Comprehensive model Evaluation) protocol first introduced for environmental threat analyses.  We identify this new protocol as TRACE-MANATEE SURVIVAL and this model evaluation specifically as TRACE-MANATEE SURVIVAL, Barker RD version 1. The longer-term objectives of the manatee standard reporting format are to (1) communicate to resource managers consistent evaluation information over sequential modeling efforts; (2) build understanding and expertise on the structure and function of the models; (3) document changes in model structures and applications in response to evolving management objectives, new biological and ecological knowledge, and new statistical advances; and (4) provide greater transparency for management and research review.

Prognostic factors and relative risk for survival in N1-3 oral squamous cell carcinoma: a multivariate analysis using Cox's hazard model.

PubMed

Noguchi, M; Kido, Y; Kubota, H; Kinjo, H; Kohama, G

1999-12-01

The records of 136 patients with N1-3 oral squamous cell carcinoma treated by surgery were investigated retrospectively, with the aim of finding out which factors were predictive of survival on multivariate analysis. Four independent factors significantly influenced survival in the following order: pN stage; T stage; histological grade; and N stage. The most significant was pN stage, the five-year survival for patients with pN0 being 91% and for patients with pN1-3 41%. A further study was carried out on the 80 patients with pN1-3 to find out their prognostic factors for survival and the independent factors identified by multivariate analysis were T stage and presence or absence of extracapsular spread to metastatic lymph nodes.

Genetic risk score and cardiovascular mortality in a southern european population with coronary artery disease.

PubMed

Pereira, Andreia; Mendonca, Maria Isabel; Sousa, Ana Célia; Borges, Sofia; Freitas, Sónia; Henriques, Eva; Rodrigues, Mariana; Freitas, Ana Isabel; Guerra, Graça; Ornelas, Ilídio; Pereira, Décio; Brehm, António; Palma Dos Reis, Roberto

2017-06-01

Several genetic risk scores (GRS) have been associated with cardiovascular disease; their role, however, in survival from proven coronary artery disease (CAD) have yielded conflicting results. The objective of this study was to evaluate long-term cardiovascular mortality according to the genetic risk score in a Southern European population with CAD. A cohort of 1464 CAD patients with angiographic proven CAD were followed up prospectively for up to 58.3 (interquartile range: 25.8-88.1) months. Genotyping of 32 single-nucleotide polymorphisms previously associated with CAD was performed using oligonucleotides probes marked with fluorescence for each allele. GRS was constructed according to the additive model assuming codominance and categorised using the median (=26). Cox Regression analysis was performed to determine independent multivariate predictors of cardiovascular mortality. Kaplan-Meier survival curves compared high vs low GRS using log-rank test. C-index was done for our population, as a measure of discrimination in survival analysis model. During a mean follow-up of 58.3 months, 156 patients (10.7%) died, 107 (7.3%) of CV causes. High GRS (≥26) was associated with reduced cardiovascular survival. Survival analysis with Cox regression model adjusted for 8 variables showed that high GRS, dyslipidemia, diabetes and 3-vessel disease were independent risk factors for cardiovascular mortality (HR=1.53, P=.037; HR=3.64, P=.012; HR=1.75, P=.004; HR=2.97, P<.0001, respectively). At the end of follow-up, the estimated survival probability was 70.8% for high GRS and 80.8% for low GRS (Log-rank test 5.6; P=.018). C-Index of 0.71 was found when GRS was added to a multivariate survival model of diabetes, dyslipidemia, smoking, hypertension and 3 vessel disease, stable angina and dual antiplatelet therapy. Besides the classical risk factors management, this work highlights the relevance of the genetic profile in survival from CAD. It is expected that new therapies will be dirsected to gene targets with proven value in cardiovascular survival. © 2017 John Wiley & Sons Ltd.

Developing population models with data from marked individuals

USGS Publications Warehouse

Hae Yeong Ryu,; Kevin T. Shoemaker,; Eva Kneip,; Anna Pidgeon,; Patricia Heglund,; Brooke Bateman,; Thogmartin, Wayne E.; Reşit Akçakaya,

2016-01-01

Population viability analysis (PVA) is a powerful tool for biodiversity assessments, but its use has been limited because of the requirements for fully specified population models such as demographic structure, density-dependence, environmental stochasticity, and specification of uncertainties. Developing a fully specified population model from commonly available data sources – notably, mark–recapture studies – remains complicated due to lack of practical methods for estimating fecundity, true survival (as opposed to apparent survival), natural temporal variability in both survival and fecundity, density-dependence in the demographic parameters, and uncertainty in model parameters. We present a general method that estimates all the key parameters required to specify a stochastic, matrix-based population model, constructed using a long-term mark–recapture dataset. Unlike standard mark–recapture analyses, our approach provides estimates of true survival rates and fecundities, their respective natural temporal variabilities, and density-dependence functions, making it possible to construct a population model for long-term projection of population dynamics. Furthermore, our method includes a formal quantification of parameter uncertainty for global (multivariate) sensitivity analysis. We apply this approach to 9 bird species and demonstrate the feasibility of using data from the Monitoring Avian Productivity and Survivorship (MAPS) program. Bias-correction factors for raw estimates of survival and fecundity derived from mark–recapture data (apparent survival and juvenile:adult ratio, respectively) were non-negligible, and corrected parameters were generally more biologically reasonable than their uncorrected counterparts. Our method allows the development of fully specified stochastic population models using a single, widely available data source, substantially reducing the barriers that have until now limited the widespread application of PVA. This method is expected to greatly enhance our understanding of the processes underlying population dynamics and our ability to analyze viability and project trends for species of conservation concern.

Nomogram Prediction of Overall Survival After Curative Irradiation for Uterine Cervical Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Seo, YoungSeok; Yoo, Seong Yul; Kim, Mi-Sook

Purpose: The purpose of this study was to develop a nomogram capable of predicting the probability of 5-year survival after radical radiotherapy (RT) without chemotherapy for uterine cervical cancer. Methods and Materials: We retrospectively analyzed 549 patients that underwent radical RT for uterine cervical cancer between March 1994 and April 2002 at our institution. Multivariate analysis using Cox proportional hazards regression was performed and this Cox model was used as the basis for the devised nomogram. The model was internally validated for discrimination and calibration by bootstrap resampling. Results: By multivariate regression analysis, the model showed that age, hemoglobin levelmore » before RT, Federation Internationale de Gynecologie Obstetrique (FIGO) stage, maximal tumor diameter, lymph node status, and RT dose at Point A significantly predicted overall survival. The survival prediction model demonstrated good calibration and discrimination. The bootstrap-corrected concordance index was 0.67. The predictive ability of the nomogram proved to be superior to FIGO stage (p = 0.01). Conclusions: The devised nomogram offers a significantly better level of discrimination than the FIGO staging system. In particular, it improves predictions of survival probability and could be useful for counseling patients, choosing treatment modalities and schedules, and designing clinical trials. However, before this nomogram is used clinically, it should be externally validated.« less

Improving Gastric Cancer Outcome Prediction Using Single Time-Point Artificial Neural Network Models

PubMed Central

Nilsaz-Dezfouli, Hamid; Abu-Bakar, Mohd Rizam; Arasan, Jayanthi; Adam, Mohd Bakri; Pourhoseingholi, Mohamad Amin

2017-01-01

In cancer studies, the prediction of cancer outcome based on a set of prognostic variables has been a long-standing topic of interest. Current statistical methods for survival analysis offer the possibility of modelling cancer survivability but require unrealistic assumptions about the survival time distribution or proportionality of hazard. Therefore, attention must be paid in developing nonlinear models with less restrictive assumptions. Artificial neural network (ANN) models are primarily useful in prediction when nonlinear approaches are required to sift through the plethora of available information. The applications of ANN models for prognostic and diagnostic classification in medicine have attracted a lot of interest. The applications of ANN models in modelling the survival of patients with gastric cancer have been discussed in some studies without completely considering the censored data. This study proposes an ANN model for predicting gastric cancer survivability, considering the censored data. Five separate single time-point ANN models were developed to predict the outcome of patients after 1, 2, 3, 4, and 5 years. The performance of ANN model in predicting the probabilities of death is consistently high for all time points according to the accuracy and the area under the receiver operating characteristic curve. PMID:28469384

Stereotactic Body Radiotherapy Versus Surgery for Medically Operable Stage I Non-Small-Cell Lung Cancer: A Markov Model-Based Decision Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Louie, Alexander V.; Rodrigues, George, E-mail: george.rodrigues@lhsc.on.ca; Department of Epidemiology/Biostatistics, University of Western Ontario, London, ON

Purpose: To compare the quality-adjusted life expectancy and overall survival in patients with Stage I non-small-cell lung cancer (NSCLC) treated with either stereotactic body radiation therapy (SBRT) or surgery. Methods and Materials: We constructed a Markov model to describe health states after either SBRT or lobectomy for Stage I NSCLC for a 5-year time frame. We report various treatment strategy survival outcomes stratified by age, sex, and pack-year history of smoking, and compared these with an external outcome prediction tool (Adjuvant{exclamation_point} Online). Results: Overall survival, cancer-specific survival, and other causes of death as predicted by our model correlated closely withmore » those predicted by the external prediction tool. Overall survival at 5 years as predicted by baseline analysis of our model is in favor of surgery, with a benefit ranging from 2.2% to 3.0% for all cohorts. Mean quality-adjusted life expectancy ranged from 3.28 to 3.78 years after surgery and from 3.35 to 3.87 years for SBRT. The utility threshold for preferring SBRT over surgery was 0.90. Outcomes were sensitive to quality of life, the proportion of local and regional recurrences treated with standard vs. palliative treatments, and the surgery- and SBRT-related mortalities. Conclusions: The role of SBRT in the medically operable patient is yet to be defined. Our model indicates that SBRT may offer comparable overall survival and quality-adjusted life expectancy as compared with surgical resection. Well-powered prospective studies comparing surgery vs. SBRT in early-stage lung cancer are warranted to further investigate the relative survival, quality of life, and cost characteristics of both treatment paradigms.« less

Current estimates of the cure fraction: a feasibility study of statistical cure for breast and colorectal cancer.

PubMed

Stedman, Margaret R; Feuer, Eric J; Mariotto, Angela B

2014-11-01

The probability of cure is a long-term prognostic measure of cancer survival. Estimates of the cure fraction, the proportion of patients "cured" of the disease, are based on extrapolating survival models beyond the range of data. The objective of this work is to evaluate the sensitivity of cure fraction estimates to model choice and study design. Data were obtained from the Surveillance, Epidemiology, and End Results (SEER)-9 registries to construct a cohort of breast and colorectal cancer patients diagnosed from 1975 to 1985. In a sensitivity analysis, cure fraction estimates are compared from different study designs with short- and long-term follow-up. Methods tested include: cause-specific and relative survival, parametric mixture, and flexible models. In a separate analysis, estimates are projected for 2008 diagnoses using study designs including the full cohort (1975-2008 diagnoses) and restricted to recent diagnoses (1998-2008) with follow-up to 2009. We show that flexible models often provide higher estimates of the cure fraction compared to parametric mixture models. Log normal models generate lower estimates than Weibull parametric models. In general, 12 years is enough follow-up time to estimate the cure fraction for regional and distant stage colorectal cancer but not for breast cancer. 2008 colorectal cure projections show a 15% increase in the cure fraction since 1985. Estimates of the cure fraction are model and study design dependent. It is best to compare results from multiple models and examine model fit to determine the reliability of the estimate. Early-stage cancers are sensitive to survival type and follow-up time because of their longer survival. More flexible models are susceptible to slight fluctuations in the shape of the survival curve which can influence the stability of the estimate; however, stability may be improved by lengthening follow-up and restricting the cohort to reduce heterogeneity in the data. Published by Oxford University Press 2014.

Identification of subgroups by risk of graft failure after paediatric renal transplantation: application of survival tree models on the ESPN/ERA-EDTA Registry.

PubMed

Lofaro, Danilo; Jager, Kitty J; Abu-Hanna, Ameen; Groothoff, Jaap W; Arikoski, Pekka; Hoecker, Britta; Roussey-Kesler, Gwenaelle; Spasojević, Brankica; Verrina, Enrico; Schaefer, Franz; van Stralen, Karlijn J

2016-02-01

Identification of patient groups by risk of renal graft loss might be helpful for accurate patient counselling and clinical decision-making. Survival tree models are an alternative statistical approach to identify subgroups, offering cut-off points for covariates and an easy-to-interpret representation. Within the European Society of Pediatric Nephrology/European Renal Association-European Dialysis and Transplant Association (ESPN/ERA-EDTA) Registry data we identified paediatric patient groups with specific profiles for 5-year renal graft survival. Two analyses were performed, including (i) parameters known at time of transplantation and (ii) additional clinical measurements obtained early after transplantation. The identified subgroups were added as covariates in two survival models. The prognostic performance of the models was tested and compared with conventional Cox regression analyses. The first analysis included 5275 paediatric renal transplants. The best 5-year graft survival (90.4%) was found among patients who received a renal graft as a pre-emptive transplantation or after short-term dialysis (<45 days), whereas graft survival was poorest (51.7%) in adolescents transplanted after long-term dialysis (>2.2 years). The Cox model including both pre-transplant factors and tree subgroups had a significantly better predictive performance than conventional Cox regression (P < 0.001). In the analysis including clinical factors, graft survival ranged from 97.3% [younger patients with estimated glomerular filtration rate (eGFR) >30 mL/min/1.73 m(2) and dialysis <20 months] to 34.7% (adolescents with eGFR <60 mL/min/1.73 m(2) and dialysis >20 months). Also in this case combining tree findings and clinical factors improved the predictive performance as compared with conventional Cox model models (P < 0.0001). In conclusion, we demonstrated the tree model to be an accurate and attractive tool to predict graft failure for patients with specific characteristics. This may aid the evaluation of individual graft prognosis and thereby the design of measures to improve graft survival in the poor prognosis groups. © The Author 2015. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.

Cure models for estimating hospital-based breast cancer survival.

PubMed

Rama, Ranganathan; Swaminathan, Rajaraman; Venkatesan, Perumal

2010-01-01

Research on cancer survival is enriched by development and application of innovative analytical approaches in relation to standard methods. The aim of the present paper is to document the utility of a mixture model to estimate the cure fraction and compare it with other approaches. The data were for 1,107 patients with locally advanced breast cancer, who completed the neo-adjuvant treatment protocol during 1990-99 at the Cancer Institute (WIA), Chennai, India. Tumour stage, post-operative pathological node (PN) and tumour residue (TR) status were studied. Event free survival probability was estimated using the Kaplan-Meier method. Cure models under proportional and non-proportional hazard assumptions following log normal distribution for survival time were used to estimate both the cure fraction and the survival function for the uncured. Event free survival at 5 and 10 years were 64.2% and 52.6% respectively and cure fraction was 47.5% for all cases together. Follow up ranged between 0-15 years and survival probabilities showed minimal changes after 7 years of follow up. TR and PN emerged as independent prognostic factors using Cox and proportional hazard (PH) cure models. Proportionality condition was violated when tumour stage was considered and it was statistically significant only under PH and not under non PH cure models. However, TR and PN continued to be independent prognostic factors after adjusting for tumour stage using the non PH cure model. A consistent ordering of cure fractions with respect to factors of PN and TR was forthcoming across tumour stages using PH and non PH cure models, but perceptible differences in survival were observed between the two. If PH conditions are violated, analysis using a non PH model is advocated and mixture cure models are useful in estimating the cure fraction and constructing survival curves for non-cures.

Survival of white-tailed deer neonates in Minnesota and South Dakota

USGS Publications Warehouse

Grovenburg, T.W.; Swanson, C.C.; Jacques, C.N.; Klaver, R.W.; Brinkman, T.J.; Burris, B.M.; Deperno, C.S.; Jenks, J.A.

2011-01-01

Understanding the influence of intrinsic (e.g., age, birth mass, and sex) and habitat factors on survival of neonate white-tailed deer improves understanding of population ecology. During 2002–2004, we captured and radiocollared 78 neonates in eastern South Dakota and southwestern Minnesota, of which 16 died before 1 September. Predation accounted for 80% of mortality; the remaining 20% was attributed to starvation. Canids (coyotes [Canis latrans], domestic dogs) accounted for 100% of predation on neonates. We used known fate analysis in Program MARK to estimate survival rates and investigate the influence of intrinsic and habitat variables on survival. We developed 2 a priori model sets, including intrinsic variables (model set 1) and habitat variables (model set 2; forested cover, wetlands, grasslands, and croplands). For model set 1, model {Sage-interval} had the lowest AICc (Akaike's information criterion for small sample size) value, indicating that age at mortality (3-stage age-interval: 0–2 weeks, 2–8 weeks, and >8 weeks) best explained survival. Model set 2 indicated that habitat variables did not further influence survival in the study area; β-estimates and 95% confidence intervals for habitat variables in competing models encompassed zero; thus, we excluded these models from consideration. Overall survival rate using model {Sage-interval} was 0.87 (95% CI = 0.83–0.91); 61% of mortalities occurred at 0–2 weeks of age, 26% at 2–8 weeks of age, and 13% at >8 weeks of age. Our results indicate that variables influencing survival may be area specific. Region-specific data are needed to determine influences of intrinsic and habitat variables on neonate survival before wildlife managers can determine which habitat management activities influence neonate populations.

Pretreatment health behaviors predict survival among patients with head and neck squamous cell carcinoma.

PubMed

Duffy, Sonia A; Ronis, David L; McLean, Scott; Fowler, Karen E; Gruber, Stephen B; Wolf, Gregory T; Terrell, Jeffrey E

2009-04-20

Our prior work has shown that the health behaviors of head and neck cancer patients are interrelated and are associated with quality of life; however, other than smoking, the relationship between health behaviors and survival is unclear. A prospective cohort study was conducted to determine the relationship between five pretreatment health behaviors (smoking, alcohol, diet, physical activity, and sleep) and all-cause survival among 504 head and neck cancer patients. Smoking status was the strongest predictor of survival, with both current smokers (hazard ratio [HR] = 2.4; 95% CI, 1.3 to 4.4) and former smokers (HR = 2.0; 95% CI, 1.2 to 3.5) showing significant associations with poor survival. Problem drinking was associated with survival in the univariate analysis (HR = 1.4; 95% CI, 1.0 to 2.0) but lost significance when controlling for other factors. Low fruit intake was negatively associated with survival in the univariate analysis only (HR = 1.6; 95% CI, 1.1 to 2.1), whereas vegetable intake was not significant in either univariate or multivariate analyses. Although physical activity was associated with survival in the univariate analysis (HR = 0.95; 95% CI, 0.93 to 0.97), it was not significant in the multivariate model. Sleep was not significantly associated with survival in either univariate or multivariate analysis. Control variables that were also independently associated with survival in the multivariate analysis were age, education, tumor site, cancer stage, and surgical treatment. Variation in selected pretreatment health behaviors (eg, smoking, fruit intake, and physical activity) in this population is associated with variation in survival.

Effects of temperature on development, survival and reproduction of insects: Experimental design, data analysis and modeling

Treesearch

Jacques Regniere; James Powell; Barbara Bentz; Vincent Nealis

2012-01-01

The developmental response of insects to temperature is important in understanding the ecology of insect life histories. Temperature-dependent phenology models permit examination of the impacts of temperature on the geographical distributions, population dynamics and management of insects. The measurement of insect developmental, survival and reproductive responses to...

Modeling the Impact of Breast-Feeding by HIV-Infected Women on Child Survival.

ERIC Educational Resources Information Center

Heymann, Sally Jody

1990-01-01

Models the survival outcomes of children in developing countries born to women infected with human immunodeficiency virus (HIV) who are breast-fed, bottle-fed, and wet-nursed. Uses decision analysis to assess the relative risk of child mortality from HIV transmission and non-HIV causes associated with different methods of feeding. (FMW)

Bayesian survival analysis in clinical trials: What methods are used in practice?

PubMed

Brard, Caroline; Le Teuff, Gwénaël; Le Deley, Marie-Cécile; Hampson, Lisa V

2017-02-01

Background Bayesian statistics are an appealing alternative to the traditional frequentist approach to designing, analysing, and reporting of clinical trials, especially in rare diseases. Time-to-event endpoints are widely used in many medical fields. There are additional complexities to designing Bayesian survival trials which arise from the need to specify a model for the survival distribution. The objective of this article was to critically review the use and reporting of Bayesian methods in survival trials. Methods A systematic review of clinical trials using Bayesian survival analyses was performed through PubMed and Web of Science databases. This was complemented by a full text search of the online repositories of pre-selected journals. Cost-effectiveness, dose-finding studies, meta-analyses, and methodological papers using clinical trials were excluded. Results In total, 28 articles met the inclusion criteria, 25 were original reports of clinical trials and 3 were re-analyses of a clinical trial. Most trials were in oncology (n = 25), were randomised controlled (n = 21) phase III trials (n = 13), and half considered a rare disease (n = 13). Bayesian approaches were used for monitoring in 14 trials and for the final analysis only in 14 trials. In the latter case, Bayesian survival analyses were used for the primary analysis in four cases, for the secondary analysis in seven cases, and for the trial re-analysis in three cases. Overall, 12 articles reported fitting Bayesian regression models (semi-parametric, n = 3; parametric, n = 9). Prior distributions were often incompletely reported: 20 articles did not define the prior distribution used for the parameter of interest. Over half of the trials used only non-informative priors for monitoring and the final analysis (n = 12) when it was specified. Indeed, no articles fitting Bayesian regression models placed informative priors on the parameter of interest. The prior for the treatment effect was based on historical data in only four trials. Decision rules were pre-defined in eight cases when trials used Bayesian monitoring, and in only one case when trials adopted a Bayesian approach to the final analysis. Conclusion Few trials implemented a Bayesian survival analysis and few incorporated external data into priors. There is scope to improve the quality of reporting of Bayesian methods in survival trials. Extension of the Consolidated Standards of Reporting Trials statement for reporting Bayesian clinical trials is recommended.

Twenty five years long survival analysis of an individual shortleaf pine trees

Treesearch

Pradip Saud; Thomas B. Lynch; James M. Guldin

2016-01-01

A semi parametric cox proportion hazard model is preferred when censored data and survival time information is available (Kleinbaum and Klein 1996; Alison 2010). Censored data are observations that have incomplete information related to survival time or event time of interest. In repeated forest measurements, usually observations are either right censored or...

Creation of a Prognostic Index for Spine Metastasis to Stratify Survival in Patients Treated With Spinal Stereotactic Radiosurgery: Secondary Analysis of Mature Prospective Trials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tang, Chad; Hess, Kenneth; Bishop, Andrew J.

Purpose: There exists uncertainty in the prognosis of patients following spinal metastasis treatment. We sought to create a scoring system that stratifies patients based on overall survival. Methods and Materials: Patients enrolled in 2 prospective trials investigating stereotactic spine radiation surgery (SSRS) for spinal metastasis with ≥3-year follow-up were analyzed. A multivariate Cox regression model was used to create a survival model. Pretreatment variables included were race, sex, age, performance status, tumor histology, extent of vertebrae involvement, previous therapy at the SSRS site, disease burden, and timing of diagnosis and metastasis. Four survival groups were generated based on the model-derivedmore » survival score. Results: Median follow-up in the 206 patients included in this analysis was 70 months (range: 37-133 months). Seven variables were selected: female sex (hazard ratio [HR] = 0.7, P=.02), Karnofsky performance score (HR = 0.8 per 10-point increase above 60, P=.007), previous surgery at the SSRS site (HR = 0.7, P=.02), previous radiation at the SSRS site (HR = 1.8, P=.001), the SSRS site as the only site of metastatic disease (HR = 0.5, P=.01), number of organ systems involved outside of bone (HR = 1.4 per involved system, P<.001), and >5 year interval from initial diagnosis to detection of spine metastasis (HR = 0.5, P<.001). The median survival among all patients was 25.5 months and was significantly different among survival groups (in group 1 [excellent prognosis], median survival was not reached; group 2 reached 32.4 months; group 3 reached 22.2 months; and group 4 [poor prognosis] reached 9.1 months; P<.001). Pretreatment symptom burden was significantly higher in the patient group with poor survival than in the group with excellent survival (all metrics, P<.05). Conclusions: We developed the prognostic index for spinal metastases (PRISM) model, a new model that identified patient subgroups with poor and excellent prognoses.« less

Modeling the kinetics of survival of Staphylococcus aureus in regional yogurt from goat's milk.

PubMed

Bednarko-Młynarczyk, E; Szteyn, J; Białobrzewski, I; Wiszniewska-Łaszczych, A; Liedtke, K

2015-01-01

The aim of this study was to determine the kinetics of the survival of the test strain of Staphylococcus aureus in the product investigated. Yogurt samples were contaminated with S. aure to an initial level of 10(3)-10(4) cfu/g. The samples were then stored at four temperatures: 4, 6, 20, 22°C. During storage, the number of S. aureus forming colonies in a gram of yogurt was determined every two hours. Based on the results of the analysis culture the curves of survival were plotted. Three primary models were selected to describe the kinetics of changes in the count of bacteria: Cole's model, a modified model of Gompertz and the model of Baranyi and Roberts. Analysis of the model fit carried out based on the average values of Pearson's correlation coefficient, between the modeled and measured values, showed that the Cole's model had the worst fit. The modified Gompertz model showed the count of S. aureus as a negative value. These drawbacks were not observed in the model of Baranyi and Roberts. For this reason, this model best reflects the kinetics of changes in the number of staphylococci in yogurt.

An Incident Cohort Study Comparing Survival on Home Hemodialysis and Peritoneal Dialysis (Australia and New Zealand Dialysis and Transplantation Registry)

PubMed Central

Nadeau-Fredette, Annie-Claire; Hawley, Carmel M.; Pascoe, Elaine M.; Chan, Christopher T.; Clayton, Philip A.; Polkinghorne, Kevan R.; Boudville, Neil; Leblanc, Martine

2015-01-01

Background and objectives Home dialysis is often recognized as a first-choice therapy for patients initiating dialysis. However, studies comparing clinical outcomes between peritoneal dialysis and home hemodialysis have been very limited. Design, setting, participants, & measurements This Australia and New Zealand Dialysis and Transplantation Registry study assessed all Australian and New Zealand adult patients receiving home dialysis on day 90 after initiation of RRT between 2000 and 2012. The primary outcome was overall survival. The secondary outcomes were on-treatment survival, patient and technique survival, and death-censored technique survival. All results were adjusted with three prespecified models: multivariable Cox proportional hazards model (main model), propensity score quintile–stratified model, and propensity score–matched model. Results The study included 10,710 patients on incident peritoneal dialysis and 706 patients on incident home hemodialysis. Treatment with home hemodialysis was associated with better patient survival than treatment with peritoneal dialysis (5-year survival: 85% versus 44%, respectively; log-rank P<0.001). Using multivariable Cox proportional hazards analysis, home hemodialysis was associated with superior patient survival (hazard ratio for overall death, 0.47; 95% confidence interval, 0.38 to 0.59) as well as better on-treatment survival (hazard ratio for on-treatment death, 0.34; 95% confidence interval, 0.26 to 0.45), composite patient and technique survival (hazard ratio for death or technique failure, 0.34; 95% confidence interval, 0.29 to 0.40), and death-censored technique survival (hazard ratio for technique failure, 0.34; 95% confidence interval, 0.28 to 0.41). Similar results were obtained with the propensity score models as well as sensitivity analyses using competing risks models and different definitions for technique failure and lag period after modality switch, during which events were attributed to the initial modality. Conclusions Home hemodialysis was associated with superior patient and technique survival compared with peritoneal dialysis. PMID:26068181

Impact of triple-negative phenotype on prognosis of patients with breast cancer brain metastases.

PubMed

Xu, Zhiyuan; Schlesinger, David; Toulmin, Sushila; Rich, Tyvin; Sheehan, Jason

2012-11-01

To elucidate survival times and identify potential prognostic factors in patients with triple-negative (TN) phenotype who harbored brain metastases arising from breast cancer and who underwent stereotactic radiosurgery (SRS). A total of 103 breast cancer patients with brain metastases were treated with SRS and then studied retrospectively. Twenty-four patients (23.3%) were TN. Survival times were estimated using the Kaplan-Meier method, with a log-rank test computing the survival time difference between groups. Univariate and multivariate analyses to predict potential prognostic factors were performed using a Cox proportional hazard regression model. The presence of TN phenotype was associated with worse survival times, including overall survival after the diagnosis of primary breast cancer (43 months vs. 82 months), neurologic survival after the diagnosis of intracranial metastases, and radiosurgical survival after SRS, with median survival times being 13 months vs. 25 months and 6 months vs. 16 months, respectively (p < 0.002 in all three comparisons). On multivariate analysis, radiosurgical survival benefit was associated with non-TN status and lower recursive partitioning analysis class at the initial SRS. The TN phenotype represents a significant adverse prognostic factor with respect to overall survival, neurologic survival, and radiosurgical survival in breast cancer patients with intracranial metastasis. Recursive partitioning analysis class also served as an important and independent prognostic factor. Copyright © 2012 Elsevier Inc. All rights reserved.

Survival analysis with functional covariates for partial follow-up studies.

PubMed

Fang, Hong-Bin; Wu, Tong Tong; Rapoport, Aaron P; Tan, Ming

2016-12-01

Predictive or prognostic analysis plays an increasingly important role in the era of personalized medicine to identify subsets of patients whom the treatment may benefit the most. Although various time-dependent covariate models are available, such models require that covariates be followed in the whole follow-up period. This article studies a new class of functional survival models where the covariates are only monitored in a time interval that is shorter than the whole follow-up period. This paper is motivated by the analysis of a longitudinal study on advanced myeloma patients who received stem cell transplants and T cell infusions after the transplants. The absolute lymphocyte cell counts were collected serially during hospitalization. Those patients are still followed up if they are alive after hospitalization, while their absolute lymphocyte cell counts cannot be measured after that. Another complication is that absolute lymphocyte cell counts are sparsely and irregularly measured. The conventional method using Cox model with time-varying covariates is not applicable because of the different lengths of observation periods. Analysis based on each single observation obviously underutilizes available information and, more seriously, may yield misleading results. This so-called partial follow-up study design represents increasingly common predictive modeling problem where we have serial multiple biomarkers up to a certain time point, which is shorter than the total length of follow-up. We therefore propose a solution to the partial follow-up design. The new method combines functional principal components analysis and survival analysis with selection of those functional covariates. It also has the advantage of handling sparse and irregularly measured longitudinal observations of covariates and measurement errors. Our analysis based on functional principal components reveals that it is the patterns of the trajectories of absolute lymphocyte cell counts, instead of the actual counts, that affect patient's disease-free survival time. © The Author(s) 2014.

Italian regional health system structure and expected cancer survival.

PubMed

Vercelli, Marina; Lillini, Roberto; Quaglia, Alberto; Capocaccia, Riccardo

2014-01-01

Few studies deal with the association of socioeconomic and health system resource variables with cancer survival at the Italian regional level, where the greatest number of decisions about social and health policies and resource allocations are taken. The present study aimed to describe the causal relationships between socioeconomic and health system resource factors and regional cancer survival and to compute the expected cancer survival at provincial, regional and area levels. Age-standardized relative survival at 5 years from diagnosis of cases incident in 1995-1998 and followed up to 2004 were derived by gender for 11 sites from the Italian Association of Cancer Registries data bank. The socioeconomic and health system resource variables, describing at a regional level the macro-economy, demography, labor market, and health resources for 1995-2005, came from the Health for All database. A principal components factor analysis was applied to the socioeconomic and health system resource variables. For every site, linear regression models were computed considering the relative survival at 5 years as a dependent variable and the principal components factor analysis factors as independent variables. The factors described the socioeconomic and health-related features of the regional systems and were causally related to the characteristics of the patient taken in charge. The models built by the factors allowed computation of the expected relative survival at 5 years with very good concordance with those observed at regional, macro-regional and national levels. In the regions without any cancer registry, survival was coherent with that of neighboring regions with similar socioeconomic and health system resources characteristics. The models highlighted the causal correlations between socioeconomic and health system resources and cancer survival, suggesting that they could be good evaluation tools for the efficiency of the resources allocation and use.

The Design and Analysis of Salmonid Tagging Studies in the Columbia Basin; Volume XII; A Multinomial Model for Estimating Ocean Survival from Salmonid Coded Wire-Tag Data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ryding, Kristen E.; Skalski, John R.

1999-06-01

The purpose of this report is to illustrate the development of a stochastic model using coded wire-tag (CWT) release and age-at-return data, in order to regress first year ocean survival probabilities against coastal ocean conditions and climate covariates.

Defining optimum treatment of patients with pancreatic adenocarcinoma using regret-based decision curve analysis.

PubMed

Hernandez, Jonathan M; Tsalatsanis, Athanasios; Humphries, Leigh Ann; Miladinovic, Branko; Djulbegovic, Benjamin; Velanovich, Vic

2014-06-01

To use regret decision theory methodology to assess three treatment strategies in pancreatic adenocarcinoma. Pancreatic adenocarcinoma is uniformly fatal without operative intervention. Resection can prolong survival in some patients; however, it is associated with significant morbidity and mortality. Regret theory serves as a novel framework linking both rationality and intuition to determine the optimal course for physicians facing difficult decisions related to treatment. We used the Cox proportional hazards model to predict survival of patients with pancreatic adenocarcinoma and generated a decision model using regret-based decision curve analysis, which integrates both the patient's prognosis and the physician's preferences expressed in terms of regret associated with a certain action. A physician's treatment preferences are indicated by a threshold probability, which is the probability of death/survival at which the physician is uncertain whether or not to perform surgery. The analysis modeled 3 possible choices: perform surgery on all patients; never perform surgery; and act according to the prediction model. The records of 156 consecutive patients with pancreatic adenocarcinoma were retrospectively evaluated by a single surgeon at a tertiary referral center. Significant independent predictors of overall survival included preoperative stage [P = 0.005; 95% confidence interval (CI), 1.19-2.27], vitality (P < 0.001; 95% CI, 0.96-0.98), daily physical function (P < 0.001; 95% CI, 0.97-0.99), and pathological stage (P < 0.001; 95% CI, 3.06-16.05). Compared with the "always aggressive" or "always passive" surgical treatment strategies, the survival model was associated with the least amount of regret for a wide range of threshold probabilities. Regret-based decision curve analysis provides a novel perspective for making treatment-related decisions by incorporating the decision maker's preferences expressed as his or her estimates of benefits and harms associated with the treatment considered.

Causal Mediation Analysis of Survival Outcome with Multiple Mediators.

PubMed

Huang, Yen-Tsung; Yang, Hwai-I

2017-05-01

Mediation analyses have been a popular approach to investigate the effect of an exposure on an outcome through a mediator. Mediation models with multiple mediators have been proposed for continuous and dichotomous outcomes. However, development of multimediator models for survival outcomes is still limited. We present methods for multimediator analyses using three survival models: Aalen additive hazard models, Cox proportional hazard models, and semiparametric probit models. Effects through mediators can be characterized by path-specific effects, for which definitions and identifiability assumptions are provided. We derive closed-form expressions for path-specific effects for the three models, which are intuitively interpreted using a causal diagram. Mediation analyses using Cox models under the rare-outcome assumption and Aalen additive hazard models consider effects on log hazard ratio and hazard difference, respectively; analyses using semiparametric probit models consider effects on difference in transformed survival time and survival probability. The three models were applied to a hepatitis study where we investigated effects of hepatitis C on liver cancer incidence mediated through baseline and/or follow-up hepatitis B viral load. The three methods show consistent results on respective effect scales, which suggest an adverse estimated effect of hepatitis C on liver cancer not mediated through hepatitis B, and a protective estimated effect mediated through the baseline (and possibly follow-up) of hepatitis B viral load. Causal mediation analyses of survival outcome with multiple mediators are developed for additive hazard and proportional hazard and probit models with utility demonstrated in a hepatitis study.

Survival analysis for customer satisfaction: A case study

NASA Astrophysics Data System (ADS)

Hadiyat, M. A.; Wahyudi, R. D.; Sari, Y.

2017-11-01

Most customer satisfaction surveys are conducted periodically to track their dynamics. One of the goals of this survey was to evaluate the service design by recognizing the trend of satisfaction score. Many researchers recommended in redesigning the service when the satisfaction scores were decreasing, so that the service life cycle could be predicted qualitatively. However, these scores were usually set in Likert scale and had quantitative properties. Thus, they should also be analyzed in quantitative model so that the predicted service life cycle would be done by applying the survival analysis. This paper discussed a starting point for customer satisfaction survival analysis with a case study in healthcare service.

Sources of variation in survival of breeding female wood ducks

USGS Publications Warehouse

Hartke, Kevin M.; Grand, J.B.; Hepp, G.R.; Folk, T.H.

2006-01-01

In waterfowl, reproduction is physiologically demanding and females are exposed to varying risks of mortality at different periods of the breeding cycle. Moreover, differences among females may influence survival within breeding periods. We captured and fitted female Wood Ducks (Aix sponsa) with radio-transmitters before nest initiation during two breeding seasons to estimate survival and investigate sources of variation in survival. We partitioned the breeding season into three periods (preincubation, incubation, postnesting) according to breeding status of individual females, and used information-theoretic methods to compare models in which daily survival varied among periods, between successful and failed nesting females, and with parameters describing individual heterogeneity. Our analysis suggested that daily survival was best modeled as a function of breeding period, differences between successful and failed nesting females during postnesting, and early incubation body condition of successful females during post-nesting. Model-averaged daily survival was 0.9988 (95% CL: 0.9963-0.9996) during preincubation and 1.0 during incubation. Postnesting daily survival was 1.0 for failed nesting females and 0.9948 (0.9773-0.9988) for successful females, suggesting a trade-off between current reproduction and survival. Female age, body condition at capture, nest initiation date, and brood size generally were not useful for explaining variation in survival. Only early incubation body condition was important for modeling survival of successful females during postnesting; however, weight of evidence was limited and the effect on survival was weak. Mortality was greatest for females during preincubation and for females that nested successfully. Results support the hypothesis that brood care is costly for females. ?? The Cooper Ornithological Society 2006.

A capture-recapture survival analysis model for radio-tagged animals

USGS Publications Warehouse

Pollock, K.H.; Bunck, C.M.; Winterstein, S.R.; Chen, C.-L.; North, P.M.; Nichols, J.D.

1995-01-01

In recent years, survival analysis of radio-tagged animals has developed using methods based on the Kaplan-Meier method used in medical and engineering applications (Pollock et al., 1989a,b). An important assumption of this approach is that all tagged animals with a functioning radio can be relocated at each sampling time with probability 1. This assumption may not always be reasonable in practice. In this paper, we show how a general capture-recapture model can be derived which allows for some probability (less than one) for animals to be relocated. This model is not simply a Jolly-Seber model because it is possible to relocate both dead and live animals, unlike when traditional tagging is used. The model can also be viewed as a generalization of the Kaplan-Meier procedure, thus linking the Jolly-Seber and Kaplan-Meier approaches to survival estimation. We present maximum likelihood estimators and discuss testing between submodels. We also discuss model assumptions and their validity in practice. An example is presented based on canvasback data collected by G. M. Haramis of Patuxent Wildlife Research Center, Laurel, Maryland, USA.

Spatial Random Effects Survival Models to Assess Geographical Inequalities in Dengue Fever Using Bayesian Approach: a Case Study

NASA Astrophysics Data System (ADS)

Astuti Thamrin, Sri; Taufik, Irfan

2018-03-01

Dengue haemorrhagic fever (DHF) is an infectious disease caused by dengue virus. The increasing number of people with DHF disease correlates with the neighbourhood, for example sub-districts, and the characteristics of the sub-districts are formed from individuals who are domiciled in the sub-districts. Data containing individuals and sub-districts is a hierarchical data structure, called multilevel analysis. Frequently encountered response variable of the data is the time until an event occurs. Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in DHF survival. Using a case study approach, we report on the implications of using multilevel with spatial survival models to study geographical inequalities in all cause survival.

White-cheeked Pintail duckling and brood survival across wetland types at Humacao Nature Reserve, Puerto Rico

USGS Publications Warehouse

Davis, J. Brian; Vilella, Francisco; Lancaster, Joseph D.; Lopez-Flores, Marisel; Kaminski, Richard M.; Cruz-Burgos, José A.

2017-01-01

Duckling survival is an important influence on recruitment in several North American Anas species. White-cheeked Pintail (Anas bahamensis) breeding in Puerto Rico encounter a variety of wetland types that may influence duckling survival. We monitored fates of 92 radio-tagged ducklings in 31 broods in 5 wetland habitat types at Humacao Nature Reserve in southeastern Puerto Rico from 2000 to 2002. Wetlands included 2 separate coastal lagoon complexes, mangrove forest, and managed and unmanaged wetland impoundments containing herbaceous vegetation. We used known-fate models to estimate daily and interval survival rates of ducklings and broods. We conducted conservative and liberal analyses of survival because of uncertain fates of 36 ducklings. In the conservative analysis, the most parsimonious model for duckling survival contained wetland type and a positive influence of daily precipitation. In the liberal analysis, duckling survival also varied among wetlands, was positively influenced by daily precipitation, but negatively influenced by hatch date. Brood survival was also positively influenced by precipitation and female body mass. Managed wetland impoundments and shallowly flooded lagoon habitats containing ferns, interspersed cattail (Typha dominguensis), and other herbaceous cover promoted up to 3 times higher survival of ducklings over the course of a 30-day duckling period than we found in mangroves, more deeply flooded lagoons with predominately restricted shoreline cover, or unmanaged impoundments overgrown with vegetation. Broad confidence intervals for survival estimates among wetlands preclude unequivocal interpretation, but our results suggest that White-cheeked Pintail ducklings survive poorly in mangroves but benefit from appropriate management.

Pretreatment Health Behaviors Predict Survival Among Patients With Head and Neck Squamous Cell Carcinoma

PubMed Central

Duffy, Sonia A.; Ronis, David L.; McLean, Scott; Fowler, Karen E.; Gruber, Stephen B.; Wolf, Gregory T.; Terrell, Jeffrey E.

2009-01-01

Purpose Our prior work has shown that the health behaviors of head and neck cancer patients are interrelated and are associated with quality of life; however, other than smoking, the relationship between health behaviors and survival is unclear. Patients and Methods A prospective cohort study was conducted to determine the relationship between five pretreatment health behaviors (smoking, alcohol, diet, physical activity, and sleep) and all-cause survival among 504 head and neck cancer patients. Results Smoking status was the strongest predictor of survival, with both current smokers (hazard ratio [HR] = 2.4; 95% CI, 1.3 to 4.4) and former smokers (HR = 2.0; 95% CI, 1.2 to 3.5) showing significant associations with poor survival. Problem drinking was associated with survival in the univariate analysis (HR = 1.4; 95% CI, 1.0 to 2.0) but lost significance when controlling for other factors. Low fruit intake was negatively associated with survival in the univariate analysis only (HR = 1.6; 95% CI, 1.1 to 2.1), whereas vegetable intake was not significant in either univariate or multivariate analyses. Although physical activity was associated with survival in the univariate analysis (HR = 0.95; 95% CI, 0.93 to 0.97), it was not significant in the multivariate model. Sleep was not significantly associated with survival in either univariate or multivariate analysis. Control variables that were also independently associated with survival in the multivariate analysis were age, education, tumor site, cancer stage, and surgical treatment. Conclusion Variation in selected pretreatment health behaviors (eg, smoking, fruit intake, and physical activity) in this population is associated with variation in survival. PMID:19289626

Drawing Nomograms with R: applications to categorical outcome and survival data.

PubMed

Zhang, Zhongheng; Kattan, Michael W

2017-05-01

Outcome prediction is a major task in clinical medicine. The standard approach to this work is to collect a variety of predictors and build a model of appropriate type. The model is a mathematical equation that connects the outcome of interest with the predictors. A new patient with given clinical characteristics can be predicted for outcome with this model. However, the equation describing the relationship between predictors and outcome is often complex and the computation requires software for practical use. There is another method called nomogram which is a graphical calculating device allowing an approximate graphical computation of a mathematical function. In this article, we describe how to draw nomograms for various outcomes with nomogram() function. Binary outcome is fit by logistic regression model and the outcome of interest is the probability of the event of interest. Ordinal outcome variable is also discussed. Survival analysis can be fit with parametric model to fully describe the distributions of survival time. Statistics such as the median survival time, survival probability up to a specific time point are taken as the outcome of interest.

A survival tree method for the analysis of discrete event times in clinical and epidemiological studies.

PubMed

Schmid, Matthias; Küchenhoff, Helmut; Hoerauf, Achim; Tutz, Gerhard

2016-02-28

Survival trees are a popular alternative to parametric survival modeling when there are interactions between the predictor variables or when the aim is to stratify patients into prognostic subgroups. A limitation of classical survival tree methodology is that most algorithms for tree construction are designed for continuous outcome variables. Hence, classical methods might not be appropriate if failure time data are measured on a discrete time scale (as is often the case in longitudinal studies where data are collected, e.g., quarterly or yearly). To address this issue, we develop a method for discrete survival tree construction. The proposed technique is based on the result that the likelihood of a discrete survival model is equivalent to the likelihood of a regression model for binary outcome data. Hence, we modify tree construction methods for binary outcomes such that they result in optimized partitions for the estimation of discrete hazard functions. By applying the proposed method to data from a randomized trial in patients with filarial lymphedema, we demonstrate how discrete survival trees can be used to identify clinically relevant patient groups with similar survival behavior. Copyright © 2015 John Wiley & Sons, Ltd.

Genetic polymorphisms in pre-miRNAs predict the survival of non-small-cell lung cancer in Chinese population: a cohort study and a meta-analysis

PubMed Central

Xia, Lingzi; Yin, Zhihua; Li, Xuelian; Ren, Yangwu; Zhang, Haibo; Zhao, Yuxia; Zhou, Baosen

2017-01-01

Background To explore the association of genetic polymorphisms in pre-miRNA 30c-1 rs928508 and pre-miRNA 27a rs895819 with non-small-cell lung cancer prognosis. Materials and Methods 480 patients from five hospitals were enrolled in this prospective cohort study. They were followed up for five years. The association between genotypes and overall survival was assessed by Cox proportional hazards regression models. A meta-analysis was conducted to provide evidence for the effect of microRNA 27a rs895819 on cancer survival. Results G-allele containing genotypes of microRNA 30c-1 polymorphisms and C-allele containing genotypes of microRNA 27a were significantly associated with poorer overall survival. Multivariate Cox regression models indicated that these genetic polymorhpisms were independently predictive factors of poorer overall survival. In stratified analysis, the effect was observed in many strata. The significant joint effect was also observed in our study. Patients with G allele of microRNA 30c-1 rs928508 and C allele of microRNA 27a rs895819 had the poorer overall survival than patients with C allele of rs928508 and T allele of rs895819. The effect of the microRNA 27a rs895819 on non-small cell lung cancer overall survival was supported by the meta-analysis results. Conclusions The two single nucleotide polymorphisms in microRNA 30c-1 and microRNA 27a can predict the outcome of non-small cell lung cancer patients and they may decrease the sensitivity to anti-cancer drugs. PMID:29100439

Dynamic frailty models based on compound birth-death processes.

PubMed

Putter, Hein; van Houwelingen, Hans C

2015-07-01

Frailty models are used in survival analysis to model unobserved heterogeneity. They accommodate such heterogeneity by the inclusion of a random term, the frailty, which is assumed to multiply the hazard of a subject (individual frailty) or the hazards of all subjects in a cluster (shared frailty). Typically, the frailty term is assumed to be constant over time. This is a restrictive assumption and extensions to allow for time-varying or dynamic frailties are of interest. In this paper, we extend the auto-correlated frailty models of Henderson and Shimakura and of Fiocco, Putter and van Houwelingen, developed for longitudinal count data and discrete survival data, to continuous survival data. We present a rigorous construction of the frailty processes in continuous time based on compound birth-death processes. When the frailty processes are used as mixtures in models for survival data, we derive the marginal hazards and survival functions and the marginal bivariate survival functions and cross-ratio function. We derive distributional properties of the processes, conditional on observed data, and show how to obtain the maximum likelihood estimators of the parameters of the model using a (stochastic) expectation-maximization algorithm. The methods are applied to a publicly available data set. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Semi-parametric regression model for survival data: graphical visualization with R

PubMed Central

2016-01-01

Cox proportional hazards model is a semi-parametric model that leaves its baseline hazard function unspecified. The rationale to use Cox proportional hazards model is that (I) the underlying form of hazard function is stringent and unrealistic, and (II) researchers are only interested in estimation of how the hazard changes with covariate (relative hazard). Cox regression model can be easily fit with coxph() function in survival package. Stratified Cox model may be used for covariate that violates the proportional hazards assumption. The relative importance of covariates in population can be examined with the rankhazard package in R. Hazard ratio curves for continuous covariates can be visualized using smoothHR package. This curve helps to better understand the effects that each continuous covariate has on the outcome. Population attributable fraction is a classic quantity in epidemiology to evaluate the impact of risk factor on the occurrence of event in the population. In survival analysis, the adjusted/unadjusted attributable fraction can be plotted against survival time to obtain attributable fraction function. PMID:28090517

Parametric analysis for matched pair survival data.

PubMed

Manatunga, A K; Oakes, D

1999-12-01

Hougaard's (1986) bivariate Weibull distribution with positive stable frailties is applied to matched pairs survival data when either or both components of the pair may be censored and covariate vectors may be of arbitrary fixed length. When there is no censoring, we quantify the corresponding gain in Fisher information over a fixed-effects analysis. With the appropriate parameterization, the results take a simple algebraic form. An alternative marginal ("independence working model") approach to estimation is also considered. This method ignores the correlation between the two survival times in the derivation of the estimator, but provides a valid estimate of standard error. It is shown that when both the correlation between the two survival times is high, and the ratio of the within-pair variability to the between-pair variability of the covariates is high, the fixed-effects analysis captures most of the information about the regression coefficient but the independence working model does badly. When the correlation is low, and/or most of the variability of the covariates occurs between pairs, the reverse is true. The random effects model is applied to data on skin grafts, and on loss of visual acuity among diabetics. In conclusion some extensions of the methods are indicated and they are placed in a wider context of Generalized Estimation Equation methodology.

Survival in Patients with Advanced Non-cystic Fibrosis Bronchiectasis Versus Cystic Fibrosis on the Waitlist for Lung Transplantation.

PubMed

Hayes, Don; Kopp, Benjamin T; Tobias, Joseph D; Woodley, Frederick W; Mansour, Heidi M; Tumin, Dmitry; Kirkby, Stephen E

2015-12-01

Survival in non-cystic fibrosis (CF) bronchiectasis is not well studied. The United Network for Organ Sharing database was queried from 1987 to 2013 to compare survival in adult patients with non-CF bronchiectasis to patients with CF listed for lung transplantation (LTx). Each subject was tracked from waitlist entry date until death or censoring to determine survival differences between the two groups. Of 2112 listed lung transplant candidates with bronchiectasis (180 non-CF, 1932 CF), 1617 were used for univariate Cox and Kaplan-Meier survival function analysis, 1173 for multivariate Cox models, and 182 for matched-pairs analysis based on propensity scores. Compared to CF, patients with non-CF bronchiectasis had a significantly lower mortality by univariate Cox analysis (HR 0.565; 95 % CI 0.424, 0.754; p < 0.001). Adjusting for potential confounders, multivariate Cox models identified a significant reduction in risk for death associated with non-CF bronchiectasis who were lung transplant candidates (HR 0.684; 95 % CI 0.475, 0.985; p = 0.041). Results were consistent in multivariate models adjusting for pulmonary hypertension and forced expiratory volume in one second. Non-CF bronchiectasis with advanced lung disease was associated with significantly lower mortality hazard compared to CF bronchiectasis on the waitlist for LTx. Separate referral and listing criteria for LTx in non-CF and CF populations should be considered.

A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data.

PubMed

Nasejje, Justine B; Mwambi, Henry; Dheda, Keertan; Lesosky, Maia

2017-07-28

Random survival forest (RSF) models have been identified as alternative methods to the Cox proportional hazards model in analysing time-to-event data. These methods, however, have been criticised for the bias that results from favouring covariates with many split-points and hence conditional inference forests for time-to-event data have been suggested. Conditional inference forests (CIF) are known to correct the bias in RSF models by separating the procedure for the best covariate to split on from that of the best split point search for the selected covariate. In this study, we compare the random survival forest model to the conditional inference model (CIF) using twenty-two simulated time-to-event datasets. We also analysed two real time-to-event datasets. The first dataset is based on the survival of children under-five years of age in Uganda and it consists of categorical covariates with most of them having more than two levels (many split-points). The second dataset is based on the survival of patients with extremely drug resistant tuberculosis (XDR TB) which consists of mainly categorical covariates with two levels (few split-points). The study findings indicate that the conditional inference forest model is superior to random survival forest models in analysing time-to-event data that consists of covariates with many split-points based on the values of the bootstrap cross-validated estimates for integrated Brier scores. However, conditional inference forests perform comparably similar to random survival forests models in analysing time-to-event data consisting of covariates with fewer split-points. Although survival forests are promising methods in analysing time-to-event data, it is important to identify the best forest model for analysis based on the nature of covariates of the dataset in question.

Tests for senescent decline in annual survival probabilities of common pochards, Aythya ferina

USGS Publications Warehouse

Nichols, J.D.; Hines, J.E.; Blums, P.

1997-01-01

Senescent decline in survival probabilities of animals is a topic about which much has been written but little is known. Here, we present formal tests of senescence hypotheses, using 1373 recaptures from 8877 duckling (age 0) and 504 yearling Common Pochards (Aythya ferina) banded at a Latvian study site, 1975-1992. The tests are based on capture-recapture models that explicitly incorporate sampling probabilities that, themselves, may exhibit timeand age-specific variation. The tests provided no evidence of senescent decline in survival probabilities for this species. Power of the most useful test was low for gradual declines in annual survival probability with age, but good for steeper declines. We recommend use of this type of capture-recapture modeling and analysis for other investigations of senescence in animal survival rates.

Human papilloma virus and survival of oropharyngeal cancer patients treated with surgery and adjuvant radiotherapy.

PubMed

Broglie, Martina A; Soltermann, Alex; Haile, Sarah R; Huber, Gerhard F; Stoeckli, Sandro J

2015-07-01

Impact of p16 protein, a surrogate marker for human papilloma virus induced cancer, p53 and EGFR as well as clinical factors on survival in a patient cohort with oropharyngeal squamous cell carcinoma (OPSCC) treated by surgical resection and adjuvant radiotherapy (RT) ± concomitant chemotherapy (CT). This is a retrospective analysis of patient's charts and tumor tissue. 57 patients were consecutively included and their tumor tissue assembled on a tissue microarray following immunohistochemical analysis. Survival times were estimated by means of Kaplan-Meier analysis. The importance of clinical and immunohistochemical factors for outcome was estimated by cox proportional hazard models. With 88% 5-year overall survival, 91% 5-year disease-specific survival and 91% 5-year disease-free survival, respectively, we found excellent survival rates in this surgically treated patient cohort of mainly advanced OPSCC (93% AJCC stage III or IV). The only factors positively influencing survival were p16 overexpression as well as p53 negativity and even more pronounced the combination of those biomarkers. Survival analysis of patients classified into three risk categories according to an algorithm based on p16, smoking, T- and N-category revealed a low, intermediate and high-risk group with significant survival differences between the low and the high-risk group. Patients with OPSCC can be successfully treated by surgery and adjuvant RT ± CT with a clear survival benefit of p16 positive, p53 negative patients. We recommend considering a combination of immunohistochemical (p16, p53) and clinical factors (smoking, T- and N-category) for risk stratification.

Meta-analysis of neutropenia or leukopenia as a prognostic factor in patients with malignant disease undergoing chemotherapy.

PubMed

Shitara, Kohei; Matsuo, Keitaro; Oze, Isao; Mizota, Ayako; Kondo, Chihiro; Nomura, Motoo; Yokota, Tomoya; Takahari, Daisuke; Ura, Takashi; Muro, Kei

2011-08-01

We performed a systematic review and meta-analysis to determine the impact of neutropenia or leukopenia experienced during chemotherapy on survival. Eligible studies included prospective or retrospective analyses that evaluated neutropenia or leukopenia as a prognostic factor for overall survival or disease-free survival. Statistical analyses were conducted to calculate a summary hazard ratio and 95% confidence interval (CI) using random-effects or fixed-effects models based on the heterogeneity of the included studies. Thirteen trials were selected for the meta-analysis, with a total of 9,528 patients. The hazard ratio of death was 0.69 (95% CI, 0.64-0.75) for patients with higher-grade neutropenia or leukopenia compared to patients with lower-grade or lack of cytopenia. Our analysis was also stratified by statistical method (any statistical method to decrease lead-time bias; time-varying analysis or landmark analysis), but no differences were observed. Our results indicate that neutropenia or leukopenia experienced during chemotherapy is associated with improved survival in patients with advanced cancer or hematological malignancies undergoing chemotherapy. Future prospective analyses designed to investigate the potential impact of chemotherapy dose adjustment coupled with monitoring of neutropenia or leukopenia on survival are warranted.

Performance Analysis of Garbage Collection and Dynamic Reordering in a Lisp System. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Llames, Rene Lim

1991-01-01

Generation based garbage collection and dynamic reordering of objects are two techniques for improving the efficiency of memory management in Lisp and similar dynamic language systems. An analysis of the effect of generation configuration is presented, focusing on the effect of a number of generations and generation capabilities. Analytic timing and survival models are used to represent garbage collection runtime and to derive structural results on its behavior. The survival model provides bounds on the age of objects surviving a garbage collection at a particular level. Empirical results show that execution time is most sensitive to the capacity of the youngest generation. A technique called scanning for transport statistics, for evaluating the effectiveness of reordering independent of main memory size, is presented.

Complete hazard ranking to analyze right-censored data: An ALS survival study.

PubMed

Huang, Zhengnan; Zhang, Hongjiu; Boss, Jonathan; Goutman, Stephen A; Mukherjee, Bhramar; Dinov, Ivo D; Guan, Yuanfang

2017-12-01

Survival analysis represents an important outcome measure in clinical research and clinical trials; further, survival ranking may offer additional advantages in clinical trials. In this study, we developed GuanRank, a non-parametric ranking-based technique to transform patients' survival data into a linear space of hazard ranks. The transformation enables the utilization of machine learning base-learners including Gaussian process regression, Lasso, and random forest on survival data. The method was submitted to the DREAM Amyotrophic Lateral Sclerosis (ALS) Stratification Challenge. Ranked first place, the model gave more accurate ranking predictions on the PRO-ACT ALS dataset in comparison to Cox proportional hazard model. By utilizing right-censored data in its training process, the method demonstrated its state-of-the-art predictive power in ALS survival ranking. Its feature selection identified multiple important factors, some of which conflicts with previous studies.

2D- and 3D-quantitative structure-activity relationship studies for a series of phenazine N,N'-dioxide as antitumour agents.

PubMed

Cunha, Jonathan Da; Lavaggi, María Laura; Abasolo, María Inés; Cerecetto, Hugo; González, Mercedes

2011-12-01

Hypoxic regions of tumours are associated with increased resistance to radiation and chemotherapy. Nevertheless, hypoxia has been used as a tool for specific activation of some antitumour prodrugs, named bioreductive agents. Phenazine dioxides are an example of such bioreductive prodrugs. Our 2D-quantitative structure activity relationship studies established that phenazine dioxides electronic and lipophilic descriptors are related to survival fraction in oxia or in hypoxia. Additionally, statistically significant models, derived by partial least squares, were obtained between survival fraction in oxia and comparative molecular field analysis standard model (r² = 0.755, q² = 0.505 and F = 26.70) or comparative molecular similarity indices analysis-combined steric and electrostatic fields (r² = 0.757, q² = 0.527 and F = 14.93), and survival fraction in hypoxia and comparative molecular field analysis standard model (r² = 0.736, q² = 0.521 and F = 18.63) or comparative molecular similarity indices analysis-hydrogen bond acceptor field (r² = 0.858, q² = 0.737 and F = 27.19). Categorical classification was used for the biological parameter selective cytotoxicity emerging also good models, derived by soft independent modelling of class analogy, with both comparative molecular field analysis standard model (96% of overall classification accuracy) and comparative molecular similarity indices analysis-steric field (92% of overall classification accuracy). 2D- and 3D-quantitative structure-activity relationships models provided important insights into the chemical and structural basis involved in the molecular recognition process of these phenazines as bioreductive agents and should be useful for the design of new structurally related analogues with improved potency. © 2011 John Wiley & Sons A/S.

Molecular Signature for Lymphatic Invasion Associated with Survival of Epithelial Ovarian Cancer.

PubMed

Paik, E Sun; Choi, Hyun Jin; Kim, Tae-Joong; Lee, Jeong-Won; Kim, Byoung-Gie; Bae, Duk-Soo; Choi, Chel Hun

2018-04-01

We aimed to develop molecular classifier that can predict lymphatic invasion and their clinical significance in epithelial ovarian cancer (EOC) patients. We analyzed gene expression (mRNA, methylated DNA) in data from The Cancer Genome Atlas. To identify molecular signatures for lymphatic invasion, we found differentially expressed genes. The performance of classifier was validated by receiver operating characteristics analysis, logistic regression, linear discriminant analysis (LDA), and support vector machine (SVM). We assessed prognostic role of classifier using random survival forest (RSF) model and pathway deregulation score (PDS). For external validation,we analyzed microarray data from 26 EOC samples of Samsung Medical Center and curatedOvarianData database. We identified 21 mRNAs, and seven methylated DNAs from primary EOC tissues that predicted lymphatic invasion and created prognostic models. The classifier predicted lymphatic invasion well, which was validated by logistic regression, LDA, and SVM algorithm (C-index of 0.90, 0.71, and 0.74 for mRNA and C-index of 0.64, 0.68, and 0.69 for DNA methylation). Using RSF model, incorporating molecular data with clinical variables improved prediction of progression-free survival compared with using only clinical variables (p < 0.001 and p=0.008). Similarly, PDS enabled us to classify patients into high-risk and low-risk group, which resulted in survival difference in mRNA profiles (log-rank p-value=0.011). In external validation, gene signature was well correlated with prediction of lymphatic invasion and patients' survival. Molecular signature model predicting lymphatic invasion was well performed and also associated with survival of EOC patients.

Comparison of Weibull and Lognormal Cure Models with Cox in the Survival Analysis Of Breast Cancer Patients in Rafsanjan.

PubMed

Hoseini, Mina; Bahrampour, Abbas; Mirzaee, Moghaddameh

2017-02-16

Breast cancer is the most common cancer after lung cancer and the second cause of death. In this study we compared Weibull and Lognormal Cure Models with Cox regression on the survival of breast cancer. A cohort study. The current study retrospective cohort study was conducted on 140 patients referred to Ali Ibn Abitaleb Hospital, Rafsanjan southeastern Iran from 2001 to 2015 suffering from breast cancer. We determined and analyzed the effective survival causes by different models using STATA14. According to AIC, log-normal model was more consistent than Weibull. In the multivariable Lognormal model, the effective factors like smoking, second -hand smoking, drinking herbal tea and the last breast-feeding period were included. In addition, using Cox regression factors of significant were the disease grade, size of tumor and its metastasis (p-value<0.05). As Rafsanjan is surrounded by pistachio orchards and pesticides applied by farmers, people of this city are exposed to agricultural pesticides and its harmful consequences. The effect of the pesticide on breast cancer was studied and the results showed that the effect of pesticides on breast cancer was not in agreement with the models used in this study. Based on different methods for survival analysis, researchers can decide how they can reach a better conclusion. This comparison indicates the result of semi-parametric Cox method is closer to clinical experiences evidences.

A stochastic evolutionary model generating a mixture of exponential distributions

NASA Astrophysics Data System (ADS)

Fenner, Trevor; Levene, Mark; Loizou, George

2016-02-01

Recent interest in human dynamics has stimulated the investigation of the stochastic processes that explain human behaviour in various contexts, such as mobile phone networks and social media. In this paper, we extend the stochastic urn-based model proposed in [T. Fenner, M. Levene, G. Loizou, J. Stat. Mech. 2015, P08015 (2015)] so that it can generate mixture models, in particular, a mixture of exponential distributions. The model is designed to capture the dynamics of survival analysis, traditionally employed in clinical trials, reliability analysis in engineering, and more recently in the analysis of large data sets recording human dynamics. The mixture modelling approach, which is relatively simple and well understood, is very effective in capturing heterogeneity in data. We provide empirical evidence for the validity of the model, using a data set of popular search engine queries collected over a period of 114 months. We show that the survival function of these queries is closely matched by the exponential mixture solution for our model.

Geographic variation in survival and migratory tendency among North American Common Mergansers

USGS Publications Warehouse

Pearce, J.M.; Reed, J.A.; Flint, Paul L.

2005-01-01

Movement ecology and demographic parameters for the Common Merganser (Mergus merganser americanus) in North America are poorly known. We used band-recovery data from five locations across North America spanning the years 1938-1998 to examine migratory patterns and estimate survival rates. We examined competing time-invariant, age-graduated models with program MARK to study sources of variation in survival and reporting probability. We considered age, sex, geographic location, and the use of nasal saddles on hatching year birds at one location as possible sources of variation. Year-of-banding was included as a covariate in a post-hoc analysis. We found that migratory tendency, defined as the average distance between banding and recovery locations, varied geographically. Similarly, all models accounting for the majority of variation in recovery and survival probabilities included location of banding. Models that included age and sex received less support, but we lacked sufficient data to adequately assess these parameters. Model-averaged estimates of annual survival ranged from 0.21 in Michigan to 0.82 in Oklahoma. Heterogeneity in migration tendency and survival suggests that demographic patterns may vary across geographic scales, with implications for the population dynamics of this species.

Decision curve analysis and external validation of the postoperative Karakiewicz nomogram for renal cell carcinoma based on a large single-center study cohort.

PubMed

Zastrow, Stefan; Brookman-May, Sabine; Cong, Thi Anh Phuong; Jurk, Stanislaw; von Bar, Immanuel; Novotny, Vladimir; Wirth, Manfred

2015-03-01

To predict outcome of patients with renal cell carcinoma (RCC) who undergo surgical therapy, risk models and nomograms are valuable tools. External validation on independent datasets is crucial for evaluating accuracy and generalizability of these models. The objective of the present study was to externally validate the postoperative nomogram developed by Karakiewicz et al. for prediction of cancer-specific survival. A total of 1,480 consecutive patients with a median follow-up of 82 months (IQR 46-128) were included into this analysis with 268 RCC-specific deaths. Nomogram-estimated survival probabilities were compared with survival probabilities of the actual cohort, and concordance indices were calculated. Calibration plots and decision curve analyses were used for evaluating calibration and clinical net benefit of the nomogram. Concordance between predictions of the nomogram and survival rates of the cohort was 0.911 after 12, 0.909 after 24 months and 0.896 after 60 months. Comparison of predicted probabilities and actual survival estimates with calibration plots showed an overestimation of tumor-specific survival based on nomogram predictions of high-risk patients, although calibration plots showed a reasonable calibration for probability ranges of interest. Decision curve analysis showed a positive net benefit of nomogram predictions for our patient cohort. The postoperative Karakiewicz nomogram provides a good concordance in this external cohort and is reasonably calibrated. It may overestimate tumor-specific survival in high-risk patients, which should be kept in mind when counseling patients. A positive net benefit of nomogram predictions was proven.

Impact of Donor Arterial Partial Pressure of Oxygen on Outcomes After Lung Transplantation in Adult Cystic Fibrosis Recipients.

PubMed

Hayes, Don; Kopp, Benjamin T; Kirkby, Stephen E; Reynolds, Susan D; Mansour, Heidi M; Tobias, Joseph D; Tumin, Dmitry

2016-08-01

Donor PaO2 levels are used for assessing organs for lung transplantation (LTx), but survival implications of PaO2 levels in adult cystic fibrosis (CF) patients receiving LTx are unclear. UNOS registry data spanning 2005-2013 were used to test for associations of donor PaO2 with patient survival and bronchiolitis obliterans syndrome (BOS) in adult (age ≥ 18 years) first-time LTx recipients diagnosed with CF. The analysis included 1587 patients, of whom 1420 had complete data for multivariable Cox models. No statistically significant differences among donor PaO2 categories of ≤200, 201-300, 301-400, or >400 mmHg were found in univariate survival analysis (log-rank test p = 0.290). BOS onset did not significantly differ across donor PaO2 categories (Chi-square p = 0.480). Multivariable Cox models of patient survival supported the lack of difference across donor PaO2 categories. Interaction analysis found a modest difference in survival between the two top categories of donor PaO2 when examining patients with body mass index (BMI) in the lowest decile (≤16.5 kg/m(2)). Donor PaO2 was not associated with survival or BOS onset in adult CF patients undergoing LTx. Notwithstanding statistically significant interactions between donor PaO2 and BMI, there was no evidence of post-LTx survival risk associated with donor PaO2 below conventional thresholds in any subgroup of adults with CF.

Cost-Effectiveness Analysis of Stereotactic Body Radiation Therapy Compared With Radiofrequency Ablation for Inoperable Colorectal Liver Metastases

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kim, Hayeon, E-mail: kimh2@upmc.edu; Gill, Beant; Beriwal, Sushil

Purpose: To conduct a cost-effectiveness analysis to determine whether stereotactic body radiation therapy (SBRT) is a cost-effective therapy compared with radiofrequency ablation (RFA) for patients with unresectable colorectal cancer (CRC) liver metastases. Methods and Materials: A cost-effectiveness analysis was conducted using a Markov model and 1-month cycle over a lifetime horizon. Transition probabilities, quality of life utilities, and costs associated with SBRT and RFA were captured in the model on the basis of a comprehensive literature review and Medicare reimbursements in 2014. Strategies were compared using the incremental cost-effectiveness ratio, with effectiveness measured in quality-adjusted life years (QALYs). To account formore » model uncertainty, 1-way and probabilistic sensitivity analyses were performed. Strategies were evaluated with a willingness-to-pay threshold of $100,000 per QALY gained. Results: In base case analysis, treatment costs for 3 fractions of SBRT and 1 RFA procedure were $13,000 and $4397, respectively. Median survival was assumed the same for both strategies (25 months). The SBRT costs $8202 more than RFA while gaining 0.05 QALYs, resulting in an incremental cost-effectiveness ratio of $164,660 per QALY gained. In 1-way sensitivity analyses, results were most sensitive to variation of median survival from both treatments. Stereotactic body radiation therapy was economically reasonable if better survival was presumed (>1 month gain) or if used for large tumors (>4 cm). Conclusions: If equal survival is assumed, SBRT is not cost-effective compared with RFA for inoperable colorectal liver metastases. However, if better local control leads to small survival gains with SBRT, this strategy becomes cost-effective. Ideally, these results should be confirmed with prospective comparative data.« less

Survival analysis of heart failure patients: A case study.

PubMed

Ahmad, Tanvir; Munir, Assia; Bhatti, Sajjad Haider; Aftab, Muhammad; Raza, Muhammad Ali

2017-01-01

This study was focused on survival analysis of heart failure patients who were admitted to Institute of Cardiology and Allied hospital Faisalabad-Pakistan during April-December (2015). All the patients were aged 40 years or above, having left ventricular systolic dysfunction, belonging to NYHA class III and IV. Cox regression was used to model mortality considering age, ejection fraction, serum creatinine, serum sodium, anemia, platelets, creatinine phosphokinase, blood pressure, gender, diabetes and smoking status as potentially contributing for mortality. Kaplan Meier plot was used to study the general pattern of survival which showed high intensity of mortality in the initial days and then a gradual increase up to the end of study. Martingale residuals were used to assess functional form of variables. Results were validated computing calibration slope and discrimination ability of model via bootstrapping. For graphical prediction of survival probability, a nomogram was constructed. Age, renal dysfunction, blood pressure, ejection fraction and anemia were found as significant risk factors for mortality among heart failure patients.

Survival analysis in hematologic malignancies: recommendations for clinicians

PubMed Central

Delgado, Julio; Pereira, Arturo; Villamor, Neus; López-Guillermo, Armando; Rozman, Ciril

2014-01-01

The widespread availability of statistical packages has undoubtedly helped hematologists worldwide in the analysis of their data, but has also led to the inappropriate use of statistical methods. In this article, we review some basic concepts of survival analysis and also make recommendations about how and when to perform each particular test using SPSS, Stata and R. In particular, we describe a simple way of defining cut-off points for continuous variables and the appropriate and inappropriate uses of the Kaplan-Meier method and Cox proportional hazard regression models. We also provide practical advice on how to check the proportional hazards assumption and briefly review the role of relative survival and multiple imputation. PMID:25176982

WE-H-BRA-08: A Monte Carlo Cell Nucleus Model for Assessing Cell Survival Probability Based On Particle Track Structure Analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, B; Georgia Institute of Technology, Atlanta, GA; Wang, C

Purpose: To correlate the damage produced by particles of different types and qualities to cell survival on the basis of nanodosimetric analysis and advanced DNA structures in the cell nucleus. Methods: A Monte Carlo code was developed to simulate subnuclear DNA chromatin fibers (CFs) of 30nm utilizing a mean-free-path approach common to radiation transport. The cell nucleus was modeled as a spherical region containing 6000 chromatin-dense domains (CDs) of 400nm diameter, with additional CFs modeled in a sparser interchromatin region. The Geant4-DNA code was utilized to produce a particle track database representing various particles at different energies and dose quantities.more » These tracks were used to stochastically position the DNA structures based on their mean free path to interaction with CFs. Excitation and ionization events intersecting CFs were analyzed using the DBSCAN clustering algorithm for assessment of the likelihood of producing DSBs. Simulated DSBs were then assessed based on their proximity to one another for a probability of inducing cell death. Results: Variations in energy deposition to chromatin fibers match expectations based on differences in particle track structure. The quality of damage to CFs based on different particle types indicate more severe damage by high-LET radiation than low-LET radiation of identical particles. In addition, the model indicates more severe damage by protons than of alpha particles of same LET, which is consistent with differences in their track structure. Cell survival curves have been produced showing the L-Q behavior of sparsely ionizing radiation. Conclusion: Initial results indicate the feasibility of producing cell survival curves based on the Monte Carlo cell nucleus method. Accurate correlation between simulated DNA damage to cell survival on the basis of nanodosimetric analysis can provide insight into the biological responses to various radiation types. Current efforts are directed at producing cell survival curves for high-LET radiation.« less

Long-Term Survival and Death Causes of Systemic Lupus Erythematosus in China

PubMed Central

Wang, Ziqian; Wang, Yanhong; Zhu, Rongrong; Tian, Xinping; Xu, Dong; Wang, Qian; Wu, Chanyuan; Zhang, Shangzhu; Zhao, Jiuliang; Zhao, Yan; Li, Mengtao; Zeng, Xiaofeng

2015-01-01

Abstract Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with an increased risk of death compared to general population. Although previous studies showed improvement in survival of SLE, the long-term prognosis has not been elaborated in China. This study aims to integrate the observational studies estimating current long-term survival of Chinese SLE patients and analyze the death-cause situation of SLE in China. The study is a systemic review of English and non-English articles using MEDLINE, EMBASE, CNKI, WANFANG, and SINOMED databases. Additional studies were found by consultation with clinical experts, browse of references in selected papers, and search of related textbooks. Our major search terms were SLE, follow-up, prognosis, survival, mortality, and China. We included cohort studies for survival analysis, and both cohort studies and case series for death-cause analysis in China. The extraction of the articles were done by 2 authors independently using predesigned charts, including characteristics of study, clinical data, analyzing data, and study quality indicators. All pooled analyses were conducted both for random-effects model and fixed-effects model. Funnel plots and Egger regression tests were applied to check potential publication bias. Heterogeneity was tested by sensitivity analysis. We identified 5 studies for survival analysis comprising 4469 Chinese patients with SLE (380 observed deaths). Thirty-six studies were suitable for death-cause analysis with 2179 observed deaths (derived from more than 20,000 Chinese patients with SLE). The overall pooled survival rates for SLE in China were 94% for 5-year survival rate and 89% for 10-year survival rate after disease onset from the year 1995 to 2013, which were similar with previous publications in Asia-Pacific area. The proportions of different causes of death showed infection (33.2%), renal involvement (18.7%), lupus encephalopathy (13.8%), and cardiovascular disease (11.5%) as the top 4 causes. The overall survival rates for Chinese patients with SLE resembled previous publications in Asia-Pacific area. But the death causes of SLE in China were of some differences indicating relatively higher proportion of infection and lupus encephalopathy and lower cardiovascular disease. Ethnicity and more aggressive treatment might have contributed to the difference in death composition. PMID:25929930

CD147/EMMPRIN overexpression and prognosis in cancer: A systematic review and meta-analysis

PubMed Central

Xin, Xiaoyan; Zeng, Xianqin; Gu, Huajian; Li, Min; Tan, Huaming; Jin, Zhishan; Hua, Teng; Shi, Rui; Wang, Hongbo

2016-01-01

CD147/EMMPRIN (extracellular matrix metalloproteinase inducer) plays an important role in tumor progression and a number of studies have suggested that it is an indicator of tumor prognosis. This current meta-analysis systematically reevaluated the predictive potential of CD147/EMMPRIN in various cancers. We searched PubMed and Embase databases to screen the literature. Fixed-effect and random-effect meta-analytical techniques were used to correlate CD147 expression with outcome measures. A total of 53 studies that included 68 datasets were eligible for inclusion in the final analysis. We found a significant association between CD147/EMMPRIN overexpression and adverse tumor outcomes, such as overall survival, disease-specific survival, progression-free survival, metastasis-free survival or recurrence-free survival, irrespective of the model analysis. In addition, CD147/EMMPRIN overexpression predicted a high risk for chemotherapy drugs resistance. CD147/EMMPRIN is a central player in tumor progression and predicts a poor prognosis, including in patients who have received chemo-radiotherapy. Our results provide the evidence that CD147/EMMPRIN could be a potential therapeutic target for cancers. PMID:27608940

Diabetes mellitus and poorer prognosis in hepatocellular carcinoma: a systematic review and meta-analysis.

PubMed

Wang, Yan-Gang; Wang, Peng; Wang, Bin; Fu, Zheng-Ju; Zhao, Wen-Juan; Yan, Sheng-Li

2014-01-01

Previous studies suggested that diabetes mellitus was associated with cancer risk and prognosis, but studies investigating the relationship between diabetes mellitus and survival in patients with hepatocellular carcinoma (HCC) reported inconsistent findings. To derive a more precise estimate of the prognostic role of diabetes mellitus in HCC, we systematically reviewed published studies and carried out a meta-analysis. Eligible articles were identified in electronic databases from their inception through September 16, 2013. To evaluate the correlation between diabetes mellitus and prognosis in HCC, the pooled hazard ratios (HR) and their 95% confidence intervals (95% CI) for poorer overall and disease-free survivals were calculated by standard meta-analysis techniques with fixed-effects or random-effects models. 21 studies with a total of 9,767 HCC patients stratifying overall survival and/or disease-free survival in HCC patients by diabetes mellitus status were eligible for meta-analysis. 20 studies with a total of 9,727 HCC cases investigated the overall survival, and 10 studies with a total of 2,412 HCC patients investigated the disease-free survival. The pooled HRs for overall survival and disease-free survival were 1.46 (95% CI, 1.29 to 1.66; P<0.001) and 1.57 (95% CI, 1.21 to 2.05; P = 0.001), respectively. The adjusted HRs for overall survival and disease-free survival were 1.55 (95% CI, 1.27 to 1.91; P<0.001) and 2.15 (95% CI, 1.75 to 2.63; P<0.001), respectively. In addition, for patients receiving hepatic resection, diabetes mellitus was associated with both poorer overall survival and poorer disease-free survival, and for patients receiving non-surgical treatment or patients receiving radiofrequency ablation, diabetes mellitus was associated with poorer overall survival. There was no evidence for publication bias. Diabetes mellitus is independently associated with both poorer overall survival and poorer disease-free survival in HCC patients.

Socioeconomic deprivation and cancer survival in Germany: an ecological analysis in 200 districts in Germany.

PubMed

Jansen, Lina; Eberle, Andrea; Emrich, Katharina; Gondos, Adam; Holleczek, Bernd; Kajüter, Hiltraud; Maier, Werner; Nennecke, Alice; Pritzkuleit, Ron; Brenner, Hermann

2014-06-15

Although socioeconomic inequalities in cancer survival have been demonstrated both within and between countries, evidence on the variation of the inequalities over time past diagnosis is sparse. Furthermore, no comprehensive analysis of socioeconomic differences in cancer survival in Germany has been conducted. Therefore, we analyzed variations in cancer survival for patients diagnosed with one of the 25 most common cancer sites in 1997-2006 in ten population-based cancer registries in Germany (covering 32 million inhabitants). Patients were assigned a socioeconomic status according to the district of residence at diagnosis. Period analysis was used to derive 3-month, 5-year and conditional 1-year and 5-year age-standardized relative survival for 2002-2006 for each deprivation quintile in Germany. Relative survival of patients living in the most deprived district was compared to survival of patients living in all other districts by model-based period analysis. For 21 of 25 cancer sites, 5-year relative survival was lower in the most deprived districts than in all other districts combined. The median relative excess risk of death over the 25 cancer sites decreased from 1.24 in the first 3 months to 1.16 in the following 9 months to 1.08 in the following 4 years. Inequalities persisted after adjustment for stage. These major regional socioeconomic inequalities indicate a potential for improving cancer care and survival in Germany. Studies on individual-level patient data with access to treatment information should be conducted to examine the reasons for these socioeconomic inequalities in cancer survival in more detail. © 2013 UICC.

Survival rates and prognostic predictors of high grade brain stem gliomas in childhood: a systematic review and meta-analysis.

PubMed

Hassan, Hadeel; Pinches, Anne; Picton, Susan V; Phillips, Robert S

2017-10-01

Diagnosis of a pediatric high grade brain stem glioma is devastating with dismal outcomes. This systematic review and meta-analysis was undertaken to determine the survival rates and assess potential prognostic factors including selected interventions. Studies included involved pediatric participants with high grade brain stem gliomas diagnosed by magnetic resonance imaging or biopsy reporting overall survival rates. Meta-analysis was undertaken using a binomial random effects model. Sixty-five studies (2336 participants) were included. Meta-analysis showed 1 year overall survival (OS) of 41% (95% confidence interval (CI) 38-44%, I-sq 52%, 2083 participants), 2 year OS of 15.3% (95% confidence interval 12-20%, I-sq 73.1%, 1329 participants) and 3 year OS of 7.3% (95% confidence interval 5.2-10%, I-sq 26%, 584 participants). Meta-analyses of median overall survival results was not possible due to the lack of reported measures of variance. Subgroup analysis comparing date of study, classification of tumor, use of temozolomide, non-standard interventions or phase 1/2 versus other studies demonstrated no difference in survival outcomes. There was insufficient data to undertake subgroup meta-analysis of patient age, duration of symptoms, K27M histone mutations and AVCR1 mutations. Survival outcomes of high grade brain stem gliomas have remained very poor, and do not clearly vary according to classification, phase of study or use of different therapeutic interventions. Future studies should harmonize outcome and prognostic variable reporting to enable accurate meta-analysis and better exploration of prognosis.

Prognostic and survival analysis of presbyopia: The healthy twin study

NASA Astrophysics Data System (ADS)

Lira, Adiyani; Sung, Joohon

2015-12-01

Presbyopia, a vision condition in which the eye loses its flexibility to focus on near objects, is part of ageing process which mostly perceptible in the early or mid 40s. It is well known that age is its major risk factor, while sex, alcohol, poor nutrition, ocular and systemic diseases are known as common risk factors. However, many other variables might influence the prognosis. Therefore in this paper we developed a prognostic model to estimate survival from presbyopia. 1645 participants which part of the Healthy Twin Study, a prospective cohort study that has recruited Korean adult twins and their family members based on a nation-wide registry at public health agencies since 2005, were collected and analyzed by univariate analysis as well as Cox proportional hazard model to reveal the prognostic factors for presbyopia while survival curves were calculated by Kaplan-Meier method. Besides age, sex, diabetes, and myopia; the proposed model shows that education level (especially engineering program) also contribute to the occurrence of presbyopia as well. Generally, at 47 years old, the chance of getting presbyopia becomes higher with the survival probability is less than 50%. Furthermore, our study shows that by stratifying the survival curve, MZ has shorter survival with average onset time about 45.8 compare to DZ and siblings with 47.5 years old. By providing factors that have more effects and mainly associate with presbyopia, we expect that we could help to design an intervention to control or delay its onset time.

Hemodialysis and peritoneal dialysis are associated with similar outcomes for end-stage renal disease treatment in Canada.

PubMed

Yeates, Karen; Zhu, Naisu; Vonesh, Edward; Trpeski, Lilyanna; Blake, Peter; Fenton, Stanley

2012-09-01

There were 35 265 patients receiving renal replacement therapy in Canada at the end of 2007 with 11.0% of patients on peritoneal dialysis (PD) and 48.9% on hemodialysis (HD) and a remaining 40.1% living with a functioning kidney transplant. There are no contemporary studies examining PD survival relative to HD in Canada. The objective was to compare survival outcomes for incident patients starting on PD as compared to HD in Canada. Using data from the Canadian Organ Replacement Register, the Cox proportional hazards (PH) model was employed to study survival outcomes for patients initiating PD as compared to HD in Canada from 1991 to 2004 with follow-up to 31 December 2007. Comparisons of outcomes were made between three successive calendar periods: 1991-95, 1996-2000 and 2001-04 with the relative risk of death of incident patients calculated using an intent-to-treat (ITT) analysis with proportional and non-PH models using a piecewise exponential survival model to compare adjusted mortality rates. In the ITT analysis, overall survival for the entire study period favored PD in the first 18 months and HD after 36 months. However, for the 2001-04 cohort, survival favored PD for the first 2 years and thereafter PD and HD were similar. Among female patients > 65 years with diabetes, PD had a 27% higher mortality rate. Overall, HD and PD are associated with similar outcomes for end-stage renal disease treatment in Canada.

Adélie penguin survival: age structure, temporal variability and environmental influences.

PubMed

Emmerson, Louise; Southwell, Colin

2011-12-01

The driving factors of survival, a key demographic process, have been particularly challenging to study, especially for winter migratory species such as the Adélie penguin (Pygoscelis adeliae). While winter environmental conditions clearly influence Antarctic seabird survival, it has been unclear to which environmental features they are most likely to respond. Here, we examine the influence of environmental fluctuations, broad climatic conditions and the success of the breeding season prior to winter on annual survival of an Adélie penguin population using mark-recapture models based on penguin tag and resight data over a 16-year period. This analysis required an extension to the basic Cormack-Jolly-Seber model by incorporating age structure in recapture and survival sub-models. By including model covariates, we show that survival of older penguins is primarily related to the amount and concentration of ice present in their winter foraging grounds. In contrast, fledgling and yearling survival depended on other factors in addition to the physical marine environment and outcomes of the previous breeding season, but we were unable to determine what these were. The relationship between sea-ice and survival differed with penguin age: extensive ice during the return journey to breeding colonies was detrimental to survival for the younger penguins, whereas either too little or too much ice (between 15 and 80% cover) in the winter foraging grounds was detrimental for adults. Our results demonstrate that predictions of Adélie penguin survival can be improved by taking into account penguin age, prior breeding conditions and environmental features.

Use of a Survival Analysis Technique in Understanding Game Performance in Instructional Games. CRESST Report 812

ERIC Educational Resources Information Center

Kim, Jinok; Chung, Gregory K. W. K.

2012-01-01

In this study we compared the effects of two math game designs on math and game performance, using discrete-time survival analysis (DTSA) to model players' risk of not advancing to the next level in the game. 137 students were randomly assigned to two game conditions. The game covered the concept of a unit and the addition of like-sized fractional…

A new biologic prognostic model based on immunohistochemistry predicts survival in patients with diffuse large B-cell lymphoma.

PubMed

Perry, Anamarija M; Cardesa-Salzmann, Teresa M; Meyer, Paul N; Colomo, Luis; Smith, Lynette M; Fu, Kai; Greiner, Timothy C; Delabie, Jan; Gascoyne, Randy D; Rimsza, Lisa; Jaffe, Elaine S; Ott, German; Rosenwald, Andreas; Braziel, Rita M; Tubbs, Raymond; Cook, James R; Staudt, Louis M; Connors, Joseph M; Sehn, Laurie H; Vose, Julie M; López-Guillermo, Armando; Campo, Elias; Chan, Wing C; Weisenburger, Dennis D

2012-09-13

Biologic factors that predict the survival of patients with a diffuse large B-cell lymphoma, such as cell of origin and stromal signatures, have been discovered by gene expression profiling. We attempted to simulate these gene expression profiling findings and create a new biologic prognostic model based on immunohistochemistry. We studied 199 patients (125 in the training set, 74 in the validation set) with de novo diffuse large B-cell lymphoma treated with rituximab and CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone) or CHOP-like therapies, and immunohistochemical stains were performed on paraffin-embedded tissue microarrays. In the model, 1 point was awarded for each adverse prognostic factor: nongerminal center B cell-like subtype, SPARC (secreted protein, acidic, and rich in cysteine) < 5%, and microvascular density quartile 4. The model using these 3 biologic markers was highly predictive of overall survival and event-free survival in multivariate analysis after adjusting for the International Prognostic Index in both the training and validation sets. This new model delineates 2 groups of patients, 1 with a low biologic score (0-1) and good survival and the other with a high score (2-3) and poor survival. This new biologic prognostic model could be used with the International Prognostic Index to stratify patients for novel or risk-adapted therapies.

Bayesian Weibull tree models for survival analysis of clinico-genomic data

PubMed Central

Clarke, Jennifer; West, Mike

2008-01-01

An important goal of research involving gene expression data for outcome prediction is to establish the ability of genomic data to define clinically relevant risk factors. Recent studies have demonstrated that microarray data can successfully cluster patients into low- and high-risk categories. However, the need exists for models which examine how genomic predictors interact with existing clinical factors and provide personalized outcome predictions. We have developed clinico-genomic tree models for survival outcomes which use recursive partitioning to subdivide the current data set into homogeneous subgroups of patients, each with a specific Weibull survival distribution. These trees can provide personalized predictive distributions of the probability of survival for individuals of interest. Our strategy is to fit multiple models; within each model we adopt a prior on the Weibull scale parameter and update this prior via Empirical Bayes whenever the sample is split at a given node. The decision to split is based on a Bayes factor criterion. The resulting trees are weighted according to their relative likelihood values and predictions are made by averaging over models. In a pilot study of survival in advanced stage ovarian cancer we demonstrate that clinical and genomic data are complementary sources of information relevant to survival, and we use the exploratory nature of the trees to identify potential genomic biomarkers worthy of further study. PMID:18618012

Racial differences in colorectal cancer survival at a safety net hospital.

PubMed

Tapan, Umit; Lee, Shin Yin; Weinberg, Janice; Kolachalama, Vijaya B; Francis, Jean; Charlot, Marjory; Hartshorn, Kevan; Chitalia, Vipul

2017-08-01

While racial disparity in colorectal cancer survival have previously been studied, whether this disparity exists in patients with metastatic colorectal cancer receiving care at safety net hospitals (and therefore of similar socioeconomic status) is poorly understood. We examined racial differences in survival in a cohort of patients with stage IV colorectal cancer treated at the largest safety net hospital in the New England region, which serves a population with a majority (65%) of non-Caucasian patients. Data was extracted from the hospital's electronic medical record. Survival differences among different racial and ethnic groups were examined graphically using Kaplan-Meier analysis. A univariate cox proportional hazards model and a multivariable adjusted model were generated. Black patients had significantly lower overall survival compared to White patients, with median overall survival of 1.9 years and 2.5 years respectively. In a multivariate analysis, Black race posed a significant hazard (HR 1.70, CI 1.01-2.90, p=0.0467) for death. Though response to therapy emerged as a strong predictor of survival (HR=0.4, CI=0.2-0.7, p=0.0021), it was comparable between Blacks and Whites. Despite presumed equal access to healthcare and socioeconomic status within a safety-net hospital system, our results reinforce findings from previous studies showing lower colorectal cancer survival in Black patients, and also point to the importance of investigating other factors such as genetic and pathologic differences. Copyright © 2017 Elsevier Ltd. All rights reserved.

Using demography and movement behavior to predict range expansion of the southern sea otter.

USGS Publications Warehouse

Tinker, M.T.; Doak, D.F.; Estes, J.A.

2008-01-01

In addition to forecasting population growth, basic demographic data combined with movement data provide a means for predicting rates of range expansion. Quantitative models of range expansion have rarely been applied to large vertebrates, although such tools could be useful for restoration and management of many threatened but recovering populations. Using the southern sea otter (Enhydra lutris nereis) as a case study, we utilized integro-difference equations in combination with a stage-structured projection matrix that incorporated spatial variation in dispersal and demography to make forecasts of population recovery and range recolonization. In addition to these basic predictions, we emphasize how to make these modeling predictions useful in a management context through the inclusion of parameter uncertainty and sensitivity analysis. Our models resulted in hind-cast (1989–2003) predictions of net population growth and range expansion that closely matched observed patterns. We next made projections of future range expansion and population growth, incorporating uncertainty in all model parameters, and explored the sensitivity of model predictions to variation in spatially explicit survival and dispersal rates. The predicted rate of southward range expansion (median = 5.2 km/yr) was sensitive to both dispersal and survival rates; elasticity analysis indicated that changes in adult survival would have the greatest potential effect on the rate of range expansion, while perturbation analysis showed that variation in subadult dispersal contributed most to variance in model predictions. Variation in survival and dispersal of females at the south end of the range contributed most of the variance in predicted southward range expansion. Our approach provides guidance for the acquisition of further data and a means of forecasting the consequence of specific management actions. Similar methods could aid in the management of other recovering populations.

Cost-effectiveness analysis of treatments for metastatic pancreatic cancer based on PRODIGE and MPACT trials.

PubMed

Zhou, Jing; Zhao, Rongce; Wen, Feng; Zhang, Pengfei; Wu, Yifan; Tang, Ruilei; Chen, Hongdou; Zhang, Jian; Li, Qiu

2016-06-02

Fluorouracil, leucovorin, irinotecan, oxaliplatin (FOLFIRINOX) and gemcitabine plus nab-paclitaxel (GEM-N) have shown a significant survival benefit for the treatment of metastatic pancreatic cancer. The objective of this study was to assess the cost-effectiveness of FOLFIRINOX versus GEM-N for treating metastatic pancreatic cancer based on the PRODIGE and MPACT trials. A decision model was performed to compare FOLFIRINOX with GEM-N. Primary base case data were identified from PRODIGE and MPACT trials. Costs were estimated and incremental cost-effectiveness ratio (ICER) was calculated at West China Hospital, Sichuan University, China. Survival benefits were reported in quality-adjusted life-years (QALY). Finally, sensitive analysis was performed by varying potentially modifiable parameters in the model. The base-case analysis showed that FOLFIRINOX cost $37,203.75 and yielded a survival of 0.67 QALY, and GEM-N cost $32,080.59 and yielded a survival of 0.51 QALY in the entire treatment. Thus, the ICER of FOLFIRINOX versus GEM-N was $32,019.75 per QALY gained. The GEM-N regimen was more cost-effective compared with the FOLFIRINOX regimen for the treatment of metastatic pancreatic cancer from a Chinese perspective.

Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data.

PubMed

Tekwe, Carmen D; Carroll, Raymond J; Dabney, Alan R

2012-08-01

Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets. Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions. The testing procedures discussed in this article can all be performed using readily available software such as R. The R codes are provided as supplemental materials. ctekwe@stat.tamu.edu.

Cost-effectiveness of histamine receptor-2 antagonist versus proton pump inhibitor for stress ulcer prophylaxis in critically ill patients*.

PubMed

MacLaren, Robert; Campbell, Jon

2014-04-01

To examine the cost-effectiveness of using histamine receptor-2 antagonist or proton pump inhibitor for stress ulcer prophylaxis. Decision analysis model examining costs and effectiveness of using histamine receptor-2 antagonist or proton pump inhibitor for stress ulcer prophylaxis. Costs were expressed in 2012 U.S. dollars from the perspective of the institution and included drug regimens and the following outcomes: clinically significant stress-related mucosal bleed, ventilator-associated pneumonia, and Clostridium difficile infection. Effectiveness was the mortality risk associated with these outcomes and represented by survival. Costs, occurrence rates, and mortality probabilities were extracted from published data. A simulation model. A mixed adult ICU population. Histamine receptor-2 antagonist or proton pump inhibitor for 9 days of stress ulcer prophylaxis therapy. Output variables were expected costs, expected survival rates, incremental cost, and incremental survival rate. Univariate sensitivity analyses were conducted to determine the drivers of incremental cost and incremental survival. Probabilistic sensitivity analysis was conducted using second-order Monte Carlo simulation. For the base case analysis, the expected cost of providing stress ulcer prophylaxis was $6,707 with histamine receptor-2 antagonist and $7,802 with proton pump inhibitor, resulting in a cost saving of $1,095 with histamine receptor-2 antagonist. The associated mortality probabilities were 3.819% and 3.825%, respectively, resulting in an absolute survival benefit of 0.006% with histamine receptor-2 antagonist. The primary drivers of incremental cost and survival were the assumptions surrounding ventilator-associated pneumonia and bleed. The probabilities that histamine receptor-2 antagonist was less costly and provided favorable survival were 89.4% and 55.7%, respectively. A secondary analysis assuming equal rates of C. difficile infection showed a cost saving of $908 with histamine receptor-2 antagonists, but the survival benefit of 0.0167% favored proton pump inhibitors. Histamine receptor-2 antagonist therapy appears to reduce costs with survival benefit comparable to proton pump inhibitor therapy for stress ulcer prophylaxis. Ventilator-associated pneumonia and bleed are the variables most affecting these outcomes. The uncertainty in the findings justifies a prospective trial.

Supplemental materials for the analysis of capture-recapture data for polar bears in Western Hudson Bay, Canada, 1984-2004

USGS Publications Warehouse

Regehr, Eric V.; Lunn, Nicholas J.; Amstrup, Steven C.; Stirling, Ian

2007-01-01

Regehr and others (2007, Survival and population size of polar bears in western Hudson Bay in relation to earlier sea ice breakup: Journal of Wildlife Management, v. 71, no. 8) evaluated survival in relation to climatic conditions and estimated population size for polar bears (Ursus maritimus) in western Hudson Bay, Canada. Here, we provide supplemental materials for the analyses in Regehr and others (2007). We demonstrate how tag-return data from harvested polar bears were used to adjust estimates of total survival for human-caused mortality. We describe the sex and age composition of the capture and harvest samples and provide results for goodness-of-fit tests applied to capture-recapture models. We also describe the capture-recapture model selection procedure and the structure of the most supported model, which was used to estimate survival and population size.

WebDISCO: a web service for distributed cox model learning without patient-level data sharing.

PubMed

Lu, Chia-Lun; Wang, Shuang; Ji, Zhanglong; Wu, Yuan; Xiong, Li; Jiang, Xiaoqian; Ohno-Machado, Lucila

2015-11-01

The Cox proportional hazards model is a widely used method for analyzing survival data. To achieve sufficient statistical power in a survival analysis, it usually requires a large amount of data. Data sharing across institutions could be a potential workaround for providing this added power. The authors develop a web service for distributed Cox model learning (WebDISCO), which focuses on the proof-of-concept and algorithm development for federated survival analysis. The sensitive patient-level data can be processed locally and only the less-sensitive intermediate statistics are exchanged to build a global Cox model. Mathematical derivation shows that the proposed distributed algorithm is identical to the centralized Cox model. The authors evaluated the proposed framework at the University of California, San Diego (UCSD), Emory, and Duke. The experimental results show that both distributed and centralized models result in near-identical model coefficients with differences in the range [Formula: see text] to [Formula: see text]. The results confirm the mathematical derivation and show that the implementation of the distributed model can achieve the same results as the centralized implementation. The proposed method serves as a proof of concept, in which a publicly available dataset was used to evaluate the performance. The authors do not intend to suggest that this method can resolve policy and engineering issues related to the federated use of institutional data, but they should serve as evidence of the technical feasibility of the proposed approach.Conclusions WebDISCO (Web-based Distributed Cox Regression Model; https://webdisco.ucsd-dbmi.org:8443/cox/) provides a proof-of-concept web service that implements a distributed algorithm to conduct distributed survival analysis without sharing patient level data. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Sparse Group Penalized Integrative Analysis of Multiple Cancer Prognosis Datasets

PubMed Central

Liu, Jin; Huang, Jian; Xie, Yang; Ma, Shuangge

2014-01-01

SUMMARY In cancer research, high-throughput profiling studies have been extensively conducted, searching for markers associated with prognosis. Because of the “large d, small n” characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Recent studies have shown that integrative analysis, which simultaneously analyzes multiple datasets, can be more effective than single-dataset analysis and classic meta-analysis. In most of existing integrative analysis, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. Several approaches have been designed to reinforce this assumption. In practice, different datasets may differ in terms of patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. In this study, we assume the heterogeneity model, under which different datasets are allowed to have different sets of markers. With multiple cancer prognosis datasets, we adopt the AFT (accelerated failure time) model to describe survival. This model may have the lowest computational cost among popular semiparametric survival models. For marker selection, we adopt a sparse group MCP (minimax concave penalty) approach. This approach has an intuitive formulation and can be computed using an effective group coordinate descent algorithm. Simulation study shows that it outperforms the existing approaches under both the homogeneity and heterogeneity models. Data analysis further demonstrates the merit of heterogeneity model and proposed approach. PMID:23938111

Model for breast cancer survival: relative prognostic roles of axillary nodal status, TNM stage, estrogen receptor concentration, and tumor necrosis.

PubMed

Shek, L L; Godolphin, W

1988-10-01

The independent prognostic effects of certain clinical and pathological variables measured at the time of primary diagnosis were assessed with Cox multivariate regression analysis. The 859 patients with primary breast cancer, on which the proportional hazards model was based, had a median follow-up of 60 months. Axillary nodal status (categorized as N0, N1-3 or N4+) was the most significant and independent factor in overall survival, but inclusion of TNM stage, estrogen receptor (ER) concentration and tumor necrosis significantly improved survival predictions. Predictions made with the model showed striking subset survival differences within stage: 5-year survival from 36% (N4+, loge[ER] = 0, marked necrosis) to 96% (N0, loge[ER] = 6, no necrosis) in TNM I, and from 0 to 70% for the same categories in TNM IV. Results of the model were used to classify patients into four distinct risk groups according to a derived hazard index. An 8-fold variation in survival was seen with the highest (greater than 3) to lowest index values (less than 1). Each hazard index level included patients with varied combinations of the above factors, but could be considered to denote the same degree of risk of breast cancer mortality. A model with ER concentration, nodal status, and tumor necrosis was found to best predict survival after disease recurrence in 369 patients, thus confirming the enduring biological significance of these factors.

Protein profiles associated with survival in lung adenocarcinoma

PubMed Central

Chen, Guoan; Gharib, Tarek G; Wang, Hong; Huang, Chiang-Ching; Kuick, Rork; Thomas, Dafydd G.; Shedden, Kerby A.; Misek, David E.; Taylor, Jeremy M. G.; Giordano, Thomas J.; Kardia, Sharon L. R.; Iannettoni, Mark D.; Yee, John; Hogg, Philip J.; Orringer, Mark B.; Hanash, Samir M.; Beer, David G.

2003-01-01

Morphologic assessment of lung tumors is informative but insufficient to adequately predict patient outcome. We previously identified transcriptional profiles that predict patient survival, and here we identify proteins associated with patient survival in lung adenocarcinoma. A total of 682 individual protein spots were quantified in 90 lung adenocarcinomas by using quantitative two-dimensional polyacrylamide gel electrophoresis analysis. A leave-one-out cross-validation procedure using the top 20 survival-associated proteins identified by Cox modeling indicated that protein profiles as a whole can predict survival in stage I tumor patients (P = 0.01). Thirty-three of 46 survival-associated proteins were identified by using mass spectrometry. Expression of 12 candidate proteins was confirmed as tumor-derived with immunohistochemical analysis and tissue microarrays. Oligonucleotide microarray results from both the same tumors and from an independent study showed mRNAs associated with survival for 11 of 27 encoded genes. Combined analysis of protein and mRNA data revealed 11 components of the glycolysis pathway as associated with poor survival. Among these candidates, phosphoglycerate kinase 1 was associated with survival in the protein study, in both mRNA studies and in an independent validation set of 117 adenocarcinomas and squamous lung tumors using tissue microarrays. Elevated levels of phosphoglycerate kinase 1 in the serum were also significantly correlated with poor outcome in a validation set of 107 patients with lung adenocarcinomas using ELISA analysis. These studies identify new prognostic biomarkers and indicate that protein expression profiles can predict the outcome of patients with early-stage lung cancer. PMID:14573703

Development and Validation of a qRT-PCR Classifier for Lung Cancer Prognosis

PubMed Central

Chen, Guoan; Kim, Sinae; Taylor, Jeremy MG; Wang, Zhuwen; Lee, Oliver; Ramnath, Nithya; Reddy, Rishindra M; Lin, Jules; Chang, Andrew C; Orringer, Mark B; Beer, David G

2011-01-01

Purpose This prospective study aimed to develop a robust and clinically-applicable method to identify high-risk early stage lung cancer patients and then to validate this method for use in future translational studies. Patients and Methods Three published Affymetrix microarray data sets representing 680 primary tumors were used in the survival-related gene selection procedure using clustering, Cox model and random survival forest (RSF) analysis. A final set of 91 genes was selected and tested as a predictor of survival using a qRT-PCR-based assay utilizing an independent cohort of 101 lung adenocarcinomas. Results The RSF model built from 91 genes in the training set predicted patient survival in an independent cohort of 101 lung adenocarcinomas, with a prediction error rate of 26.6%. The mortality risk index (MRI) was significantly related to survival (Cox model p < 0.00001) and separated all patients into low, medium, and high-risk groups (HR = 1.00, 2.82, 4.42). The MRI was also related to survival in stage 1 patients (Cox model p = 0.001), separating patients into low, medium, and high-risk groups (HR = 1.00, 3.29, 3.77). Conclusions The development and validation of this robust qRT-PCR platform allows prediction of patient survival with early stage lung cancer. Utilization will now allow investigators to evaluate it prospectively by incorporation into new clinical trials with the goal of personalized treatment of lung cancer patients and improving patient survival. PMID:21792073

The outcome and survival of palliative surgery in thoraco-lumbar spinal metastases: contemporary retrospective cohort study.

PubMed

Nemelc, R M; Stadhouder, A; van Royen, B J; Jiya, T U

2014-11-01

Purpose: To evaluate outcome and survival and to identify prognostic variables for patients surgically treated for spinal metastases. Methods: A retrospective study was performed on 86 patients, surgically treated for spinal metastases. Preoperative analyses of the ASIA and spinal instability neoplastic scores (SINS) were performed. Survival curves of different prognostic variables were made by Kaplan–Meier analysis and the variables entered in a Cox proportional hazards model to determine their significance on survival. The correlation between preoperative radiotherapy and postoperative wound infections was also evaluated. Results: Survival analysis was performed on 81 patients,37 women and 44 men. Five patients were excluded due to missing data. Median overall survival was 38 weeks [95 % confidence interval (CI) 27.5–48.5 weeks], with a 3-month survival rate of 81.5 %. Breast tumor had the best median survival of 127 weeks and lung tumor the worst survival of 18 weeks. Univariate analysis showed tumor type, preoperative ASIA score (p = 0.01) and visceral metastases(p = 0.18) were significant prognostic variables for survival.Colon tumors had 5.53 times hazard ratio compared to patients with breast tumor. ASIA-C score had more than 13.03 times the hazard ratio compared to patients with an ASIA-E score. Retrospective analysis of the SINS scores showed 34 patients with a score of 13–18 points, 44 patients with a score of 7–12 points, and 1 patient with a score of 6 points. Preoperative radiotherapy had no influence on the postoperative incidence of deep surgical wound infections (p = 0.37). Patients with spinal metastases had a median survival of 38 weeks postoperative. The primary tumor type and ASIA score were significant prognostic factors for survival. Preoperative radiotherapy neither had influence on survival nor did it constitute a risk for postoperative surgical wound infections.

A 10-Year Retrospective Review of a Nonrandomized Cohort of 458 Patients Undergoing Radical Radiotherapy or Cystectomy in Yorkshire, UK

DOE Office of Scientific and Technical Information (OSTI.GOV)

Munro, Nicholas P., E-mail: nic@munron.plus.co; Sundaram, Subramnian K.; Weston, Philip

2010-05-01

Purpose: We have previously reported on the mortality, morbidity, and 5-year survival of 458 patients who underwent radical radiotherapy or surgery for invasive bladder cancer in Yorkshire from 1993 to 1996. We aim to present the 10-year outcomes of these patients and to reassess factors predicting survival. Methods and Materials: The Northern and Yorkshire Cancer Registry identified 458 patients whose cases were subjected to Kaplan-Meier all-cause survival analyses, and a retrospective casenote analysis was undertaken on 398 (87%) for univariate and multivariate Cox proportional hazards modeling. Additional proportional hazards regression modeling was used to assess the statistical significance of variablesmore » on overall survival. Results: The ratio of radiotherapy to cystectomy was 3:1. There was no significant difference in overall 10-year survival between those who underwent radiotherapy (22%) and radical cystectomy (24%). Univariate analyses suggested that female sex, performance status, hydronephrosis and clinical T stage, were associated with an inferior outcome at 10 years. Patient age, tumor grade, treatment delay, and caseload factors were not significant. Multivariate analysis models were created for 0-2 and 2-10 years after treatment. There were no significant differences in treatment for 0-2 years; however, after 2 years follow-up there was some evidence of increased survival for patients receiving surgery compared with radiotherapy (hazard ratio 0.66, 95% confidence interval: 0.44-1.01, p = 0.06). Conclusions: a 10-year minimum follow-up has rarely been reported after radical treatment for invasive bladder cancer. At 10 years, there was no statistical difference in all-cause survival between surgery and radiotherapy treatment modalities.« less

Analysis of the mechanism of nucleosome survival during transcription

PubMed Central

Chang, Han-Wen; Kulaeva, Olga I.; Shaytan, Alexey K.; Kibanov, Mikhail; Kuznedelov, Konstantin; Severinov, Konstantin V.; Kirpichnikov, Mikhail P.; Clark, David J.; Studitsky, Vasily M.

2014-01-01

Maintenance of nucleosomal structure in the cell nuclei is essential for cell viability, regulation of gene expression and normal aging. Our previous data identified a key intermediate (a small intranucleosomal DNA loop, Ø-loop) that is likely required for nucleosome survival during transcription by RNA polymerase II (Pol II) through chromatin, and suggested that strong nucleosomal pausing guarantees efficient nucleosome survival. To evaluate these predictions, we analysed transcription through a nucleosome by different, structurally related RNA polymerases and mutant yeast Pol II having different histone-interacting surfaces that presumably stabilize the Ø-loop. The height of the nucleosomal barrier to transcription and efficiency of nucleosome survival correlate with the net negative charges of the histone-interacting surfaces. Molecular modeling and analysis of Pol II-nucleosome intermediates by DNase I footprinting suggest that efficient Ø-loop formation and nucleosome survival are mediated by electrostatic interactions between the largest subunit of Pol II and core histones. PMID:24234452

Analysis of dam-passage survival of yearling and subyearling Chinook salmon and juvenile steelhead at The Dalles Dam, Oregon, 2010

USGS Publications Warehouse

Beeman, John W.; Kock, Tobias J.; Perry, Russell W.; Smith, Steven G.

2011-01-01

We performed a series of analyses of mark-recapture data from a study at The Dalles Dam during 2010 to determine if model assumptions for estimation of juvenile salmonid dam-passage survival were met and if results were similar to those using the University of Washington's newly developed ATLAS software. The study was conducted by the Pacific Northwest National Laboratory and used acoustic telemetry of yearling Chinook salmon, juvenile steelhead, and subyearling Chinook salmon released at three sites according to the new virtual/paired-release statistical model. This was the first field application of the new model, and the results were used to measure compliance with minimum survival standards set forth in a recent Biological Opinion. Our analyses indicated that most model assumptions were met. The fish groups mixed in time and space, and no euthanized tagged fish were detected. Estimates of reach-specific survival were similar in fish tagged by each of the six taggers during the spring, but not in the summer. Tagger effort was unevenly allocated temporally during tagging of subyearling Chinook salmon in the summer; the difference in survival estimates among taggers was more likely a result of a temporal trend in actual survival than of tagger effects. The reach-specific survival of fish released at the three sites was not equal in the reaches they had in common for juvenile steelhead or subyearling Chinook salmon, violating one model assumption. This violation did not affect the estimate of dam-passage survival, because data from the common reaches were not used in its calculation. Contrary to expectation, precision of survival estimates was not improved by using the most parsimonious model of recapture probabilities instead of the fully parameterized model. Adjusting survival estimates for differences in fish travel times and tag lives increased the dam-passage survival estimate for yearling Chinook salmon by 0.0001 and for juvenile steelhead by 0.0004. The estimate was unchanged for subyearling Chinook salmon. The tag-life-adjusted dam-passage survival estimates from our analyses were 0.9641 (standard error [SE] 0.0096) for yearling Chinook salmon, 0.9534 (SE 0.0097) for juvenile steelhead, and 0.9404 (SE 0.0091) for subyearling Chinook salmon. These were within 0.0001 of estimates made by the University of Washington using the ATLAS software. Contrary to the intent of the virtual/paired-release model to adjust estimates of the paired-release model downward in order to account for differential handling mortality rates between release groups, random variation in survival estimates may result in an upward adjustment of survival relative to estimates from the paired-release model. Further investigation of this property of the virtual/paired-release model likely would prove beneficial. In addition, we suggest that differential selective pressures near release sites of the two control groups could bias estimates of dam-passage survival from the virtual/paired-release model.

Survival pattern of first accident among commercial drivers in the Greater Accra Region of Ghana.

PubMed

Nanga, Salifu; Odai, Nii Afotey; Lotsi, Anani

2017-06-01

In this study, the average accident risk of commercial drivers in the Greater Accra region of Ghana and its associated risks were examined based on a survey data collected using paper-based questionnaires from 204 commercial drivers from the Greater Accra Region of Ghana. The Cox Proportional Hazards Model was used for multivariate analysis while the Kaplan-Meier (KM) Model was used to study the survival patterns of the commercial drivers. The study revealed that the median survival time for an accident to happen is 2.50 years. Good roads provided a better chance of survival than bad roads and experienced drivers have a better chance of survival than the inexperienced drivers. Age of driver, alcohol usage of driver, marital status, condition of road and duration of driver's license were found to be related to the risk of accident. Copyright © 2017 Elsevier Ltd. All rights reserved.

Survivability of Deterministic Dynamical Systems

PubMed Central

Hellmann, Frank; Schultz, Paul; Grabow, Carsten; Heitzig, Jobst; Kurths, Jürgen

2016-01-01

The notion of a part of phase space containing desired (or allowed) states of a dynamical system is important in a wide range of complex systems research. It has been called the safe operating space, the viability kernel or the sunny region. In this paper we define the notion of survivability: Given a random initial condition, what is the likelihood that the transient behaviour of a deterministic system does not leave a region of desirable states. We demonstrate the utility of this novel stability measure by considering models from climate science, neuronal networks and power grids. We also show that a semi-analytic lower bound for the survivability of linear systems allows a numerically very efficient survivability analysis in realistic models of power grids. Our numerical and semi-analytic work underlines that the type of stability measured by survivability is not captured by common asymptotic stability measures. PMID:27405955

Blast Fragmentation Modeling and Analysis

DTIC Science & Technology

2010-10-31

weapons device containing a multiphase blast explosive (MBX). 1. INTRODUCTION The ARL Survivability Lethality and Analysis Directorate (SLAD) is...velocity. In order to simulate the highly complex phenomenon, the exploding cylinder is modeled with the hydrodynamics code ALE3D , an arbitrary...Lagrangian-Eulerian multiphysics code, developed at Lawrence Livermore National Laboratory. ALE3D includes physical properties, constitutive models for

Comparison of Survival Models for Analyzing Prognostic Factors in Gastric Cancer Patients

PubMed

Habibi, Danial; Rafiei, Mohammad; Chehrei, Ali; Shayan, Zahra; Tafaqodi, Soheil

2018-03-27

Objective: There are a number of models for determining risk factors for survival of patients with gastric cancer. This study was conducted to select the model showing the best fit with available data. Methods: Cox regression and parametric models (Exponential, Weibull, Gompertz, Log normal, Log logistic and Generalized Gamma) were utilized in unadjusted and adjusted forms to detect factors influencing mortality of patients. Comparisons were made with Akaike Information Criterion (AIC) by using STATA 13 and R 3.1.3 softwares. Results: The results of this study indicated that all parametric models outperform the Cox regression model. The Log normal, Log logistic and Generalized Gamma provided the best performance in terms of AIC values (179.2, 179.4 and 181.1, respectively). On unadjusted analysis, the results of the Cox regression and parametric models indicated stage, grade, largest diameter of metastatic nest, largest diameter of LM, number of involved lymph nodes and the largest ratio of metastatic nests to lymph nodes, to be variables influencing the survival of patients with gastric cancer. On adjusted analysis, according to the best model (log normal), grade was found as the significant variable. Conclusion: The results suggested that all parametric models outperform the Cox model. The log normal model provides the best fit and is a good substitute for Cox regression. Creative Commons Attribution License

Estimating and modeling the cure fraction in population-based cancer survival analysis.

PubMed

Lambert, Paul C; Thompson, John R; Weston, Claire L; Dickman, Paul W

2007-07-01

In population-based cancer studies, cure is said to occur when the mortality (hazard) rate in the diseased group of individuals returns to the same level as that expected in the general population. The cure fraction (the proportion of patients cured of disease) is of interest to patients and is a useful measure to monitor trends in survival of curable disease. There are 2 main types of cure fraction model, the mixture cure fraction model and the non-mixture cure fraction model, with most previous work concentrating on the mixture cure fraction model. In this paper, we extend the parametric non-mixture cure fraction model to incorporate background mortality, thus providing estimates of the cure fraction in population-based cancer studies. We compare the estimates of relative survival and the cure fraction between the 2 types of model and also investigate the importance of modeling the ancillary parameters in the selected parametric distribution for both types of model.

The effect of delayed graft function on graft and patient survival in kidney transplantation: an approach using competing events analysis.

PubMed

Fonseca, Isabel; Teixeira, Laetitia; Malheiro, Jorge; Martins, La Salete; Dias, Leonídio; Castro Henriques, António; Mendonça, Denisa

2015-06-01

In kidney transplantation, the impact of delayed graft function (DGF) on long-term graft and patient survival is controversial. We examined the impact of DGF on graft and recipient survival by accounting for the possibility that death with graft function may act as a competing risk for allograft failure. We used data from 1281 adult primary deceased-donor kidney recipients whose allografts functioned at least 1 year. The probability of graft loss occurrence is overestimated using the complement of Kaplan-Meier estimates (1-KM). Both the cause-specific Cox proportional hazard regression model (standard Cox) and the subdistribution hazard regression model proposed by Fine and Gray showed that DGF was associated with shorter time to graft failure (csHR = 2.0, P = 0.002; sHR = 1.57, P = 0.009), independent of acute rejection (AR) and after adjusting for traditional factors associated with graft failure. Regarding patient survival, DGF was a predictor of patient death using the cause-specific Cox model (csHR = 1.57, P = 0.029) but not using the subdistribution model. The probability of graft loss from competing end points should not be reported with the 1-KM. Application of a regression model for subdistribution hazard showed that, independent of AR, DGF has a detrimental effect on long-term graft survival, but not on patient survival. © 2015 Steunstichting ESOT.

Design/Analysis of the JWST ISIM Bonded Joints for Survivability at Cryogenic Temperatures

NASA Technical Reports Server (NTRS)

Bartoszyk, Andrew; Johnston, John; Kaprielian, Charles; Kuhn, Jonathan; Kunt, Cengiz; Rodini, Benjamin; Young, Daniel

2005-01-01

Contents include the following: JWST/ISIM introduction. Design and analysis challenges for ISIM bonded joints. JWST/ISIM joint designs. Bonded joint analysis. Finite element modeling. Failure criteria and margin calculation. Analysis/test correlation procedure. Example of test data and analysis.

Cougar survival and source-sink structure on Greater Yellowstone's Northern Range

USGS Publications Warehouse

Ruth, T.K.; Haroldson, M.A.; Murphy, K.M.; Buotte, P.C.; Hornocker, M.G.; Quigley, H.B.

2011-01-01

We studied survival and causes of mortality of radiocollared cougars (Puma concolor) on the Greater Yellowstone Northern Range (GYNR) prior to (1987–1994) and after wolf (Canis lupus) reintroduction (1998–2005) and evaluated temporal, spatial, and environmental factors that explain variation in adult, subadult, and kitten survival. Using Program MARK and multimodel inference, we modeled cougar survival based on demographic status, season, and landscape attributes. Our best models for adult and independent subadults indicated that females survived better than males and survival increased with age until cougars reached older ages. Lower elevations and increasing density of roads, particularly in areas open to cougar hunting north of Yellowstone National Park (YNP), increased mortality risks for cougars on the GYNR. Indices of ungulate biomass, cougar and wolf population size, winter severity, rainfall, and individual characteristics such as the presence of dependent young, age class, and use of Park or Wilderness were not important predictors of survival. Kitten survival increased with age, was lower during winter, increased with increasing minimum estimates of elk calf biomass, and increased with increasing density of adult male cougars. Using our best model, we mapped adult cougar survival on the GYNR landscape. Results of receiver operating characteristic (ROC) analysis indicated a good model fit for both female (area under the curve [AUC] = 0.81, 95%CI = 0.70–0.92, n = 35 locations) and male cougars (AUC = 0.84, 95%CI = 0.74–0.94, n = 49 locations) relative to hunter harvest locations in our study area. Using minimum estimates of survival necessary to sustain the study population, we developed a source-sink surface and we identify several measures that resource management agencies can take to enhance cougar population management based on a source-sink strategy.

Increased flexibility for modeling telemetry and nest-survival data using the multistate framework

USGS Publications Warehouse

Devineau, Olivier; Kendall, William L.; Doherty, Paul F.; Shenk, Tanya M.; White, Gary C.; Lukacs, Paul M.; Burnham, Kenneth P.

2014-01-01

Although telemetry is one of the most common tools used in the study of wildlife, advances in the analysis of telemetry data have lagged compared to progress in the development of telemetry devices. We demonstrate how standard known-fate telemetry and related nest-survival data analysis models are special cases of the more general multistate framework. We present a short theoretical development, and 2 case examples regarding the American black duck and the mallard. We also present a more complex lynx data analysis. Although not necessary in all situations, the multistate framework provides additional flexibility to analyze telemetry data, which may help analysts and biologists better deal with the vagaries of real-world data collection.

Addressing issues associated with evaluating prediction models for survival endpoints based on the concordance statistic.

PubMed

Wang, Ming; Long, Qi

2016-09-01

Prediction models for disease risk and prognosis play an important role in biomedical research, and evaluating their predictive accuracy in the presence of censored data is of substantial interest. The standard concordance (c) statistic has been extended to provide a summary measure of predictive accuracy for survival models. Motivated by a prostate cancer study, we address several issues associated with evaluating survival prediction models based on c-statistic with a focus on estimators using the technique of inverse probability of censoring weighting (IPCW). Compared to the existing work, we provide complete results on the asymptotic properties of the IPCW estimators under the assumption of coarsening at random (CAR), and propose a sensitivity analysis under the mechanism of noncoarsening at random (NCAR). In addition, we extend the IPCW approach as well as the sensitivity analysis to high-dimensional settings. The predictive accuracy of prediction models for cancer recurrence after prostatectomy is assessed by applying the proposed approaches. We find that the estimated predictive accuracy for the models in consideration is sensitive to NCAR assumption, and thus identify the best predictive model. Finally, we further evaluate the performance of the proposed methods in both settings of low-dimensional and high-dimensional data under CAR and NCAR through simulations. © 2016, The International Biometric Society.

Spatially explicit dynamic N-mixture models

USGS Publications Warehouse

Zhao, Qing; Royle, Andy; Boomer, G. Scott

2017-01-01

Knowledge of demographic parameters such as survival, reproduction, emigration, and immigration is essential to understand metapopulation dynamics. Traditionally the estimation of these demographic parameters requires intensive data from marked animals. The development of dynamic N-mixture models makes it possible to estimate demographic parameters from count data of unmarked animals, but the original dynamic N-mixture model does not distinguish emigration and immigration from survival and reproduction, limiting its ability to explain important metapopulation processes such as movement among local populations. In this study we developed a spatially explicit dynamic N-mixture model that estimates survival, reproduction, emigration, local population size, and detection probability from count data under the assumption that movement only occurs among adjacent habitat patches. Simulation studies showed that the inference of our model depends on detection probability, local population size, and the implementation of robust sampling design. Our model provides reliable estimates of survival, reproduction, and emigration when detection probability is high, regardless of local population size or the type of sampling design. When detection probability is low, however, our model only provides reliable estimates of survival, reproduction, and emigration when local population size is moderate to high and robust sampling design is used. A sensitivity analysis showed that our model is robust against the violation of the assumption that movement only occurs among adjacent habitat patches, suggesting wide applications of this model. Our model can be used to improve our understanding of metapopulation dynamics based on count data that are relatively easy to collect in many systems.

Serum CA125 predicts extrauterine disease and survival in uterine carcinosarcoma

PubMed Central

Huang, Gloria S.; Chiu, Lydia G.; Gebb, Juliana S.; Gunter, Marc J.; Sukumvanich, Paniti; Goldberg, Gary L.; Einstein, Mark H.

2009-01-01

Objective The purpose of this study was to determine the clinical utility of CA125 measurement in patients with uterine carcinosarcoma (CS). Methods Ninety-five consecutive patients treated for CS at a single institution were identified. All 54 patients who underwent preoperative CA125 measurement were included in the study. Data were abstracted from the medical records. Tests of association between preoperative CA125 and previously identified clinicopathologic prognostic factors were performed using Fisher’s exact test and Pearson chi-square test. To evaluate relationship of CA125 elevation and survival, a Cox proportional hazard model was used for multivariate analysis, incorporating all of prognostic factors identified by univariate analysis. Results Preoperative CA125 was significantly associated with the presence of extrauterine disease (P<0.001), deep myometrial invasion (P<0.001), and serous histology of the epithelial component (P=0.005). Using univariate survival analysis, stage (HR=1.808, P=0.004), postoperative CA125 level (HR=9.855, P<0.001), and estrogen receptor positivity (HR=0.314, P=0.029) were significantly associated with survival. In the multivariate model, only postoperative CA125 level remained significantly associated with poor survival (HR=5.725, P=0.009). Conclusion Preoperative CA125 elevation is a marker of extrauterine disease and deep myometrial invasion in patients with uterine CS. Postoperative CA125 elevation is an independent prognostic factor for poor survival. These findings indicate that CA125 may be a clinically useful serum marker in the management of patients with CS. PMID:17935762

The efficacy of chimeric antigen receptor (CAR) immunotherapy in animal models for solid tumors: A systematic review and meta-analysis.

PubMed

Wu, Yingcheng; Xu, Ran; Jia, Keren; Shi, Hui

2017-01-01

Most recently, an emerging theme in the field of tumor immunology predominates: chimeric antigen receptor (CAR) therapy in treating solid tumors. The number of related preclinical trials was surging. However, an evaluation of the effects of preclinical studies remained absent. Hence, a meta-analysis was conducted on the efficacy of CAR in animal models for solid tumors. The authors searched PubMed/Medline, Embase, and Google scholar up to April 2017. HR for survival was extracted based on the survival curve. The authors used fixed effect models to combine the results of all the trials. Heterogeneity was assessed by I-square statistic. Quality assessment was conducted following the Stroke Therapy Academic Industry Roundtable standard. Publication bias was assessed using Egger's test. Eleven trials were included, including 54 experiments with a total of 362 animals involved. CAR immunotherapy significantly improved the survival of animals (HR: 0.25, 95% CI: 0.13-0.37, P < 0.001). The quality assessment revealed that no study reported whether allocation concealment and blinded outcome assessment were conducted, and only five studies implemented randomization. This meta-analysis indicated that CAR therapy may be a potential clinical strategy in treating solid tumors.

A gene expression signature associated with survival in metastatic melanoma

PubMed Central

Mandruzzato, Susanna; Callegaro, Andrea; Turcatel, Gianluca; Francescato, Samuela; Montesco, Maria C; Chiarion-Sileni, Vanna; Mocellin, Simone; Rossi, Carlo R; Bicciato, Silvio; Wang, Ena; Marincola, Francesco M; Zanovello, Paola

2006-01-01

Background Current clinical and histopathological criteria used to define the prognosis of melanoma patients are inadequate for accurate prediction of clinical outcome. We investigated whether genome screening by means of high-throughput gene microarray might provide clinically useful information on patient survival. Methods Forty-three tumor tissues from 38 patients with stage III and stage IV melanoma were profiled with a 17,500 element cDNA microarray. Expression data were analyzed using significance analysis of microarrays (SAM) to identify genes associated with patient survival, and supervised principal components (SPC) to determine survival prediction. Results SAM analysis revealed a set of 80 probes, corresponding to 70 genes, associated with survival, i.e. 45 probes characterizing longer and 35 shorter survival times, respectively. These transcripts were included in a survival prediction model designed using SPC and cross-validation which allowed identifying 30 predicting probes out of the 80 associated with survival. Conclusion The longer-survival group of genes included those expressed in immune cells, both innate and acquired, confirming the interplay between immunological mechanisms and the natural history of melanoma. Genes linked to immune cells were totally lacking in the poor-survival group, which was instead associated with a number of genes related to highly proliferative and invasive tumor cells. PMID:17129373

Cost-Effectiveness of Pembrolizumab Versus Ipilimumab in Ipilimumab-Naïve Patients with Advanced Melanoma in the United States.

PubMed

Wang, Jingshu; Chmielowski, Bartosz; Pellissier, James; Xu, Ruifeng; Stevinson, Kendall; Liu, Frank Xiaoqing

2017-02-01

Recent clinical trials have shown that pembrolizumab significantly prolonged progression-free survival and overall survival compared with ipilimumab in ipilimumab-naïve patients with unresectable or metastatic melanoma. However, there has been no published evidence on the cost-effectiveness of pembrolizumab for this indication. To assess the long-term cost-effectiveness of pembrolizumab versus ipilimumab in ipilimumab-naïve patients with unresectable or meta-static melanoma from a U.S. integrated health system perspective. A partitioned-survival model was developed, which divided overall survival time into progression-free survival and postprogression survival. The model used Kaplan-Meier estimates of progression-free survival and overall survival from a recent randomized phase 3 study (KEYNOTE-006) that compared pembrolizumab and ipilimumab. Extrapolation of progression-free survival and overall survival beyond the clinical trial was based on parametric functions and literature data. The base-case time horizon was 20 years, and costs and health outcomes were discounted at a rate of 3% per year. Clinical data-including progression-free survival and overall survival data spanning a median follow-up time of 15 months, as well as quality of life and adverse event data from the ongoing KEYNOTE-006 trial-and cost data from public sources were used to populate the model. Costs included those of drug acquisition, treatment administration, adverse event management, and disease management of advanced melanoma. The incremental cost-effectiveness ratio (ICER) expressed as cost difference per quality-adjusted life-year (QALY) gained was the main outcome, and a series of sensitivity analyses were performed to test the robustness of the results. In the base case, pembrolizumab was projected to increase the life expectancy of U.S. patients with advanced melanoma by 1.14 years, corresponding to a gain of 0.79 discounted QALYs over ipilimumab. The model also projected an average increase of $63,680 in discounted perpatient costs of treatment with pembrolizumab versus ipilimumab. The corresponding ICER was $81,091 per QALY ($68,712 per life-year) over a 20-year time horizon. With $100,000 per QALY as the threshold, when input parameters were varied in deterministic one-way sensitivity analyses, the use of pembrolizumab was cost-effective relative to ipilimumab in most ranges. Further, in a comprehensive probabilistic sensitivity analysis, the ICER was cost-effective in 83% of the simulations. Compared with ipilimumab, pembrolizumab had higher expected QALYs and was cost-effective for the treatment of patients with unresectable or metastatic melanoma from a U.S. integrated health system perspective. This study was supported by funding from Merck & Co., which reviewed and approved the manuscript before journal submission. Wang, Pellissier, Xu, Stevinson, and Liu are employees of, and own stock in, Merck & Co. Chmielowski has served as a paid consultant for Merck & Co. and received a consultant fee for clinical input in connection with this study. Chmielowski also reports receiving advisory board and speaker bureau fees from multiple major pharmaceutical companies. Wang led the modeling and writing of the manuscript. Chmielowski, Xu, Stevinson, and Pellissier contributed substantially to the modeling design and methodology. Liu led the data collection work and contributed substantially to writing the manuscript. In conducting the analysis and writing the manuscript, the authors followed Merck publication polices and the "cost-effectiveness analysis alongside clinical trials-good research practices and the CHEERS reporting format as recommended by the International Society for Pharmacoeconomics and Outcomes Research.

Modeling survival of juvenile salmon during downriver migration in the Columbia River on a microcomputer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Peloquin, R.A.; McKenzie, D.H.

1994-10-01

A compartmental model has been implemented on a microcomputer as an aid in the analysis of alternative solutions to a problem. The model, entitled Smolt Survival Simulator, simulates the survival of juvenile salmon during their downstream migration and passage of hydroelectric dams in the Columbia River. The model is designed to function in a workshop environment where resource managers and fisheries biologists can study alternative measures that may potentially increase juvenile anadromous fish survival during downriver migration. The potential application of the model has placed several requirements on the implementing software. It must be available for use in workshop settings.more » The software must be easily to use with minimal computer knowledge. Scenarios must be created and executed quickly and efficiently. Results must be immediately available. Software design emphasis vas placed on the user interface because of these requirements. The discussion focuses on methods used in the development of the SSS software user interface. These methods should reduce user stress and alloy thorough and easy parameter modification.« less

[Analysis of histoprognostic factors for the non metastatic rectal cancer in a west Algerian series of 58 cases].

PubMed

Mesli, Smain Nabil; Regagba, Derbali; Tidjane, Anisse; Benkalfat, Mokhtar; Abi-Ayad, Chakib

2016-01-01

The aim of our study was to analyze histoprognostic factors in patients with non-metastatic rectal cancer operated at the division of surgery "A" in Tlemcen, west Algeria, over a period of six years. Retrospective study of 58 patients with rectal adenocarcinoma. Evaluation criterion was survival. Parameters studied were sex, age, tumor stage, tumor recurrence. The average age was 58 years, 52% of men and 48% of women, with sex-ratio (1,08). Tumor seat was: middle rectum 41.37%, lower rectum 34.48% and upper rectum 24.13%. Concerning TNM clinical staging, patients were classified as stage I (17.65%), stage II (18.61%), stage III (53.44%) and stage IV (7.84%). Median overall survival was 40 months ±2,937 months. Survival based on tumor staging: stage III and IV had a lower 3 years survival rate (19%) versus stage I, II which had a survival rate of 75% (P = 0.000) (95%). Patients with tumor recurrences had a lower 3 years survival rate compared to those who had no tumoral recurrences (30.85% vs 64.30% P = 0.043). In this series, univariate analysis of prognostic factors affecting survival allowed to retain only three factors influencing survival: tumor size, stage and tumor recurrences. In multivariate analysis using Cox's model only one factor was retained: tumor recurrence.

Prematurity and fetal lung response after tracheal occlusion in fetuses with severe congenital diaphragmatic hernia.

PubMed

Sananes, Nicolas; Rodo, Carlota; Peiro, Jose Luis; Britto, Ingrid Schwach Werneck; Sangi-Haghpeykar, Haleh; Favre, Romain; Joal, Arnaud; Gaudineau, Adrien; Silva, Marcos Marques da; Tannuri, Uenis; Zugaib, Marcelo; Carreras, Elena; Ruano, Rodrigo

2016-09-01

To evaluate the independent association of fetal pulmonary response and prematurity to postnatal outcomes after fetal tracheal occlusion for congenital diaphragmatic hernia. Fetal pulmonary response, prematurity (<37 weeks at delivery) and extreme prematurity (<32 weeks at delivery) were evaluated and compared between survivors and non-survivors at 6 months of life. Multivariable analysis was conducted with generalized linear mixed models for variables significantly associated with survival in univariate analysis. Eighty-four infants were included, of whom 40 survived (47.6%) and 44 died (52.4%). Univariate analysis demonstrated that survival was associated with greater lung response (p=0.006), and the absence of extreme preterm delivery (p=0.044). In multivariable analysis, greater pulmonary response after FETO was an independent predictor of survival (aOR 1.87, 95% CI 1.08-3.33, p=0.023), whereas the presence of extreme prematurity was not statistically associated with mortality after controlling for fetal pulmonary response (aOR 0.52, 95% CI 0.12-2.30, p=0.367). Fetal pulmonary response after FETO is the most important factor associated with survival, independently from the gestational age at delivery.

Clinicopathologic characteristics associated with long-term survival in advanced epithelial ovarian cancer: an NRG Oncology/Gynecologic Oncology Group ancillary data study.

PubMed

Hamilton, C A; Miller, A; Casablanca, Y; Horowitz, N S; Rungruang, B; Krivak, T C; Richard, S D; Rodriguez, N; Birrer, M J; Backes, F J; Geller, M A; Quinn, M; Goodheart, M J; Mutch, D G; Kavanagh, J J; Maxwell, G L; Bookman, M A

2018-02-01

To identify clinicopathologic factors associated with 10-year overall survival in epithelial ovarian cancer (EOC) and primary peritoneal cancer (PPC), and to develop a predictive model identifying long-term survivors. Demographic, surgical, and clinicopathologic data were abstracted from GOG 182 records. The association between clinical variables and long-term survival (LTS) (>10years) was assessed using multivariable regression analysis. Bootstrap methods were used to develop predictive models from known prognostic clinical factors and predictive accuracy was quantified using optimism-adjusted area under the receiver operating characteristic curve (AUC). The analysis dataset included 3010 evaluable patients, of whom 195 survived greater than ten years. These patients were more likely to have better performance status, endometrioid histology, stage III (rather than stage IV) disease, absence of ascites, less extensive preoperative disease distribution, microscopic disease residual following cyoreduction (R0), and decreased complexity of surgery (p<0.01). Multivariable regression analysis revealed that lower CA-125 levels, absence of ascites, stage, and R0 were significant independent predictors of LTS. A predictive model created using these variables had an AUC=0.729, which outperformed any of the individual predictors. The absence of ascites, a low CA-125, stage, and R0 at the time of cytoreduction are factors associated with LTS when controlling for other confounders. An extensively annotated clinicopathologic prediction model for LTS fell short of clinical utility suggesting that prognostic molecular profiles are needed to better predict which patients are likely to be long-term survivors. Published by Elsevier Inc.

Clinicopathologic characteristics associated with long-term survival in advanced epithelial ovarian cancer: an NRG Oncology/Gynecologic Oncology Group ancillary data study

PubMed Central

Hamilton, C. A.; Miller, A.; Casablanca, Y.; Horowitz, N. S.; Rungruang, B.; Krivak, T. C.; Richard, S. D.; Rodriguez, N.; Birrer, M.J.; Backes, F.J.; Geller, M.A.; Quinn, M.; Goodheart, M.J.; Mutch, D.G.; Kavanagh, J.J.; Maxwell, G. L.; Bookman, M. A.

2018-01-01

Objective To identify clinicopathologic factors associated with 10-year overall survival in epithelial ovarian cancer (EOC) and primary peritoneal cancer (PPC), and to develop a predictive model identifying long-term survivors. Methods Demographic, surgical, and clinicopathologic data were abstracted from GOG 182 records. The association between clinical variables and long-term survival (LTS) (>10 years) was assessed using multivariable regression analysis. Bootstrap methods were used to develop predictive models from known prognostic clinical factors and predictive accuracy was quantified using optimism-adjusted area under the receiver operating characteristic curve (AUC). Results The analysis dataset included 3,010 evaluable patients, of whom 195 survived greater than ten years. These patients were more likely to have better performance status, endometrioid histology, stage III (rather than stage IV) disease, absence of ascites, less extensive preoperative disease distribution, microscopic disease residual following cyoreduction (R0), and decreased complexity of surgery (p<0.01). Multivariable regression analysis revealed that lower CA-125 levels, absence of ascites, stage, and R0 were significant independent predictors of LTS. A predictive model created using these variables had an AUC=0.729, which outperformed any of the individual predictors. Conclusions The absence of ascites, a low CA-125, stage, and R0 at the time of cytoreduction are factors associated with LTS when controlling for other confounders. An extensively annotated clinicopathologic prediction model for LTS fell short of clinical utility suggesting that prognostic molecular profiles are needed to better predict which patients are likely to be long-term survivors. PMID:29195926

Prognostic Model for Resected Squamous Cell Lung Cancer: External Multicenter Validation and Propensity Score Analysis exploring the Impact of Adjuvant and Neoadjuvant Treatment.

PubMed

Pilotto, Sara; Sperduti, Isabella; Leuzzi, Giovanni; Chiappetta, Marco; Mucilli, Felice; Ratto, Giovanni Battista; Lococo, Filippo; Filosso, Pier Lugigi; Spaggiari, Lorenzo; Novello, Silvia; Milella, Michele; Santo, Antonio; Scarpa, Aldo; Infante, Maurizio; Tortora, Giampaolo; Facciolo, Francesco; Bria, Emilio

2018-04-01

We developed one of the first clinicopathological prognostic nomograms for resected squamous cell lung cancer (SQLC). Herein, we validate the model in a larger multicenter cohort and we explore the impact of adjuvant and neoadjuvant treatment (ANT). Patients with resected SQLC from January 2002 to December 2012 in six institutions were eligible. Each patient was assigned a prognostic score based on the clinicopathological factors included in the model (age, T descriptor according to seventh edition of the TNM classification, lymph node status, and grading). Kaplan-Meier analysis for disease-free survival, cancer-specific survival (CSS), and overall survival was performed according to a three-class risk model. Harrell's C-statistics were adopted for model validation. The effect of ANT was adjusted with propensity score. Data on 1375 patients were gathered (median age, 68 years; male sex, 86.8%; T descriptor 1 or 2 versus 3 or 4, 71.7% versus 24.9%; nodes negative versus positive, 53.4% versus 46.6%; and grading of 1 or 2 versus 3, 35.0% versus 41.1%). Data for survival analysis were available for 1097 patients. With a median follow-up of 55 months, patients at low risk had a significantly longer disease-free survival than did patients at intermediate risk (hazard ratio [HR] = 1.67, 95% confidence interval [CI]: 1.40-2.01) and patients at high risk (HR = 2.46, 95% CI: 1.90-3.19); they also had a significantly longer CSS (HR = 2.46, 95% CI: 1.80-3.36 versus HR = 4.30, 95% CI: 2.92-6.33) and overall survival (HR = 1.79, 95% CI: 1.48-2.17 versus HR = 2.33, 95% CI: 1.76-3.07). A trend in favor of ANT was observed for intermediate-risk/high-risk patients, particularly for CSS (p = 0.06 [5-year CSS 72.7% versus 60.8%]). A model based on a combination of easily available clinicopathological factors effectively stratifies patients with resected SQLC into three risk classes. Copyright © 2017 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Clinical outcome and predictors of survival in patients with pneumocystis jirovecii pneumonia--results of a tertiary referral centre.

PubMed

Roembke, Felicitas; Heinzow, Hauke Sebastian; Gosseling, Thomas; Heinecke, Achim; Domagk, Dirk; Domschke, Wolfram; Meister, Tobias

2014-01-01

Pneumocystis jirovecii pneumonia also known as pneumocystis pneumonia (PCP) is an opportunistic respiratory infection in human immunodeficiency virus (HIV) patients that may also develop in non-HIV immunocompromised persons. The aim of our study was to evaluate mortality predictors of PCP patients in a tertiary referral centre. Fifty-one patients with symptomatic PCP were enrolled in the study. The patients had either HIV infection (n = 21) or other immunosuppressive conditions (n = 30). Baseline characteristics (e.g. age, sex and underlying disease) were retrieved. Kaplan-Meier analysis was employed to calculate survival. Comparisons were made by log-rank test. A multivariate analysis of factors influencing survival was carried out using the Cox regression model. Chi-squared test and Wilcoxon-Mann-Whitney test was applied as appropriate. The median survival time for the HIV group was >120 months compared with 3 months for the non-HIV group (P = 0.009). Three-month survival probability was also significantly greater in the HIV group compared with the non-HIV group (90% vs 41%, P = 0.002). In univariate log-rank test, intensive care unit (ICU) necessity, HIV negativity, age >50 years, haemoglobin <10g/dl, C-reactive protein >5 mg/dL and multiple comorbidities were significant negative predictors of survival. In the Cox regression model, ICU and HIV statuses turned out to be independent prognostic factors of survival. PCP is a serious problem in non-HIV immunocompromised patients in whom survival outcomes are worse than those in HIV patients. © 2013 John Wiley & Sons Ltd.

Evaluation of clinical and histopathologic prognostic factors for survival in canine osteosarcoma of the extracranial flat and irregular bones.

PubMed

Kruse, M A; Holmes, E S; Balko, J A; Fernandez, S; Brown, D C; Goldschmidt, M H

2013-07-01

Osteosarcoma is the most common bone tumor in dogs. However, current literature focuses primarily on appendicular osteosarcoma. This study examined the prognostic value of histological and clinical factors in flat and irregular bone osteosarcomas and hypothesized that clinical factors would have a significant association with survival time while histological factors would not. All osteosarcoma biopsy samples of the vertebra, rib, sternum, scapula, or pelvis were reviewed while survival information and clinical data were obtained from medical records, veterinarians, and owners. Forty-six dogs were included in the analysis of histopathological variables and 27 dogs with complete clinical data were included in the analysis of clinical variables. In the histopathologic cox regression model, there was no significant association between any histologic feature of osteosarcoma, including grade, and survival time. In the clinical cox regression model, there was a significant association between the location of the tumor and survival time as well as between the percent elevation of alkaline phosphatase (ALP) above normal and survival time. Controlling for ALP elevation, dogs with osteosarcoma located in the scapula had a significantly greater hazard for death (2.8) compared to dogs with tumors in other locations. Controlling for tumor location, every 100% increase in ALP from normal increased the hazard for death by 1.7. For canine osteosarcomas of the flat and irregular bones, histopathological features, including grade do not appear to be rigorous predictors of survival. Clinical variables such as increased ALP levels and tumor location in the scapula were associated with decreased survival times.

Statin use and kidney cancer outcomes: A propensity score analysis.

PubMed

Nayan, Madhur; Finelli, Antonio; Jewett, Michael A S; Juurlink, David N; Austin, Peter C; Kulkarni, Girish S; Hamilton, Robert J

2016-11-01

Studies evaluating the association between statin use and survival outcomes in renal cell carcinoma have demonstrated conflicting results. Our objective was to evaluate this association in a large clinical cohort by using propensity score methods to reduce confounding from measured covariates. We performed a retrospective review of 893 patients undergoing nephrectomy for unilateral, M0 renal cell carcinoma between 2000 and 2014 at a tertiary academic center. Inverse probability of treatment weights were derived from a propensity score model based on clinical, surgical, and pathological characteristics. We used Cox proportional hazard models to evaluate the association between statin use and disease-free survival, cancer-specific survival, and overall survival in the sample weighted by the inverse probability of treatment weights. A secondary analysis was performed matching statin users 1:1 to statin nonusers on the propensity score. Of the 893 patients, 259 (29%) were on statins at the time of surgery. Median follow-up was 47 months (interquartile range: 20-80). Statin use was not significantly associated with disease-free survival (hazard ratio [HR] = 1.09, 95% CI: 0.65-1.81), cancer-specific survival (HR = 0.90, 95% CI: 0.40-2.01), or overall survival (HR = 0.89, 95% CI: 0.55-1.44). Similar results were observed when using propensity score matching. The present study found no significant association between statin use and kidney cancer outcomes. Population-based studies are needed to further evaluate the role of statins in kidney cancer therapy. Copyright © 2016 Elsevier Inc. All rights reserved.

A model of survival following pre-hospital cardiac arrest based on the Victorian Ambulance Cardiac Arrest Register.

PubMed

Fridman, Masha; Barnes, Vanessa; Whyman, Andrew; Currell, Alex; Bernard, Stephen; Walker, Tony; Smith, Karen L

2007-11-01

This study describes the epidemiology of sudden cardiac arrest patients in Victoria, Australia, as captured via the Victorian Ambulance Cardiac Arrest Register (VACAR). We used the VACAR data to construct a new model of out-of-hospital cardiac arrest (OHCA), which was specified in accordance with observed trends. All cases of cardiac arrest in Victoria that were attended by Victorian ambulance services during the period of 2002-2005. Overall survival to hospital discharge was 3.8% among 18,827 cases of OHCA. Survival was 15.7% among 1726 bystander witnessed, adult cardiac arrests of presumed cardiac aetiology, presenting in ventricular fibrillation or ventricular tachycardia (VF/VT), where resuscitation was attempted. In multivariate logistic regression analysis, bystander CPR, cardiac arrest (CA) location, response time, age and sex were predictors of VF/VT, which, in turn, was a strong predictor of survival. The same factors that affected VF/VT made an additional contribution to survival. However, for bystander CPR, CA location and response time this additional contribution was limited to VF/VT patients only. There was no detectable association between survival and age younger than 60 years or response time over 15min. The new model accounts for relationships among predictors of survival. These relationships indicate that interventions such as reduced response times and bystander CPR act in multiple ways to improve survival.

Long-Term Survival Prediction for Coronary Artery Bypass Grafting: Validation of the ASCERT Model Compared With The Society of Thoracic Surgeons Predicted Risk of Mortality.

PubMed

Lancaster, Timothy S; Schill, Matthew R; Greenberg, Jason W; Ruaengsri, Chawannuch; Schuessler, Richard B; Lawton, Jennifer S; Maniar, Hersh S; Pasque, Michael K; Moon, Marc R; Damiano, Ralph J; Melby, Spencer J

2018-05-01

The recently developed American College of Cardiology Foundation-Society of Thoracic Surgeons (STS) Collaboration on the Comparative Effectiveness of Revascularization Strategy (ASCERT) Long-Term Survival Probability Calculator is a valuable addition to existing short-term risk-prediction tools for cardiac surgical procedures but has yet to be externally validated. Institutional data of 654 patients aged 65 years or older undergoing isolated coronary artery bypass grafting between 2005 and 2010 were reviewed. Predicted survival probabilities were calculated using the ASCERT model. Survival data were collected using the Social Security Death Index and institutional medical records. Model calibration and discrimination were assessed for the overall sample and for risk-stratified subgroups based on (1) ASCERT 7-year survival probability and (2) the predicted risk of mortality (PROM) from the STS Short-Term Risk Calculator. Logistic regression analysis was performed to evaluate additional perioperative variables contributing to death. Overall survival was 92.1% (569 of 597) at 1 year and 50.5% (164 of 325) at 7 years. Calibration assessment found no significant differences between predicted and actual survival curves for the overall sample or for the risk-stratified subgroups, whether stratified by predicted 7-year survival or by PROM. Discriminative performance was comparable between the ASCERT and PROM models for 7-year survival prediction (p < 0.001 for both; C-statistic = 0.815 for ASCERT and 0.781 for PROM). Prolonged ventilation, stroke, and hospital length of stay were also predictive of long-term death. The ASCERT survival probability calculator was externally validated for prediction of long-term survival after coronary artery bypass grafting in all risk groups. The widely used STS PROM performed comparably as a predictor of long-term survival. Both tools provide important information for preoperative decision making and patient counseling about potential outcomes after coronary artery bypass grafting. Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Comparison of listing strategies for allosensitized heart transplant candidates requiring transplant at high urgency: a decision model analysis.

PubMed

Feingold, B; Webber, S A; Bryce, C L; Park, S Y; Tomko, H E; Comer, D M; Mahle, W T; Smith, K J

2015-02-01

Allosensitized children who require a negative prospective crossmatch have a high risk of death awaiting heart transplantation. Accepting the first suitable organ offer, regardless of the possibility of a positive crossmatch, would improve waitlist outcomes but it is unclear whether it would result in improved survival at all times after listing, including posttransplant. We created a Markov decision model to compare survival after listing with a requirement for a negative prospective donor cell crossmatch (WAIT) versus acceptance of the first suitable offer (TAKE). Model parameters were derived from registry data on status 1A (highest urgency) pediatric heart transplant listings. We assumed no possibility of a positive crossmatch in the WAIT strategy and a base-case probability of a positive crossmatch in the TAKE strategy of 47%, as estimated from cohort data. Under base-case assumptions, TAKE showed an incremental survival benefit of 1.4 years over WAIT. In multiple sensitivity analyses, including variation of the probability of a positive crossmatch from 10% to 100%, TAKE was consistently favored. While model input data were less well suited to comparing survival when awaiting transplantation across a negative virtual crossmatch, our analysis suggests that taking the first suitable organ offer under these circumstances is also favored. © Copyright 2015 The American Society of Transplantation and the American Society of Transplant Surgeons.

Survival analysis: comparing peritoneal dialysis and hemodialysis in Taiwan.

PubMed

Huang, Chiu-Ching; Cheng, Kuang-Fu; Wu, Hong-Dar Isaac

2008-06-01

Comparisons of survival in patients on peritoneal dialysis (PD) and on hemodialysis (HD) have been conducted in many Western countries, but publications on this subject in Asian populations are scarce. The present study estimated the survival and the relative mortality hazard for HD and PD patients in Taiwan. Incident end-stage renal disease patients reported to the Taiwan Renal Registry during 1995 - 2002 were included in the study. Patients had to be 20 years of age or older and had to have survived for the first 90 days on dialysis. A total of 45,820 incident HD and 2,809 incident PD patients formed the study population. Patients on PD were treated mainly with traditional glucose-based solutions. Using an intent-to-treat analysis, the Cox proportional hazards (CPH) model was applied to identify the factors that predict survival by treatment modality. Subgroup analyses were conducted by stratifying patients according to sex, comorbidity, age, and diabetes status. Kaplan-Meier estimates were used to explore the survival of HD and PD patients. Adjustments were implemented using the CPH model. The overall 1-year, 2-year, 3-year, 5-year and 10-year survival rates for PD patients were 89.8%, 77.6%, 67.6%, 55.5%, and 35% respectively. The equivalent survival rates for HD patients were 87.5%, 76.6%, 68.1%, 54.3%, and 33.8%. The differences were not statistically significant (p = 0.125). The CPH analysis stratified by diabetes status and age revealed that PD patients 55 years of age or younger and nondiabetic had a lower mortality ratio (MR) of 0.94. But the MR increased to 1.31 for nondiabetic patients older than 55. The MR for PD versus HD further increased to 1.72 for diabetic patients 55 years of age or younger, and to 1.99 for diabetic patients older than 55. After adjusting for both demographic and clinical case-mix differences, PD and HD patients were observed to have similar long-term survival. Subgroup analyses revealed that, among diabetic patients and patients older than 55, those on HD experienced better survival than did those on PD.

Prognostic significance of hemoglobin level in patients with congestive heart failure and normal ejection fraction.

PubMed

Varadarajan, Padmini; Gandhi, Siddharth; Sharma, Sanjay; Umakanthan, Branavan; Pai, Ramdas G

2006-10-01

Previous studies have shown low hemoglobin (Hb) to have an adverse effect on survival in patients with congestive heart failure (CHF) and reduced left ventricular (LV) ejection fraction (EF); but its effect on survival in patients with CHF and normal EF is not known. This study sought to determine whether low Hb has an effect on survival in patients with both CHF and normal EF. Detailed chart reviews were performed by medical residents on 2,246 patients (48% with normal EF) with a discharge diagnosis of CHF in a large tertiary care hospital from 1990 to 1999. The CHF diagnosis was validated using the Framingham criteria. Mortality data were obtained from the National Death Index. Survival analysis was performed using Kaplan-Meier and Cox regression models. By Kaplan-Meier analysis, low Hb (< 12 gm/dl) compared with normal hemoglobin was associated with a lower 5-year survival in patients with CHF and both normal (38 vs. 50%, p = 0.0008) and reduced (35 vs. 48%, p = 0.0009) EF. Using the Cox regression model, low Hb was an independent predictor of mortality after adjusting for age, gender, renal dysfunction, diabetes mellitus, hypertension, and EF in both groups of patients. Low Hb has an independent adverse effect on survival in patients with CHF and both normal and reduced EF in both groups of patients.

Conditional Survival in Anal Carcinoma Using the National Population-Based Survey of Epidemiology and End Results Database (1988-2012).

PubMed

Kim, Ellen; Kim, Jong S; Choi, Mehee; Thomas, Charles R

2016-04-01

Conditional survival can provide valuable information for both patients and healthcare providers about the changing prognosis in surviving patients over time. This study estimated conditional survival for patients with anal cancer in the United States through analysis of a national population-based cancer registry. Log-rank test identified significant covariates of cause-specific survival (defined as time from diagnosis until death from anal cancer). Significant covariates were considered in the multivariable regression of cause-specific survival using Cox proportional hazards models. Covariates included cancer stage and demographic variables. Patients in Surveillance, Epidemiology, and End Results regions diagnosed with anal squamous cell carcinoma as their first and only cancer diagnosis from 1988 to 2012 were selected from this database, and 5145 patients were included in the retrospective cohort study. Five-year conditional survival stratified by each variable in the final Cox models was measured : The final multivariable models of overall and cause-specific survivals included stage, grade, sex, age, race, and relationship status. Over the first 6 years after diagnosis, conditional survival of distant stage increased from 37% to 89%, whereas regional stage increased from 65% to 93% and localized stage increased from 84% to 96%. The other variables had increasing prognosis as well, but the subgroups increased at a more similar rate over time. The data source used does not include information on chemotherapy treatment, patient comorbidities, or socioeconomic status. Conditional survival showed improvement over time. Patients with advanced stage had the greatest improvement in conditional survival. This is the first study to provide specific conditional survival probabilities for patients with anal cancer.

Impact of the number of mutations in survival and response outcomes to hypomethylating agents in patients with myelodysplastic syndromes or myelodysplastic/myeloproliferative neoplasms

PubMed Central

Montalban-Bravo, Guillermo; Takahashi, Koichi; Patel, Keyur; Wang, Feng; Xingzhi, Song; Nogueras, Graciela M.; Huang, Xuelin; Pierola, Ana Alfonso; Jabbour, Elias; Colla, Simona; Gañan-Gomez, Irene; Borthakur, Gautham; Daver, Naval; Estrov, Zeev; Kadia, Tapan; Pemmaraju, Naveen; Ravandi, Farhad; Bueso-Ramos, Carlos; Chamseddine, Ali; Konopleva, Marina; Zhang, Jianhua; Kantarjian, Hagop; Futreal, Andrew; Garcia-Manero, Guillermo

2018-01-01

The prognostic and predictive value of sequencing analysis in myelodysplastic syndromes (MDS) has not been fully integrated into clinical practice. We performed whole exome sequencing (WES) of bone marrow samples from 83 patients with MDS and 31 with MDS/MPN identifying 218 driver mutations in 31 genes in 98 (86%) patients. A total of 65 (57%) patients received therapy with hypomethylating agents. By univariate analysis, mutations in BCOR, STAG2, TP53 and SF3B1 significantly influenced survival. Increased number of mutations (≥ 3), but not clonal heterogeneity, predicted for shorter survival and LFS. Presence of 3 or more mutations also predicted for lower likelihood of response (26 vs 50%, p = 0.055), and shorter response duration (3.6 vs 26.5 months, p = 0.022). By multivariate analysis, TP53 mutations (HR 3.1, CI 1.3–7.5, p = 0.011) and number of mutations (≥ 3) (HR 2.5, CI 1.3–4.8, p = 0.005) predicted for shorter survival. A novel prognostic model integrating this mutation data with IPSS-R separated patients into three categories with median survival of not reached, 29 months and 12 months respectively (p < 0.001) and increased stratification potential, compared to IPSS-R, in patients with high/very-high IPSS-R. This model was validated in a separate cohort of 413 patients with untreated MDS. Although the use of WES did not provide significant more information than that obtained with targeted sequencing, our findings indicate that increased number of mutations is an independent prognostic factor in MDS and that mutation data can add value to clinical prognostic models. PMID:29515765

Nomogram for predicting the benefit of neoadjuvant chemoradiotherapy for patients with esophageal cancer: a SEER-Medicare analysis.

PubMed

Eil, Robert; Diggs, Brian S; Wang, Samuel J; Dolan, James P; Hunter, John G; Thomas, Charles R

2014-02-15

The survival impact of neoadjuvant chemoradiotherapy (CRT) on esophageal cancer remains difficult to establish for specific patients. The aim of the current study was to create a Web-based prediction tool providing individualized survival projections based on tumor and treatment data. Patients diagnosed with esophageal cancer between 1997 and 2005 were selected from the Surveillance, Epidemiology, and End Results (SEER)-Medicare database. The covariates analyzed were sex, T and N classification, histology, total number of lymph nodes examined, and treatment with esophagectomy or CRT followed by esophagectomy. After propensity score weighting, a log-logistic regression model for overall survival was selected based on the Akaike information criterion. A total of 824 patients with esophageal cancer who were treated with esophagectomy or trimodal therapy met the selection criteria. On multivariate analysis, age, sex, T and N classification, number of lymph nodes examined, treatment, and histology were found to be significantly associated with overall survival and were included in the regression analysis. Preoperative staging data and final surgical margin status were not available within the SEER-Medicare data set and therefore were not included. The model predicted that patients with T4 or lymph node disease benefitted from CRT. The internally validated concordance index was 0.72. The SEER-Medicare database of patients with esophageal cancer can be used to produce a survival prediction tool that: 1) serves as a counseling and decision aid to patients and 2) assists in risk modeling. Patients with T4 or lymph node disease appeared to benefit from CRT. This nomogram may underestimate the benefit of CRT due to its variable downstaging effect on pathologic stage. It is available at skynet.ohsu.edu/nomograms. © 2013 American Cancer Society.

Impact of the number of mutations in survival and response outcomes to hypomethylating agents in patients with myelodysplastic syndromes or myelodysplastic/myeloproliferative neoplasms.

PubMed

Montalban-Bravo, Guillermo; Takahashi, Koichi; Patel, Keyur; Wang, Feng; Xingzhi, Song; Nogueras, Graciela M; Huang, Xuelin; Pierola, Ana Alfonso; Jabbour, Elias; Colla, Simona; Gañan-Gomez, Irene; Borthakur, Gautham; Daver, Naval; Estrov, Zeev; Kadia, Tapan; Pemmaraju, Naveen; Ravandi, Farhad; Bueso-Ramos, Carlos; Chamseddine, Ali; Konopleva, Marina; Zhang, Jianhua; Kantarjian, Hagop; Futreal, Andrew; Garcia-Manero, Guillermo

2018-02-09

The prognostic and predictive value of sequencing analysis in myelodysplastic syndromes (MDS) has not been fully integrated into clinical practice. We performed whole exome sequencing (WES) of bone marrow samples from 83 patients with MDS and 31 with MDS/MPN identifying 218 driver mutations in 31 genes in 98 (86%) patients. A total of 65 (57%) patients received therapy with hypomethylating agents. By univariate analysis, mutations in BCOR, STAG2, TP53 and SF3B1 significantly influenced survival. Increased number of mutations (≥ 3), but not clonal heterogeneity, predicted for shorter survival and LFS. Presence of 3 or more mutations also predicted for lower likelihood of response (26 vs 50%, p = 0.055), and shorter response duration (3.6 vs 26.5 months, p = 0.022). By multivariate analysis, TP53 mutations (HR 3.1, CI 1.3-7.5, p = 0.011) and number of mutations (≥ 3) (HR 2.5, CI 1.3-4.8, p = 0.005) predicted for shorter survival. A novel prognostic model integrating this mutation data with IPSS-R separated patients into three categories with median survival of not reached, 29 months and 12 months respectively ( p < 0.001) and increased stratification potential, compared to IPSS-R, in patients with high/very-high IPSS-R. This model was validated in a separate cohort of 413 patients with untreated MDS. Although the use of WES did not provide significant more information than that obtained with targeted sequencing, our findings indicate that increased number of mutations is an independent prognostic factor in MDS and that mutation data can add value to clinical prognostic models.

Pseudobulbar affect as a negative prognostic indicator in amyotrophic lateral sclerosis.

PubMed

Tortelli, R; Arcuti, S; Copetti, M; Barone, R; Zecca, C; Capozzo, R; Barulli, M R; Simone, I L; Logroscino, G

2018-07-01

To evaluate whether the presence of pseudobulbar affect (PBA) in an early stage of the disease influences survival in a population-based incident cohort of amyotrophic lateral sclerosis (ALS). Incident ALS cases, diagnosed according to El Escorial criteria, were enrolled from a prospective population-based registry in Puglia, Southern Italy. The Center for Neurologic Study-Lability Scale (CNS-LS), a self-administered questionnaire, was used to evaluate PBA. Total scores range from 7 to 35. A score ≥13 was used to identify PBA. Cox proportional hazard models were used for survival analysis. The modified C-statistic for censored survival data was used for models' discrimination. RECursive Partitioning and AMalgamation (RECPAM) analysis was used to identify subgroups of patients with different patterns of risk, depending on baseline characteristics. We enrolled 94 sporadic ALS, median age of 64 years (range: 26-80). At the censoring date, 65 of 94 (69.2%), 39 of 60 (65.0%), and 26 of 34 (76.5%) patients reached the outcome (tracheotomy/death), in the whole, non-PBA and in the PBA groups, respectively. Kaplan-Meier survival curves for the two subgroups were not significantly different (log-rank test: 1.3, P = .25). The discrimination ability of a multivariable model with demographic and clinical variables of interest was not improved by adding PBA. In the RECPAM analysis, ALSFRSr and the total score of CNS-LS scale (

Economic evaluation of nivolumab for the treatment of second-line advanced squamous NSCLC in Canada: a comparison of modeling approaches to estimate and extrapolate survival outcomes.

PubMed

Goeree, Ron; Villeneuve, Julie; Goeree, Jeff; Penrod, John R; Orsini, Lucinda; Tahami Monfared, Amir Abbas

2016-06-01

Background Lung cancer is the most common type of cancer in the world and is associated with significant mortality. Nivolumab demonstrated statistically significant improvements in progression-free survival (PFS) and overall survival (OS) for patients with advanced squamous non-small cell lung cancer (NSCLC) who were previously treated. The cost-effectiveness of nivolumab has not been assessed in Canada. A contentious component of projecting long-term cost and outcomes in cancer relates to the modeling approach adopted, with the two most common approaches being partitioned survival (PS) and Markov models. The objectives of this analysis were to estimate the cost-utility of nivolumab and to compare the results using these alternative modeling approaches. Methods Both PS and Markov models were developed using docetaxel and erlotinib as comparators. A three-health state model was used consisting of progression-free, progressed disease, and death. Disease progression and time to progression were estimated by identifying best-fitting survival curves from the clinical trial data for PFS and OS. Expected costs and health outcomes were calculated by combining health-state occupancy with medical resource use and quality-of-life assigned to each of the three health states. The health outcomes included in the model were survival and quality-adjusted-life-years (QALYs). Results Nivolumab was found to have the highest expected per-patient cost, but also improved per-patient life years (LYs) and QALYs. Nivolumab cost an additional $151,560 and $140,601 per QALY gained compared to docetaxel and erlotinib, respectively, using a PS model approach. The cost-utility estimates using a Markov model were very similar ($152,229 and $141,838, respectively, per QALY gained). Conclusions Nivolumab was found to involve a trade-off between improved patient survival and QALYs, and increased cost. It was found that the use of a PS or Markov model produced very similar estimates of expected cost, outcomes, and incremental cost-utility.

Lamb survival analysis from birth to weaning in Iranian Kermani sheep.

PubMed

Barazandeh, Arsalan; Moghbeli, Sadrollah Molaei; Vatankhah, Mahmood; Hossein-Zadeh, Navid Ghavi

2012-04-01

Survival records from 1,763 Kermani lambs born between 1996 and 2004 from 294 ewes and 81 rams were used to determine genetic and non-genetic factors affecting lamb survival. Traits included were lamb survival across five periods from birth to 7, 14, 56, 70, and 90 days of age. Traits were analyzed under Weibull proportional hazard sire models. Several binary analyses were also conducted using animal models. Statistical models included the fixed class effects of sex of lamb, month and year of birth, a covariate effect of birth weight, and random genetic effects of both sire (in survival analyses) and animal (in binary analyses). The average survival to 90 days of age was 94.8%. Hazard rates ranged from 1.00 (birth to 90 days of age) to 1.73 (birth to 7 days of age) between the two sexes indicating that male lambs were at higher risk of mortality than females (P < 0.01). This study also revealed a curvilinear relationship between lamb survival and lamb birth weight, suggesting that viability and birth weight could be considered simultaneously in the selection programs to obtain optimal birth weight in Kermani lambs. Estimates of heritabilities from survival analyses were medium and ranged from 0.23 to 0.29. In addition, heritability estimates obtained from binary analyses were low and varied from 0.04 to 0.09. The results of this study suggest that progress in survival traits could be possible through managerial strategies and genetic selection.

Survival modeling for the estimation of transition probabilities in model-based economic evaluations in the absence of individual patient data: a tutorial.

PubMed

Diaby, Vakaramoko; Adunlin, Georges; Montero, Alberto J

2014-02-01

Survival modeling techniques are increasingly being used as part of decision modeling for health economic evaluations. As many models are available, it is imperative for interested readers to know about the steps in selecting and using the most suitable ones. The objective of this paper is to propose a tutorial for the application of appropriate survival modeling techniques to estimate transition probabilities, for use in model-based economic evaluations, in the absence of individual patient data (IPD). An illustration of the use of the tutorial is provided based on the final progression-free survival (PFS) analysis of the BOLERO-2 trial in metastatic breast cancer (mBC). An algorithm was adopted from Guyot and colleagues, and was then run in the statistical package R to reconstruct IPD, based on the final PFS analysis of the BOLERO-2 trial. It should be emphasized that the reconstructed IPD represent an approximation of the original data. Afterwards, we fitted parametric models to the reconstructed IPD in the statistical package Stata. Both statistical and graphical tests were conducted to verify the relative and absolute validity of the findings. Finally, the equations for transition probabilities were derived using the general equation for transition probabilities used in model-based economic evaluations, and the parameters were estimated from fitted distributions. The results of the application of the tutorial suggest that the log-logistic model best fits the reconstructed data from the latest published Kaplan-Meier (KM) curves of the BOLERO-2 trial. Results from the regression analyses were confirmed graphically. An equation for transition probabilities was obtained for each arm of the BOLERO-2 trial. In this paper, a tutorial was proposed and used to estimate the transition probabilities for model-based economic evaluation, based on the results of the final PFS analysis of the BOLERO-2 trial in mBC. The results of our study can serve as a basis for any model (Markov) that needs the parameterization of transition probabilities, and only has summary KM plots available.

Stochastic modeling and experimental analysis of phenotypic switching and survival of cancer cells under stress

NASA Astrophysics Data System (ADS)

Zamani Dahaj, Seyed Alireza; Kumar, Niraj; Sundaram, Bala; Celli, Jonathan; Kulkarni, Rahul

The phenotypic heterogeneity of cancer cells is critical to their survival under stress. A significant contribution to heterogeneity of cancer calls derives from the epithelial-mesenchymal transition (EMT), a conserved cellular program that is crucial for embryonic development. Several studies have investigated the role of EMT in growth of early stage tumors into invasive malignancies. Also, EMT has been closely associated with the acquisition of chemoresistance properties in cancer cells. Motivated by these studies, we analyze multi-phenotype stochastic models of the evolution of cancers cell populations under stress. We derive analytical results for time-dependent probability distributions that provide insights into the competing rates underlying phenotypic switching (e.g. during EMT) and the corresponding survival of cancer cells. Experimentally, we evaluate these model-based predictions by imaging human pancreatic cancer cell lines grown with and without cytotoxic agents and measure growth kinetics, survival, morphological changes and (terminal evaluation of) biomarkers with associated epithelial and mesenchymal phenotypes. The results derived suggest approaches for distinguishing between adaptation and selection scenarios for survival in the presence of external stresses.

Survival and breeding advantages of larger Black Brant (Branta bernicla nigricans) goslings: Within- and among-cohort variation

USGS Publications Warehouse

Sedinger, J.S.; Chelgren, N.D.

2007-01-01

We examined the relationship between mass late in the first summer and survival and return to the natal breeding colony for 12 cohorts (1986-1997) of female Black Brant (Branta bernicla nigricans). We used Cormack-Jolly-Seber methods and the program MARK to analyze capture-recapture data. Models included two kinds of residuals from regressions of mass on days after peak of hatch when goslings were measured; one based on the entire sample (12 cohorts) and the other based only on individuals in the same cohort. Some models contained date of peak of hatch (a group covariate related to lateness of nesting in that year) and mean cohort residual mass. Finally, models allowed survival to vary among cohorts. The best model of encounter probability included an effect of residual mass on encounter probability and allowed encounter probability to vary among age classes and across years. All competitive models contained an effect of one of the estimates of residual mass; relatively larger goslings survived their first year at higher rates. Goslings in cohorts from later years in the analysis tended to have lower first-year survival, after controlling for residual mass, which reflected the generally smaller mean masses for these cohorts but was potentially also a result of population-density effects additional to those on growth. Variation among cohorts in mean mass accounted for 56% of variation among cohorts in first-year survival. Encounter probabilities, which were correlated with breeding probability, increased with relative mass, which suggests that larger goslings not only survived at higher rates but also bred at higher rates. Although our findings support the well-established linkage between gosling mass and fitness, they suggest that additional environmental factors also influence first-year survival.

Low Survival Rates of Oral and Oropharyngeal Squamous Cell Carcinoma

PubMed Central

da Silva Júnior, Francisco Feliciano; dos Santos, Karine de Cássia Batista; Ferreira, Stefania Jeronimo

2017-01-01

Aim To assess the epidemiological and clinical factors that influence the prognosis of oral and oropharyngeal squamous cell carcinoma (SCC). Methods One hundred and twenty-one cases of oral and oropharyngeal SCC were selected. The survival curves for each variable were estimated using the Kaplan-Meier method. The Cox regression model was applied to assess the effect of the variables on survival. Results Cancers at an advanced stage were observed in 103 patients (85.1%). Cancers on the tongue were more frequent (23.1%). The survival analysis was 59.9% in one year, 40.7% in two years, and 27.8% in 5 years. There was a significant low survival rate linked to alcohol intake (p = 0.038), advanced cancer staging (p = 0.003), and procedures without surgery (p < 0.001). When these variables were included in the Cox regression model only surgery procedures (p = 0.005) demonstrated a significant effect on survival. Conclusion The findings suggest that patients who underwent surgery had a greater survival rate compared with those that did not. The low survival rates and the high percentage of patients diagnosed at advanced stages demonstrate that oral and oropharyngeal cancer patients should receive more attention. PMID:28638410

Discrete mixture modeling to address genetic heterogeneity in time-to-event regression

PubMed Central

Eng, Kevin H.; Hanlon, Bret M.

2014-01-01

Motivation: Time-to-event regression models are a critical tool for associating survival time outcomes with molecular data. Despite mounting evidence that genetic subgroups of the same clinical disease exist, little attention has been given to exploring how this heterogeneity affects time-to-event model building and how to accommodate it. Methods able to diagnose and model heterogeneity should be valuable additions to the biomarker discovery toolset. Results: We propose a mixture of survival functions that classifies subjects with similar relationships to a time-to-event response. This model incorporates multivariate regression and model selection and can be fit with an expectation maximization algorithm, we call Cox-assisted clustering. We illustrate a likely manifestation of genetic heterogeneity and demonstrate how it may affect survival models with little warning. An application to gene expression in ovarian cancer DNA repair pathways illustrates how the model may be used to learn new genetic subsets for risk stratification. We explore the implications of this model for censored observations and the effect on genomic predictors and diagnostic analysis. Availability and implementation: R implementation of CAC using standard packages is available at https://gist.github.com/programeng/8620b85146b14b6edf8f Data used in the analysis are publicly available. Contact: kevin.eng@roswellpark.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24532723

Epithelial–mesenchymal transition-associated secretory phenotype predicts survival in lung cancer patients

PubMed Central

Reka, Ajaya Kumar; Chen, Guoan; Keshamouni, Venkateshwar G.

2014-01-01

In cancer cells, the process of epithelial–mesenchymal transition (EMT) confers migratory and invasive capacity, resistance to apoptosis, drug resistance, evasion of host immune surveillance and tumor stem cell traits. Cells undergoing EMT may represent tumor cells with metastatic potential. Characterizing the EMT secretome may identify biomarkers to monitor EMT in tumor progression and provide a prognostic signature to predict patient survival. Utilizing a transforming growth factor-β-induced cell culture model of EMT, we quantitatively profiled differentially secreted proteins, by GeLC-tandem mass spectrometry. Integrating with the corresponding transcriptome, we derived an EMT-associated secretory phenotype (EASP) comprising of proteins that were differentially upregulated both at protein and mRNA levels. Four independent primary tumor-derived gene expression data sets of lung cancers were used for survival analysis by the random survival forests (RSF) method. Analysis of 97-gene EASP expression in human lung adenocarcinoma tumors revealed strong positive correlations with lymph node metastasis, advanced tumor stage and histological grade. RSF analysis built on a training set (n = 442), including age, sex and stage as variables, stratified three independent lung cancer data sets into low-, medium- and high-risk groups with significant differences in overall survival. We further refined EASP to a 20 gene signature (rEASP) based on variable importance scores from RSF analysis. Similar to EASP, rEASP predicted survival of both adenocarcinoma and squamous carcinoma patients. More importantly, it predicted survival in the early-stage cancers. These results demonstrate that integrative analysis of the critical biological process of EMT provides mechanism-based and clinically relevant biomarkers with significant prognostic value. PMID:24510113

SU-E-T-131: Artificial Neural Networks Applied to Overall Survival Prediction for Patients with Periampullary Carcinoma

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gong, Y; Yu, J; Yeung, V

Purpose: Artificial neural networks (ANN) can be used to discover complex relations within datasets to help with medical decision making. This study aimed to develop an ANN method to predict two-year overall survival of patients with peri-ampullary cancer (PAC) following resection. Methods: Data were collected from 334 patients with PAC following resection treated in our institutional pancreatic tumor registry between 2006 and 2012. The dataset contains 14 variables including age, gender, T-stage, tumor differentiation, positive-lymph-node ratio, positive resection margins, chemotherapy, radiation therapy, and tumor histology.After censoring for two-year survival analysis, 309 patients were left, of which 44 patients (∼15%) weremore » randomly selected to form testing set. The remaining 265 cases were randomly divided into training set (211 cases, ∼80% of 265) and validation set (54 cases, ∼20% of 265) for 20 times to build 20 ANN models. Each ANN has one hidden layer with 5 units. The 20 ANN models were ranked according to their concordance index (c-index) of prediction on validation sets. To further improve prediction, the top 10% of ANN models were selected, and their outputs averaged for prediction on testing set. Results: By random division, 44 cases in testing set and the remaining 265 cases have approximately equal two-year survival rates, 36.4% and 35.5% respectively. The 20 ANN models, which were trained and validated on the 265 cases, yielded mean c-indexes as 0.59 and 0.63 on validation sets and the testing set, respectively. C-index was 0.72 when the two best ANN models (top 10%) were used in prediction on testing set. The c-index of Cox regression analysis was 0.63. Conclusion: ANN improved survival prediction for patients with PAC. More patient data and further analysis of additional factors may be needed for a more robust model, which will help guide physicians in providing optimal post-operative care. This project was supported by PA CURE Grant.« less

Single gene and gene interaction effects on fertilization and embryonic survival rates in cattle.

PubMed

Khatib, H; Huang, W; Wang, X; Tran, A H; Bindrim, A B; Schutzkus, V; Monson, R L; Yandell, B S

2009-05-01

Decrease in fertility and conception rates is a major cause of economic loss and cow culling in dairy herds. Conception rate is the product of fertilization rate and embryonic survival rate. Identification of genetic factors that cause the death of embryos is the first step in eliminating this problem from the population and thereby increasing reproductive efficiency. A candidate pathway approach was used to identify candidate genes affecting fertilization and embryo survival rates using an in vitro fertilization experimental system. A total of 7,413 in vitro fertilizations were performed using oocytes from 504 ovaries and semen samples from 10 different bulls. Fertilization rate was calculated as the number of cleaved embryos 48 h postfertilization out of the total number of oocytes exposed to sperm. Survival rate of embryos was calculated as the number of blastocysts on d 7 of development out of the number of total embryos cultured. All ovaries were genotyped for 8 genes in the POU1F1 signaling pathway. Single-gene analysis revealed significant associations of GHR, PRLR, STAT5A, and UTMP with survival rate and of POU1F1, GHR, STAT5A, and OPN with fertilization rate. To further characterize the contribution of the entire integrated POU1F1 pathway to fertilization and early embryonic survival, a model selection procedure was applied. Comparisons among the different models showed that interactions between adjacent genes in the pathway revealed a significant contribution to the variation in fertility traits compared with other models that analyzed only bull information or only genes without interactions. Moreover, some genes that were not significant in the single-gene analysis showed significant effects in the interaction analysis. Thus, we propose that single genes as well as an entire pathway can be used in selection programs to improve reproduction performance in dairy cattle.

Detectable end of radiation prostate specific antigen assists in identifying men with unfavorable intermediate-risk prostate cancer at high risk of distant recurrence and cancer-specific mortality.

PubMed

Hayman, Jonathan; Phillips, Ryan; Chen, Di; Perin, Jamie; Narang, Amol K; Trieu, Janson; Radwan, Noura; Greco, Stephen; Deville, Curtiland; McNutt, Todd; Song, Daniel Y; DeWeese, Theodore L; Tran, Phuoc T

2018-06-01

Undetectable End of Radiation PSA (EOR-PSA) has been shown to predict improved survival in prostate cancer (PCa). While validating the unfavorable intermediate-risk (UIR) and favorable intermediate-risk (FIR) stratifications among Johns Hopkins PCa patients treated with radiotherapy, we examined whether EOR-PSA could further risk stratify UIR men for survival. A total of 302 IR patients were identified in the Johns Hopkins PCa database (178 UIR, 124 FIR). Kaplan-Meier curves and multivariable analysis was performed via Cox regression for biochemical recurrence free survival (bRFS), distant metastasis free survival (DMFS), and overall survival (OS), while a competing risks model was used for PCa specific survival (PCSS). Among the 235 patients with known EOR-PSA values, we then stratified by EOR-PSA and performed the aforementioned analysis. The median follow-up time was 11.5 years (138 months). UIR was predictive of worse DMFS and PCSS (P = 0.008 and P = 0.023) on multivariable analysis (MVA). Increased radiation dose was significant for improved DMFS (P = 0.016) on MVA. EOR-PSA was excluded from the models because it did not trend towards significance as a continuous or binary variable due to interaction with UIR, and we were unable to converge a multivariable model with a variable to control for this interaction. However, when stratifying by detectable versus undetectable EOR-PSA, UIR had worse DMFS and PCSS among detectable EOR-PSA patients, but not undetectable patients. UIR was significant on MVA among detectable EOR-PSA patients for DMFS (P = 0.021) and PCSS (P = 0.033), while RT dose also predicted PCSS (P = 0.013). EOR-PSA can assist in predicting DMFS and PCSS among UIR patients, suggesting a clinically meaningful time point for considering intensification of treatment in clinical trials of intermediate-risk men. © 2018 Wiley Periodicals, Inc.

HIS-based Kaplan-Meier plots--a single source approach for documenting and reusing routine survival information.

PubMed

Breil, Bernhard; Semjonow, Axel; Müller-Tidow, Carsten; Fritz, Fleur; Dugas, Martin

2011-02-16

Survival or outcome information is important for clinical routine as well as for clinical research and should be collected completely, timely and precisely. This information is relevant for multiple usages including quality control, clinical trials, observational studies and epidemiological registries. However, the local hospital information system (HIS) does not support this documentation and therefore this data has to generated by paper based or spreadsheet methods which can result in redundantly documented data. Therefore we investigated, whether integrating the follow-up documentation of different departments in the HIS and reusing it for survival analysis can enable the physician to obtain survival curves in a timely manner and to avoid redundant documentation. We analysed the current follow-up process of oncological patients in two departments (urology, haematology) with respect to different documentation forms. We developed a concept for comprehensive survival documentation based on a generic data model and implemented a follow-up form within the HIS of the University Hospital Muenster which is suitable for a secondary use of these data. We designed a query to extract the relevant data from the HIS and implemented Kaplan-Meier plots based on these data. To re-use this data sufficient data quality is needed. We measured completeness of forms with respect to all tumour cases in the clinic and completeness of documented items per form as incomplete information can bias results of the survival analysis. Based on the form analysis we discovered differences and concordances between both departments. We identified 52 attributes from which 13 were common (e.g. procedures and diagnosis dates) and were used for the generic data model. The electronic follow-up form was integrated in the clinical workflow. Survival data was also retrospectively entered in order to perform survival and quality analyses on a comprehensive data set. Physicians are now able to generate timely Kaplan-Meier plots on current data. We analysed 1029 follow-up forms of 965 patients with survival information between 1992 and 2010. Completeness of forms was 60.2%, completeness of items ranges between 94.3% and 98.5%. Median overall survival time was 16.4 years; median event-free survival time was 7.7 years. It is feasible to integrate survival information into routine HIS documentation such that Kaplan-Meier plots can be generated directly and in a timely manner.

Modeling association among demographic parameters in analysis of open population capture-recapture data.

PubMed

Link, William A; Barker, Richard J

2005-03-01

We present a hierarchical extension of the Cormack-Jolly-Seber (CJS) model for open population capture-recapture data. In addition to recaptures of marked animals, we model first captures of animals and losses on capture. The parameter set includes capture probabilities, survival rates, and birth rates. The survival rates and birth rates are treated as a random sample from a bivariate distribution, thus the model explicitly incorporates correlation in these demographic rates. A key feature of the model is that the likelihood function, which includes a CJS model factor, is expressed entirely in terms of identifiable parameters; losses on capture can be factored out of the model. Since the computational complexity of classical likelihood methods is prohibitive, we use Markov chain Monte Carlo in a Bayesian analysis. We describe an efficient candidate-generation scheme for Metropolis-Hastings sampling of CJS models and extensions. The procedure is illustrated using mark-recapture data for the moth Gonodontis bidentata.

Modeling association among demographic parameters in analysis of open population capture-recapture data

USGS Publications Warehouse

Link, William A.; Barker, Richard J.

2005-01-01

We present a hierarchical extension of the Cormack–Jolly–Seber (CJS) model for open population capture–recapture data. In addition to recaptures of marked animals, we model first captures of animals and losses on capture. The parameter set includes capture probabilities, survival rates, and birth rates. The survival rates and birth rates are treated as a random sample from a bivariate distribution, thus the model explicitly incorporates correlation in these demographic rates. A key feature of the model is that the likelihood function, which includes a CJS model factor, is expressed entirely in terms of identifiable parameters; losses on capture can be factored out of the model. Since the computational complexity of classical likelihood methods is prohibitive, we use Markov chain Monte Carlo in a Bayesian analysis. We describe an efficient candidate-generation scheme for Metropolis–Hastings sampling of CJS models and extensions. The procedure is illustrated using mark-recapture data for the moth Gonodontis bidentata.

Gender in the allocation of organs in kidney transplants: meta-analysis

PubMed Central

Santiago, Erika Vieira Almeida e; Silveira, Micheline Rosa; de Araújo, Vânia Eloisa; Farah, Katia de Paula; Acurcio, Francisco de Assis; Ceccato, Maria das Graças Braga

2015-01-01

OBJECTIVE To analyze whether gender influence survival results of kidney transplant grafts and patients. METHODS Systematic review with meta-analysis of cohort studies available on Medline (PubMed), LILACS, CENTRAL, and Embase databases, including manual searching and in the grey literature. The selection of studies and the collection of data were conducted twice by independent reviewers, and disagreements were settled by a third reviewer. Graft and patient survival rates were evaluated as effectiveness measurements. Meta-analysis was conducted with the Review Manager® 5.2 software, through the application of a random effects model. Recipient, donor, and donor-recipient gender comparisons were evaluated. RESULTS : Twenty-nine studies involving 765,753 patients were included. Regarding graft survival, those from male donors were observed to have longer survival rates as compared to the ones from female donors, only regarding a 10-year follow-up period. Comparison between recipient genders was not found to have significant differences on any evaluated follow-up periods. In the evaluation between donor-recipient genders, male donor-male recipient transplants were favored in a statistically significant way. No statistically significant differences were observed in regards to patient survival for gender comparisons in all follow-up periods evaluated. CONCLUSIONS The quantitative analysis of the studies suggests that donor or recipient genders, when evaluated isolatedly, do not influence patient or graft survival rates. However, the combination between donor-recipient genders may be a determining factor for graft survival. PMID:26465666

Effects of weather on survival in populations of boreal toads in Colorado

USGS Publications Warehouse

Scherer, R. D.; Muths, E.; Lambert, B.A.

2008-01-01

Understanding the relationships between animal population demography and the abiotic and biotic elements of the environments in which they live is a central objective in population ecology. For example, correlations between weather variables and the probability of survival in populations of temperate zone amphibians may be broadly applicable to several species if such correlations can be validated for multiple situations. This study focuses on the probability of survival and evaluates hypotheses based on six weather variables in three populations of Boreal Toads (Bufo boreas) from central Colorado over eight years. In addition to suggesting a relationship between some weather variables and survival probability in Boreal Toad populations, this study uses robust methods and highlights the need for demographic estimates that are precise and have minimal bias. Capture-recapture methods were used to collect the data, and the Cormack-Jolly-Seber model in program MARK was used for analysis. The top models included minimum daily winter air temperature, and the sum of the model weights for these models was 0.956. Weaker support was found for the importance of snow depth and the amount of environmental moisture in winter in modeling survival probability. Minimum daily winter air temperature was positively correlated with the probability of survival in Boreal Toads at other sites in Colorado and has been identified as an important covariate in studies in other parts of the world. If air temperatures are an important component of survival for Boreal Toads or other amphibians, changes in climate may have profound impacts on populations. Copyright 2008 Society for the Study of Amphibians and Reptiles.

Prognostic model of survival for typical bronchial carcinoid tumours: analysis of 1109 patients on behalf of the European Association of Thoracic Surgeons (ESTS) Neuroendocrine Tumours Working Group.

PubMed

Filosso, Pier Luigi; Guerrera, Francesco; Evangelista, Andrea; Welter, Stefan; Thomas, Pascal; Casado, Paula Moreno; Rendina, Erino Angelo; Venuta, Federico; Ampollini, Luca; Brunelli, Alessandro; Stella, Franco; Nosotti, Mario; Raveglia, Federico; Larocca, Valentina; Rena, Ottavio; Margaritora, Stefano; Ardissone, Francesco; Travis, William D; Sarkaria, Inderpal; Sagan, Dariusz

2015-09-01

Typical carcinoids (TCs) are uncommon, slow-growing neoplasms, usually with high 5-year survival rates. As these are rare tumours, their management is still based on small clinical observations and no international guidelines exist. Based on the European Society of Thoracic Surgeon Neuroendocrine Tumours Working Group (NET-WG) Database, we evaluated factors that may influence TCs mortality. Using the NET-WG database, an analysis on TC survival was performed. Overall survival (OS) was calculated starting from the date of intervention. Predictors of OS were investigated using the Cox model with shared frailty (accounting for the within-centre correlation). Candidate predictors were: gender, age, smoking habit, tumour location, previous malignancy, Eastern Cooperative Oncology Group (ECOG) performance status (PS), pT, pN, TNM stage and tumour vascular invasion. The final model included predictors with P ≤ 0.15 after a backward selection. Missing data in the evaluated predictors were multiple-imputed and combined estimates were obtained from five imputed data sets. For 58 of 1167 TC patients vital status was unavailable and analyses were therefore performed on 1109 patients from 17 institutions worldwide. During a median follow-up of 50 months, 87 patients died, with a 5-year OS rate of 93.7% (95% confidence interval: 91.7-95.3). Backward selection resulted in a prediction model for mortality containing age, gender, previous malignancies, peripheral tumour, TNM stage and ECOG PS. The final model showed a good discrimination ability with a C-statistic equal to 0.836 (bootstrap optimism-corrected 0.806). We presented and validated a promising prognostic model for TC survival, showing good calibration and discrimination ability. Further analyses are needed and could be focused on an external validation of this model. © The Author 2015. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Using Simulation to Interpret a Discrete Time Survival Model in a Complex Biological System: Fertility and Lameness in Dairy Cows

PubMed Central

Hudson, Christopher D.; Huxley, Jonathan N.; Green, Martin J.

2014-01-01

The ever-growing volume of data routinely collected and stored in everyday life presents researchers with a number of opportunities to gain insight and make predictions. This study aimed to demonstrate the usefulness in a specific clinical context of a simulation-based technique called probabilistic sensitivity analysis (PSA) in interpreting the results of a discrete time survival model based on a large dataset of routinely collected dairy herd management data. Data from 12,515 dairy cows (from 39 herds) were used to construct a multilevel discrete time survival model in which the outcome was the probability of a cow becoming pregnant during a given two day period of risk, and presence or absence of a recorded lameness event during various time frames relative to the risk period amongst the potential explanatory variables. A separate simulation model was then constructed to evaluate the wider clinical implications of the model results (i.e. the potential for a herd’s incidence rate of lameness to influence its overall reproductive performance) using PSA. Although the discrete time survival analysis revealed some relatively large associations between lameness events and risk of pregnancy (for example, occurrence of a lameness case within 14 days of a risk period was associated with a 25% reduction in the risk of the cow becoming pregnant during that risk period), PSA revealed that, when viewed in the context of a realistic clinical situation, a herd’s lameness incidence rate is highly unlikely to influence its overall reproductive performance to a meaningful extent in the vast majority of situations. Construction of a simulation model within a PSA framework proved to be a very useful additional step to aid contextualisation of the results from a discrete time survival model, especially where the research is designed to guide on-farm management decisions at population (i.e. herd) rather than individual level. PMID:25101997

Using simulation to interpret a discrete time survival model in a complex biological system: fertility and lameness in dairy cows.

PubMed

Hudson, Christopher D; Huxley, Jonathan N; Green, Martin J

2014-01-01

The ever-growing volume of data routinely collected and stored in everyday life presents researchers with a number of opportunities to gain insight and make predictions. This study aimed to demonstrate the usefulness in a specific clinical context of a simulation-based technique called probabilistic sensitivity analysis (PSA) in interpreting the results of a discrete time survival model based on a large dataset of routinely collected dairy herd management data. Data from 12,515 dairy cows (from 39 herds) were used to construct a multilevel discrete time survival model in which the outcome was the probability of a cow becoming pregnant during a given two day period of risk, and presence or absence of a recorded lameness event during various time frames relative to the risk period amongst the potential explanatory variables. A separate simulation model was then constructed to evaluate the wider clinical implications of the model results (i.e. the potential for a herd's incidence rate of lameness to influence its overall reproductive performance) using PSA. Although the discrete time survival analysis revealed some relatively large associations between lameness events and risk of pregnancy (for example, occurrence of a lameness case within 14 days of a risk period was associated with a 25% reduction in the risk of the cow becoming pregnant during that risk period), PSA revealed that, when viewed in the context of a realistic clinical situation, a herd's lameness incidence rate is highly unlikely to influence its overall reproductive performance to a meaningful extent in the vast majority of situations. Construction of a simulation model within a PSA framework proved to be a very useful additional step to aid contextualisation of the results from a discrete time survival model, especially where the research is designed to guide on-farm management decisions at population (i.e. herd) rather than individual level.

Proposal and validation of a new model to estimate survival for hepatocellular carcinoma patients.

PubMed

Liu, Po-Hong; Hsu, Chia-Yang; Hsia, Cheng-Yuan; Lee, Yun-Hsuan; Huang, Yi-Hsiang; Su, Chien-Wei; Lee, Fa-Yauh; Lin, Han-Chieh; Huo, Teh-Ia

2016-08-01

The survival of hepatocellular carcinoma (HCC) patients is heterogeneous. We aim to develop and validate a simple prognostic model to estimate survival for HCC patients (MESH score). A total of 3182 patients were randomised into derivation and validation cohort. Multivariate analysis was used to identify independent predictors of survival in the derivation cohort. The validation cohort was employed to examine the prognostic capabilities. The MESH score allocated 1 point for each of the following parameters: large tumour (beyond Milan criteria), presence of vascular invasion or metastasis, Child-Turcotte-Pugh score ≥6, performance status ≥2, serum alpha-fetoprotein level ≥20 ng/ml, and serum alkaline phosphatase ≥200 IU/L, with a maximal of 6 points. In the validation cohort, significant survival differences were found across all MESH scores from 0 to 6 (all p < 0.01). The MESH system was associated with the highest homogeneity and lowest corrected Akaike information criterion compared with Barcelona Clínic Liver Cancer, Hong Kong Liver Cancer (HKLC), Cancer of the Liver Italian Program, Taipei Integrated Scoring and model to estimate survival in ambulatory HCC Patients systems. The prognostic accuracy of the MESH scores remained constant in patients with hepatitis B- or hepatitis C-related HCC. The MESH score can also discriminate survival for patients from early to advanced stages of HCC. This newly proposed simple and accurate survival model provides enhanced prognostic accuracy for HCC. The MESH system is a useful supplement to the BCLC and HKLC classification schemes in refining treatment strategies. Copyright © 2016 Elsevier Ltd. All rights reserved.

IPMP Global Fit - A one-step direct data analysis tool for predictive microbiology.

PubMed

Huang, Lihan

2017-12-04

The objective of this work is to develop and validate a unified optimization algorithm for performing one-step global regression analysis of isothermal growth and survival curves for determination of kinetic parameters in predictive microbiology. The algorithm is incorporated with user-friendly graphical interfaces (GUIs) to develop a data analysis tool, the USDA IPMP-Global Fit. The GUIs are designed to guide the users to easily navigate through the data analysis process and properly select the initial parameters for different combinations of mathematical models. The software is developed for one-step kinetic analysis to directly construct tertiary models by minimizing the global error between the experimental observations and mathematical models. The current version of the software is specifically designed for constructing tertiary models with time and temperature as the independent model parameters in the package. The software is tested with a total of 9 different combinations of primary and secondary models for growth and survival of various microorganisms. The results of data analysis show that this software provides accurate estimates of kinetic parameters. In addition, it can be used to improve the experimental design and data collection for more accurate estimation of kinetic parameters. IPMP-Global Fit can be used in combination with the regular USDA-IPMP for solving the inverse problems and developing tertiary models in predictive microbiology. Published by Elsevier B.V.

Survival of blood transfusion recipients identified by a look-back investigation.

PubMed

Dorsey, Kerri A; Moritz, Erin D; Notari, Edward P; Schonberger, Lawrence B; Dodd, Roger Y

2014-01-01

Survival of blood transfusion recipients is a critical consideration in assessing the outcomes of transfusion. Data from the USA on the short- and long-term survival of recipients are limited. Blood product recipients were identified through a look-back study of Creutzfeldt-Jakob disease. Survival data were obtained from searches of the National Death Index or the Social Security Death Master File. Short- and long-term survival of recipients was analysed through descriptive statistics, Kaplan-Meier survival analysis, and stratified Cox proportional hazard modelling. This study includes data from 575 blood product recipients. One half of the recipients died within the first year of transfusion and the median time to death was 1.1 years. Survival rates at 5, 10, 15, 20, and 25 years after transfusion were 32%, 22%, 15%, 12%, and 9%, respectively. Survival rates varied with age at transfusion and type of component received, but not by gender. Survival after transfusion varied by year of transfusion, with recipients transfused in 1980-1989 having longer post-transfusion survival than those transfused in 2000-2010 (p=0.049). In multivariate models, the type of component transfused, but not the year of transfusion, was a significant predictor of survival among recipients; this effect varied by age. We provide an estimate of survival time from a geographically diverse sample of blood product recipients in the USA. Predictors of post-transfusion survival are numerous and complex, and may include year of transfusion and type of component transfused.

Comparison of ORSAT and SCARAB Reentry Analysis Tools for a Generic Satellite Test Case

NASA Technical Reports Server (NTRS)

Kelley, Robert L.; Hill, Nicole M.; Rochelle, W. C.; Johnson, Nicholas L.; Lips, T.

2010-01-01

Reentry analysis is essential to understanding the consequences of the full life cycle of a spacecraft. Since reentry is a key factor in spacecraft development, NASA and ESA have separately developed tools to assess the survivability of objects during reentry. Criteria such as debris casualty area and impact energy are particularly important to understanding the risks posed to people on Earth. Therefore, NASA and ESA have undertaken a series of comparison studies of their respective reentry codes for verification and improvements in accuracy. The NASA Object Reentry Survival Analysis Tool (ORSAT) and the ESA Spacecraft Atmospheric Reentry and Aerothermal Breakup (SCARAB) reentry analysis tools serve as standard codes for reentry survivability assessment of satellites. These programs predict whether an object will demise during reentry and calculate the debris casualty area of objects determined to survive, establishing the reentry risk posed to the Earth's population by surviving debris. A series of test cases have been studied for comparison and the most recent uses "Testsat," a conceptual satellite composed of generic parts, defined to use numerous simple shapes and various materials for a better comparison of the predictions of these two codes. This study is an improvement on the others in this series because of increased consistency in modeling techniques and variables. The overall comparison demonstrated that the two codes arrive at similar results. Either most objects modeled resulted in close agreement between the two codes, or if the difference was significant, the variance could be explained as a case of semantics in the model definitions. This paper presents the main results of ORSAT and SCARAB for the Testsat case and discusses the sources of any discovered differences. Discussion of the results of previous comparisons is made for a summary of differences between the codes and lessons learned from this series of tests.

Multivariate meta-analysis using individual participant data

PubMed Central

Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

2016-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. PMID:26099484

Advantages of a multi-state approach in surgical research: how intermediate events and risk factor profile affect the prognosis of a patient with locally advanced rectal cancer.

PubMed

Manzini, G; Ettrich, T J; Kremer, M; Kornmann, M; Henne-Bruns, D; Eikema, D A; Schlattmann, P; de Wreede, L C

2018-02-13

Standard survival analysis fails to give insight into what happens to a patient after a first outcome event (like first relapse of a disease). Multi-state models are a useful tool for analyzing survival data when different treatments and results (intermediate events) can occur. Aim of this study was to implement a multi-state model on data of patients with rectal cancer to illustrate the advantages of multi-state analysis in comparison to standard survival analysis. We re-analyzed data from the RCT FOGT-2 study by using a multi-state model. Based on the results we defined a high and low risk reference patient. Using dynamic prediction, we estimated how the survival probability changes as more information about the clinical history of the patient becomes available. A patient with stage UICC IIIc (vs UICC II) has a higher risk to develop distant metastasis (DM) or both DM and local recurrence (LR) if he/she discontinues chemotherapy within 6 months or between 6 and 12 months, as well as after the completion of 12 months CTx with HR 3.55 (p = 0.026), 5.33 (p = 0.001) and 3.37 (p < 0.001), respectively. He/she also has a higher risk to die after the development of DM (HR 1.72, p = 0.023). Anterior resection vs. abdominoperineal amputation means 63% risk reduction to develop DM or both DM and LR (HR 0.37, p = 0.003) after discontinuation of chemotherapy between 6 and 12 months. After development of LR, a woman has a 4.62 times higher risk to die (p = 0.006). A high risk reference patient has an estimated 43% 5-year survival probability at start of CTx, whereas for a low risk patient this is 79%. After the development of DM 1 year later, the high risk patient has an estimated 5-year survival probability of 11% and the low risk patient one of 21%. Multi-state models help to gain additional insight into the complex events after start of treatment. Dynamic prediction shows how survival probabilities change by progression of the clinical history.

Survival Data and Regression Models

NASA Astrophysics Data System (ADS)

Grégoire, G.

2014-12-01

We start this chapter by introducing some basic elements for the analysis of censored survival data. Then we focus on right censored data and develop two types of regression models. The first one concerns the so-called accelerated failure time models (AFT), which are parametric models where a function of a parameter depends linearly on the covariables. The second one is a semiparametric model, where the covariables enter in a multiplicative form in the expression of the hazard rate function. The main statistical tool for analysing these regression models is the maximum likelihood methodology and, in spite we recall some essential results about the ML theory, we refer to the chapter "Logistic Regression" for a more detailed presentation.

A Narrowing Target for Early Mars Climate Models: Which Models Survive Confrontation with Improved Hydrology Constraints?

NASA Astrophysics Data System (ADS)

Kite, E. S.; Goldblatt, C.; Gao, P.; Mayer, D. P.; Sneed, J.; Wilson, S. A.

2016-12-01

The wettest climates in Mars' geologic history represent habitability optima, and also set the tightest constraints on climate models. For lake-forming climates on Early Mars, geologic data constrain discharge, duration, intermittency, and the number of lake-forming events. We synthesise new and existing data to suggest that post-Noachian lake-forming climates were widely separated in time, lasted >10^4 yr individually, were few in number, but cumulatively lasted <10^7 yr (to allow olivine to survive globally). We compare these data against existing models, set out a new model involving methane bursts, and conclude with future directions for Early Mars geologic analysis and modelling work.

Blood neutrophil-lymphocyte ratio predicts survival in locally advanced cancer stomach treated with neoadjuvant chemotherapy FOLFOX 4.

PubMed

el Aziz, Lamiss Mohamed Abd

2014-12-01

Accurate predictors of survival for patients with advanced gastric cancer treated with neoadjuvant chemotherapy are currently lacking. In this study, we aimed to evaluate the prognostic significance of the neutrophil-lymphocyte ratio (NLR) in patients with stage III-IV gastric cancer who received neoadjuvant chemotherapy FOLFOX 4 as neoadjuvant chemotherapy. We enrolled 70 patients with stage III-IV cancer stomach in this study. Patients received FOLFOX 4 as neoadjuvant chemotherapy. Blood sample was collected before chemotherapy. The NLR was divided into two groups: high (>3) and low (≤ 3). Univariate analysis on progression-free survival (PFS) and overall survival (OS) was performed using the Kaplan-Meier and log-rank tests, and multivariate analysis was conducted using the Cox proportional hazards regression model. The toxicity was evaluated according to National Cancer Institute Common Toxicity Criteria. The univariate analysis showed that PFS and OS were both worse for patients with high NLR than for those with low NLR before chemotherapy (median PFS 28 and 44 months, respectively, P = 0.001; median OS 30 and 48 months, P = 0.001). Multivariate analysis showed that NLRs before chemotherapy were independent prognostic factors of OS but not for progression-free survival. NLR may serve as a potential biomarker for survival prognosis in patients with stage III-IV gastric cancer receiving neoadjuvant chemotherapy. The FOLFOX 4 demonstrated an acceptable toxicity.

Prognostic Score of Long-Term Survival After Surgery for Malignant Pleural Mesothelioma: A Multicenter Analysis.

PubMed

Leuzzi, Giovanni; Rea, Federico; Spaggiari, Lorenzo; Marulli, Giuseppe; Sperduti, Isabella; Alessandrini, Gabriele; Casiraghi, Monica; Bovolato, Pietro; Pariscenti, Gianluca; Alloisio, Marco; Infante, Maurizio; Pagan, Vittore; Fontana, Paolo; Oliaro, Alberto; Ruffini, Enrico; Ratto, Giovanni Battista; Leoncini, Giacomo; Sacco, Rocco; Mucilli, Felice; Facciolo, Francesco

2015-09-01

Despite ongoing efforts to improve therapy in malignant pleural mesothelioma, few patients undergoing extrapleural pneumonectomy experience long-term survival (LTS). This study aims to explore predictors of LTS after extrapleural pneumonectomy and to define a prognostic score. From January 2000 to December 2010, we retrospectively reviewed clinicopathologic and oncological factors in a multicenter cohort of 468 malignant pleural mesothelioma patients undergoing extrapleural pneumonectomy. LTS was defined as survival longer than 3 years. Associations were evaluated using χ(2), Student's t, and Mann-Whitney U tests. Logistic regression, Cox regression hazard model, and bootstrap analysis were applied to identify outcome predictors. Survival curves were calculated by the Kaplan-Meier method. Receiver operating characteristic analyses were used to estimate optimal cutoff and area under the curve for accuracy of the model. Overall, 107 patients (22.9%) survived at least 3 years. Median overall, cancer-specific, and disease-free survival times were 60 (95% confidence interval [CI], 51 to 69), 63 (95% CI, 54 to 72), and 49 months (95% CI, 39 to 58), respectively. At multivariate analysis, age (odds ratio, 0.51; 95% CI, 0.31 to 0.82), epithelioid histology (odds ratio, 7.07; 95% CI, 1.56 to 31.93), no history of asbestos exposure (odds ratio, 3.13; 95% CI, 1.13 to 8.66), and the ratio between metastatic and resected lymph nodes less than 22% (odds ratio, 4.12; 95% CI, 1.68 to 10.12) were independent predictors of LTS. According to these factors, we created a scoring system for LTS that allowed us to correctly predict overall, cancer-specific, and disease-free survival in the total sample, obtaining two different groups with favorable or poor prognosis (area under the curve, 0.74; standard error, 0.04; p < 0.0001). Our prognostic model facilitates the prediction of LTS after surgery for malignant pleural mesothelioma and can help to stratify the outcome and, eventually, tailor postoperative treatment. Copyright © 2015 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Cost-effectiveness of pembrolizumab versus docetaxel for the treatment of previously treated PD-L1 positive advanced NSCLC patients in the United States.

PubMed

Huang, Min; Lou, Yanyan; Pellissier, James; Burke, Thomas; Liu, Frank Xiaoqing; Xu, Ruifeng; Velcheti, Vamsidhar

2017-02-01

This analysis aimed to evaluate the cost-effectiveness of pembrolizumab compared with docetaxel in patients with previously treated advanced non-squamous cell lung cancer (NSCLC) with PD-L1 positive tumors (total proportion score [TPS] ≥ 50%). The analysis was conducted from a US third-party payer perspective. A partitioned-survival model was developed using data from patients from the KEYNOTE 010 clinical trial. The model used Kaplan-Meier (KM) estimates of progression-free survival (PFS) and overall survival (OS) from the trial for patients treated with either pembrolizumab 2 mg/kg or docetaxel 75 mg/m 2 with extrapolation based on fitted parametric functions and long-term registry data. Quality-adjusted life years (QALYs) were derived based on EQ-5D data from KEYNOTE 010 using a time to death approach. Costs of drug acquisition/administration, adverse event management, and clinical management of advanced NSCLC were included in the model. The base-case analysis used a time horizon of 20 years. Costs and health outcomes were discounted at a rate of 3% per year. A series of one-way and probabilistic sensitivity analyses were performed to test the robustness of the results. Base case results project for PD-L1 positive (TPS ≥50%) patients treated with pembrolizumab a mean survival of 2.25 years. For docetaxel, a mean survival time of 1.07 years was estimated. Expected QALYs were 1.71 and 0.76 for pembrolizumab and docetaxel, respectively. The incremental cost per QALY gained with pembrolizumab vs docetaxel is $168,619/QALY, which is cost-effective in the US using a threshold of 3-times GDP per capita. Sensitivity analyses showed the results to be robust over plausible values of the majority of inputs. Results were most sensitive to extrapolation of overall survival. Pembrolizumab improves survival, increases QALYs, and can be considered as a cost-effective option compared to docetaxel in PD-L1 positive (TPS ≥50%) pre-treated advanced NSCLC patients in the US.

Integration of multimodal RNA-seq data for prediction of kidney cancer survival

PubMed Central

Schwartzi, Matt; Parkl, Martin; Phanl, John H.; Wang., May D.

2016-01-01

Kidney cancer is of prominent concern in modern medicine. Predicting patient survival is critical to patient awareness and developing a proper treatment regimens. Previous prediction models built upon molecular feature analysis are limited to just gene expression data. In this study we investigate the difference in predicting five year survival between unimodal and multimodal analysis of RNA-seq data from gene, exon, junction, and isoform modalities. Our preliminary findings report higher predictive accuracy-as measured by area under the ROC curve (AUC)-for multimodal learning when compared to unimodal learning with both support vector machine (SVM) and k-nearest neighbor (KNN) methods. The results of this study justify further research on the use of multimodal RNA-seq data to predict survival for other cancer types using a larger sample size and additional machine learning methods. PMID:27532026

Multivariate survivorship analysis using two cross-sectional samples.

PubMed

Hill, M E

1999-11-01

As an alternative to survival analysis with longitudinal data, I introduce a method that can be applied when one observes the same cohort in two cross-sectional samples collected at different points in time. The method allows for the estimation of log-probability survivorship models that estimate the influence of multiple time-invariant factors on survival over a time interval separating two samples. This approach can be used whenever the survival process can be adequately conceptualized as an irreversible single-decrement process (e.g., mortality, the transition to first marriage among a cohort of never-married individuals). Using data from the Integrated Public Use Microdata Series (Ruggles and Sobek 1997), I illustrate the multivariate method through an investigation of the effects of race, parity, and educational attainment on the survival of older women in the United States.

Vulnerability survival analysis: a novel approach to vulnerability management

NASA Astrophysics Data System (ADS)

Farris, Katheryn A.; Sullivan, John; Cybenko, George

2017-05-01

Computer security vulnerabilities span across large, enterprise networks and have to be mitigated by security engineers on a routine basis. Presently, security engineers will assess their "risk posture" through quantifying the number of vulnerabilities with a high Common Vulnerability Severity Score (CVSS). Yet, little to no attention is given to the length of time by which vulnerabilities persist and survive on the network. In this paper, we review a novel approach to quantifying the length of time a vulnerability persists on the network, its time-to-death, and predictors of lower vulnerability survival rates. Our contribution is unique in that we apply the cox proportional hazards regression model to real data from an operational IT environment. This paper provides a mathematical overview of the theory behind survival analysis methods, a description of our vulnerability data, and an interpretation of the results.

A flexible cure rate model for spatially correlated survival data based on generalized extreme value distribution and Gaussian process priors.

PubMed

Li, Dan; Wang, Xia; Dey, Dipak K

2016-09-01

Our present work proposes a new survival model in a Bayesian context to analyze right-censored survival data for populations with a surviving fraction, assuming that the log failure time follows a generalized extreme value distribution. Many applications require a more flexible modeling of covariate information than a simple linear or parametric form for all covariate effects. It is also necessary to include the spatial variation in the model, since it is sometimes unexplained by the covariates considered in the analysis. Therefore, the nonlinear covariate effects and the spatial effects are incorporated into the systematic component of our model. Gaussian processes (GPs) provide a natural framework for modeling potentially nonlinear relationship and have recently become extremely powerful in nonlinear regression. Our proposed model adopts a semiparametric Bayesian approach by imposing a GP prior on the nonlinear structure of continuous covariate. With the consideration of data availability and computational complexity, the conditionally autoregressive distribution is placed on the region-specific frailties to handle spatial correlation. The flexibility and gains of our proposed model are illustrated through analyses of simulated data examples as well as a dataset involving a colon cancer clinical trial from the state of Iowa. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantile Regression with Censored Data

ERIC Educational Resources Information Center

Lin, Guixian

2009-01-01

The Cox proportional hazards model and the accelerated failure time model are frequently used in survival data analysis. They are powerful, yet have limitation due to their model assumptions. Quantile regression offers a semiparametric approach to model data with possible heterogeneity. It is particularly powerful for censored responses, where the…

Parental Monitoring during Early Adolescence Deters Adolescent Sexual Initiation: Discrete-Time Survival Mixture Analysis

ERIC Educational Resources Information Center

Huang, David Y. C.; Murphy, Debra A.; Hser, Yih-Ing

2011-01-01

We used discrete-time survival mixture modeling to examine 5,305 adolescents from the 1997 National Longitudinal Survey of Youth regarding the impact of parental monitoring during early adolescence (ages 14-16) on initiation of sexual intercourse and problem behavior engagement (ages 14-23). Four distinctive parental-monitoring groups were…

Landscape‐level patterns in fawn survival across North America

USGS Publications Warehouse

Gingery, Tess M.; Diefenbach, Duane R.; Wallingford, Bret D.; Rosenberry, Christopher S.

2018-01-01

A landscape‐level meta‐analysis approach to examining early survival of ungulates may elucidate patterns in survival not evident from individual studies. Despite numerous efforts, the relationship between fawn survival and habitat characteristics remains unclear and there has been no attempt to examine trends in survival across landscape types with adequate replication. In 2015–2016, we radiomarked 98 white‐tailed deer (Odocoileus virginianus) fawns in 2 study areas in Pennsylvania. By using a meta‐analysis approach, we compared fawn survival estimates from across North America using published data from 29 populations in 16 states to identify patterns in survival and cause‐specific mortality related to landscape characteristics, predator communities, and deer population density. We modeled fawn survival relative to percentage of agricultural land cover and deer density. Estimated average survival to 3–6 months of age was 0.414 ± 0.062 (SE) in contiguous forest landscapes (no agriculture) and for every 10% increase in land area in agriculture, fawn survival increased 0.049 ± 0.014. We classified cause‐specific mortality as human‐caused, natural (excluding predation), and predation according to agriculturally dominated, forested, and mixed (i.e., both agricultural and forest cover) landscapes. Predation was the greatest source of mortality in all landscapes. Landscapes with mixed forest and agricultural cover had greater proportions and rates of human‐caused mortalities, and lower proportions and rates of mortality due to predators, when compared to forested landscapes. Proportion and rate of natural deaths did not differ among landscapes. We failed to detect any relationship between fawn survival and deer density. The results highlight the need to consider multiple spatial scales when accounting for factors that influence fawn survival. Furthermore, variation in mortality sources and rates among landscapes indicate the potential for altered landscape mosaics to influence fawn survival rates. Wildlife managers can use the meta‐analysis to identify factors that will facilitate comparisons of results among studies and advance a better understanding of patterns in fawn survival.

Evaluation of Liver Biomarkers as Prognostic Factors for Outcomes to Yttrium-90 Radioembolization of Primary and Secondary Liver Malignancies.

PubMed

Henrie, Adam M; Wittstrom, Kristina; Delu, Adam; Deming, Paulina

2015-09-01

The objective of this study was to examine indicators of liver function and inflammation for prognostic value in predicting outcomes to yttrium-90 radioembolization (RE). In a retrospective analysis, markers of liver function and inflammation, biomarkers required to stage liver function and inflammation, and data regarding survival, tumor response, and progression after RE were recorded. Univariate regression models were used to investigate the prognostic value of liver biomarkers in predicting outcome to RE as measured by survival, tumor progression, and radiographic and biochemical tumor response. Markers from all malignancy types were analyzed together. A subgroup analysis was performed on markers from patients with metastatic colorectal cancer. A total of 31 patients received RE from 2004 to 2014. Median survival after RE for all malignancies combined was 13.6 months (95% CI: 6.7-17.6 months). Results from an exploratory analysis of patient data suggest that liver biomarkers, including albumin concentrations, international normalized ratio, bilirubin concentrations, and the model for end-stage liver disease score, possess prognostic value in predicting outcomes to RE.

A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

PubMed

Anderson, Carl A; McRae, Allan F; Visscher, Peter M

2006-07-01

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

Inferring species interactions through joint mark–recapture analysis

USGS Publications Warehouse

Yackulic, Charles B.; Korman, Josh; Yard, Michael D.; Dzul, Maria C.

2018-01-01

Introduced species are frequently implicated in declines of native species. In many cases, however, evidence linking introduced species to native declines is weak. Failure to make strong inferences regarding the role of introduced species can hamper attempts to predict population viability and delay effective management responses. For many species, mark–recapture analysis is the more rigorous form of demographic analysis. However, to our knowledge, there are no mark–recapture models that allow for joint modeling of interacting species. Here, we introduce a two‐species mark–recapture population model in which the vital rates (and capture probabilities) of one species are allowed to vary in response to the abundance of the other species. We use a simulation study to explore bias and choose an approach to model selection. We then use the model to investigate species interactions between endangered humpback chub (Gila cypha) and introduced rainbow trout (Oncorhynchus mykiss) in the Colorado River between 2009 and 2016. In particular, we test hypotheses about how two environmental factors (turbidity and temperature), intraspecific density dependence, and rainbow trout abundance are related to survival, growth, and capture of juvenile humpback chub. We also project the long‐term effects of different rainbow trout abundances on adult humpback chub abundances. Our simulation study suggests this approach has minimal bias under potentially challenging circumstances (i.e., low capture probabilities) that characterized our application and that model selection using indicator variables could reliably identify the true generating model even when process error was high. When the model was applied to rainbow trout and humpback chub, we identified negative relationships between rainbow trout abundance and the survival, growth, and capture probability of juvenile humpback chub. Effects on interspecific interactions on survival and capture probability were strongly supported, whereas support for the growth effect was weaker. Environmental factors were also identified to be important and in many cases stronger than interspecific interactions, and there was still substantial unexplained variation in growth and survival rates. The general approach presented here for combining mark–recapture data for two species is applicable in many other systems and could be modified to model abundance of the invader via other modeling approaches.

Prognostic factors in multiple myeloma: selection using Cox's proportional hazard model.

PubMed

Pasqualetti, P; Collacciani, A; Maccarone, C; Casale, R

1996-01-01

The pretreatment characteristics of 210 patients with multiple myeloma, observed between 1980 and 1994, were evaluated as potential prognostic factors for survival. Multivariate analysis according to Cox's proportional hazard model identified in the 160 dead patients with myeloma, among 26 different single prognostic variables, the following factors in order of importance: beta 2-microglobulin; bone marrow plasma cell percentage, hemoglobinemia, degree of lytic bone lesions, serum creatinine, and serum albumin. By analysis of these variables a prognostic index (PI), that considers the regression coefficients derived by Cox's model of all significant factors, was obtained. Using this it was possible to separate the whole patient group into three stages: stage I (PI < 1.485, 67 patients), stage II (PI: 1.485-2.090, 76 patients), and stage III (PI > 2.090, 67 patients), with a median survivals of 68, 36 and 13 months (P < 0.0001), respectively. Also the responses to therapy (P < 0.0001) and the survival curves (P < 0.00001) presented significant differences among the three subgroups. Knowledge of these factors could be of value in predicting prognosis and in planning therapy in patients with multiple myeloma.

Prognostic value of tumor suppressors in osteosarcoma before and after neoadjuvant chemotherapy.

PubMed

Robl, Bernhard; Pauli, Chantal; Botter, Sander Martijn; Bode-Lesniewska, Beata; Fuchs, Bruno

2015-05-09

Primary bone cancers are among the deadliest cancer types in adolescents, with osteosarcomas being the most prevalent form. Osteosarcomas are commonly treated with multi-drug neoadjuvant chemotherapy and therapy success as well as patient survival is affected by the presence of tumor suppressors. In order to assess the prognostic value of tumor-suppressive biomarkers, primary osteosarcoma tissues were analyzed prior to and after neoadjuvant chemotherapy. We constructed a tissue microarray from high grade osteosarcoma samples, consisting of 48 chemotherapy naïve biopsies (BXs) and 47 tumor resections (RXs) after neoadjuvant chemotherapy. We performed immunohistochemical stainings of P53, P16, maspin, PTEN, BMI1 and Ki67, characterized the subcellular localization and related staining outcome with chemotherapy response and overall survival. Binary logistic regression analysis was used to analyze chemotherapy response and Kaplan-Meier-analysis as well as the Cox proportional hazards model was applied for analysis of patient survival. No significant associations between biomarker expression in BXs and patient survival or chemotherapy response were detected. In univariate analysis, positive immunohistochemistry of P53 (P = 0.008) and P16 (P16; P = 0.033) in RXs was significantly associated with poor survival prognosis. In addition, presence of P16 in RXs was associated with poor survival in multivariate regression analysis (P = 0.003; HR = 0.067) while absence of P16 was associated with good chemotherapy response (P = 0.004; OR = 74.076). Presence of PTEN on tumor RXs was significantly associated with an improved survival prognosis (P = 0.022). Positive immunohistochemistry (IHC) of P16 and P53 in RXs was indicative for poor overall patient survival whereas positive IHC of PTEN was prognostic for good overall patient survival. In addition, we found that P16 might be a marker of osteosarcoma chemotherapy resistance. Therefore, our study supports the use of tumor RXs to assess the prognostic value of biomarkers.

Lung Cancer Pathological Image Analysis Using a Hidden Potts Model

PubMed Central

Li, Qianyun; Yi, Faliu; Wang, Tao; Xiao, Guanghua; Liang, Faming

2017-01-01

Nowadays, many biological data are acquired via images. In this article, we study the pathological images scanned from 205 patients with lung cancer with the goal to find out the relationship between the survival time and the spatial distribution of different types of cells, including lymphocyte, stroma, and tumor cells. Toward this goal, we model the spatial distribution of different types of cells using a modified Potts model for which the parameters represent interactions between different types of cells and estimate the parameters of the Potts model using the double Metropolis-Hastings algorithm. The double Metropolis-Hastings algorithm allows us to simulate samples approximately from a distribution with an intractable normalizing constant. Our numerical results indicate that the spatial interaction between the lymphocyte and tumor cells is significantly associated with the patient’s survival time, and it can be used together with the cell count information to predict the survival of the patients. PMID:28615918

Causes of death in long-term lung cancer survivors: a SEER database analysis.

PubMed

Abdel-Rahman, Omar

2017-07-01

Long-term (>5 years) lung cancer survivors represent a small but distinct subgroup of lung cancer patients and information about the causes of death of this subgroup is scarce. The Surveillance, Epidemiology and End Results (SEER) database (1988-2008) was utilized to determine the causes of death of long-term survivors of lung cancer. Survival analysis was conducted using Kaplan-Meier analysis and multivariate analysis was conducted using a Cox proportional hazard model. Clinicopathological characteristics and survival outcomes were assessed for the whole cohort. A total of 78,701 lung cancer patients with >5 years survival were identified. This cohort included 54,488 patients surviving 5-10 years and 24,213 patients surviving >10 years. Among patients surviving 5-10 years, 21.8% were dead because of primary lung cancer, 10.2% were dead because of other cancers, 6.8% were dead because of cardiac disease and 5.3% were dead because of non-malignant pulmonary disease. Among patients surviving >10 years, 12% were dead because of primary lung cancer, 6% were dead because of other cancers, 6.9% were dead because of cardiac disease and 5.6% were dead because of non-malignant pulmonary disease. On multivariate analysis, factors associated with longer cardiac-disease-specific survival in multivariate analysis include younger age at diagnosis (p < .0001), white race (vs. African American race) (p = .005), female gender (p < .0001), right-sided disease (p = .003), adenocarcinoma (vs. large cell or small cell carcinoma), histology and receiving local treatment by surgery rather than radiotherapy (p < .0001). The probability of death from primary lung cancer is still significant among other causes of death even 20 years after diagnosis of lung cancer. Moreover, cardiac as well as non-malignant pulmonary causes contribute a considerable proportion of deaths in long-term lung cancer survivors.

Circulating Tumor Cells in Breast Cancer Patients Treated by Neoadjuvant Chemotherapy: A Meta-analysis.

PubMed

Bidard, François-Clément; Michiels, Stefan; Riethdorf, Sabine; Mueller, Volkmar; Esserman, Laura J; Lucci, Anthony; Naume, Bjørn; Horiguchi, Jun; Gisbert-Criado, Rafael; Sleijfer, Stefan; Toi, Masakazu; Garcia-Saenz, Jose A; Hartkopf, Andreas; Generali, Daniele; Rothé, Françoise; Smerage, Jeffrey; Muinelo-Romay, Laura; Stebbing, Justin; Viens, Patrice; Magbanua, Mark Jesus M; Hall, Carolyn S; Engebraaten, Olav; Takata, Daisuke; Vidal-Martínez, José; Onstenk, Wendy; Fujisawa, Noriyoshi; Diaz-Rubio, Eduardo; Taran, Florin-Andrei; Cappelletti, Maria Rosa; Ignatiadis, Michail; Proudhon, Charlotte; Wolf, Denise M; Bauldry, Jessica B; Borgen, Elin; Nagaoka, Rin; Carañana, Vicente; Kraan, Jaco; Maestro, Marisa; Brucker, Sara Yvonne; Weber, Karsten; Reyal, Fabien; Amara, Dominic; Karhade, Mandar G; Mathiesen, Randi R; Tokiniwa, Hideaki; Llombart-Cussac, Antonio; Meddis, Alessandra; Blanche, Paul; d'Hollander, Koenraad; Cottu, Paul; Park, John W; Loibl, Sibylle; Latouche, Aurélien; Pierga, Jean-Yves; Pantel, Klaus

2018-04-12

We conducted a meta-analysis in nonmetastatic breast cancer patients treated by neoadjuvant chemotherapy (NCT) to assess the clinical validity of circulating tumor cell (CTC) detection as a prognostic marker. We collected individual patient data from 21 studies in which CTC detection by CellSearch was performed in early breast cancer patients treated with NCT. The primary end point was overall survival, analyzed according to CTC detection, using Cox regression models stratified by study. Secondary end points included distant disease-free survival, locoregional relapse-free interval, and pathological complete response. All statistical tests were two-sided. Data from patients were collected before NCT (n = 1574) and before surgery (n = 1200). CTC detection revealed one or more CTCs in 25.2% of patients before NCT; this was associated with tumor size (P < .001). The number of CTCs detected had a detrimental and decremental impact on overall survival (P < .001), distant disease-free survival (P < .001), and locoregional relapse-free interval (P < .001), but not on pathological complete response. Patients with one, two, three to four, and five or more CTCs before NCT displayed hazard ratios of death of 1.09 (95% confidence interval [CI] = 0.65 to 1.69), 2.63 (95% CI = 1.42 to 4.54), 3.83 (95% CI = 2.08 to 6.66), and 6.25 (95% CI = 4.34 to 9.09), respectively. In 861 patients with full data available, adding CTC detection before NCT increased the prognostic ability of multivariable prognostic models for overall survival (P < .001), distant disease-free survival (P < .001), and locoregional relapse-free interval (P = .008). CTC count is an independent and quantitative prognostic factor in early breast cancer patients treated by NCT. It complements current prognostic models based on tumor characteristics and response to therapy.

Modeling Information Accumulation in Psychological Tests Using Item Response Times

ERIC Educational Resources Information Center

Ranger, Jochen; Kuhn, Jörg-Tobias

2015-01-01

In this article, a latent trait model is proposed for the response times in psychological tests. The latent trait model is based on the linear transformation model and subsumes popular models from survival analysis, like the proportional hazards model and the proportional odds model. Core of the model is the assumption that an unspecified monotone…

Prognostic nutritional index is associated with survival after total gastrectomy for patients with gastric cancer.

PubMed

Ishizuka, Mitsuru; Oyama, Yusuke; Abe, Akihito; Tago, Kazuma; Tanaka, Genki; Kubota, Keiichi

2014-08-01

To investigate the influence of clinical characteristics including nutritional markers on postoperative survival in patients undergoing total gastrectomy (TG) for gastric cancer (GC). One hundred fifty-four patients were enrolled. Uni- and multivariate analyses using the Cox proportional hazard model were performed to explore the most valuable clinical characteristic that was associated with postoperative survival. Multivariate analysis using twelve clinical characteristics selected from univariate analyses revealed that age (≤ 72/>72), carcinoembryonic antigen (≤ 20/>20) (ng/ml), white blood cell count (≤ 9.5/>9.5) (× 10(3)/mm(3)), prognostic nutritional index (PNI) (≤ 45/>45) and lymph node metastasis (negative/positive) were associated with postoperative survival. Kaplan-Meier analysis and log-rank test showed that patients with higher PNI (>45) had a higher postoperative survival rate than those with lower PNI (≤ 45) (p<0.001). PNI is associated with postoperative survival of patients undergoing TG for GC and is able to divide such patients into two independent groups before surgery. Copyright© 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Urate levels predict survival in amyotrophic lateral sclerosis: Analysis of the expanded Pooled Resource Open-Access ALS clinical trials database.

PubMed

Paganoni, Sabrina; Nicholson, Katharine; Chan, James; Shui, Amy; Schoenfeld, David; Sherman, Alexander; Berry, James; Cudkowicz, Merit; Atassi, Nazem

2018-03-01

Urate has been identified as a predictor of amyotrophic lateral sclerosis (ALS) survival in some but not all studies. Here we leverage the recent expansion of the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database to study the association between urate levels and ALS survival. Pooled data of 1,736 ALS participants from the PRO-ACT database were analyzed. Cox proportional hazards regression models were used to evaluate associations between urate levels at trial entry and survival. After adjustment for potential confounders (i.e., creatinine and body mass index), there was an 11% reduction in risk of reaching a survival endpoint during the study with each 1-mg/dL increase in uric acid levels (adjusted hazard ratio 0.89, 95% confidence interval 0.82-0.97, P < 0.01). Our pooled analysis provides further support for urate as a prognostic factor for survival in ALS and confirms the utility of the PRO-ACT database as a powerful resource for ALS epidemiological research. Muscle Nerve 57: 430-434, 2018. © 2017 Wiley Periodicals, Inc.

Marital status and survival of patients with oral cavity squamous cell carcinoma: a population-based study.

PubMed

Shi, Xiao; Zhang, Ting-Ting; Hu, Wei-Ping; Ji, Qing-Hai

2017-04-25

The relationship between marital status and oral cavity squamous cell carcinoma (OCSCC) survival has not been explored. The objective of our study was to evaluate the impact of marital status on OCSCC survival and investigate the potential mechanisms. Married patients had better 5-year cancer-specific survival (CSS) (66.7% vs 54.9%) and 5-year overall survival (OS) (56.0% vs 41.1%). In multivariate Cox regression models, unmarried patients also showed higher mortality risk for both CSS (Hazard Ratio [HR]: 1.260, 95% confidence interval (CI): 1.187-1.339, P < 0.001) and OS (HR: 1.328, 95% CI: 1.266-1.392, P < 0.001). Multivariate logistic regression showed married patients were more likely to be diagnosed at earlier stage (P < 0.001) and receive surgery (P < 0.001). Married patients still demonstrated better prognosis in the 1:1 matched group analysis (CSS: 62.9% vs 60.8%, OS: 52.3% vs 46.5%). 11022 eligible OCSCC patients were identified from Surveillance, Epidemiology, and End Results (SEER) database, including 5902 married and 5120 unmarried individuals. Kaplan-Meier analysis, Log-rank test and Cox proportional hazards regression model were used to analyze survival and mortality risk. Influence of marital status on stage, age at diagnosis and selection of treatment was determined by binomial and multinomial logistic regression. Propensity score matching method was adopted to perform a 1:1 matched cohort. Marriage has an independently protective effect on OCSCC survival. Earlier diagnosis and more sufficient treatment are possible explanations. Besides, even after 1:1 matching, survival advantage of married group still exists, indicating that spousal support from other aspects may also play an important role.

Marital status and survival of patients with oral cavity squamous cell carcinoma: a population-based study

PubMed Central

Shi, Xiao; Zhang, Ting-ting; Hu, Wei-ping; Ji, Qing-hai

2017-01-01

Background The relationship between marital status and oral cavity squamous cell carcinoma (OCSCC) survival has not been explored. The objective of our study was to evaluate the impact of marital status on OCSCC survival and investigate the potential mechanisms. Results Married patients had better 5-year cancer-specific survival (CSS) (66.7% vs 54.9%) and 5-year overall survival (OS) (56.0% vs 41.1%). In multivariate Cox regression models, unmarried patients also showed higher mortality risk for both CSS (Hazard Ratio [HR]: 1.260, 95% confidence interval (CI): 1.187–1.339, P < 0.001) and OS (HR: 1.328, 95% CI: 1.266–1.392, P < 0.001). Multivariate logistic regression showed married patients were more likely to be diagnosed at earlier stage (P < 0.001) and receive surgery (P < 0.001). Married patients still demonstrated better prognosis in the 1:1 matched group analysis (CSS: 62.9% vs 60.8%, OS: 52.3% vs 46.5%). Materials and Methods 11022 eligible OCSCC patients were identified from Surveillance, Epidemiology, and End Results (SEER) database, including 5902 married and 5120 unmarried individuals. Kaplan-Meier analysis, Log-rank test and Cox proportional hazards regression model were used to analyze survival and mortality risk. Influence of marital status on stage, age at diagnosis and selection of treatment was determined by binomial and multinomial logistic regression. Propensity score matching method was adopted to perform a 1:1 matched cohort. Conclusions Marriage has an independently protective effect on OCSCC survival. Earlier diagnosis and more sufficient treatment are possible explanations. Besides, even after 1:1 matching, survival advantage of married group still exists, indicating that spousal support from other aspects may also play an important role. PMID:28415710

Variations of Escherichia coli O157:H7 Survival in Purple Soils

PubMed Central

Zhang, Taoxiang; Hu, Suping; Yang, Wenhao

2017-01-01

Escherichia coli O157:H7 is a well-recognized cause of human illness. Survival of Escherichia coli O157:H7 in five purple soils from Sichuan Province was investigated. The dynamics of E. coli O157:H7 survival in purple soils were described by the Weibull model. Results showed that this model is suitable to fit survival curves of E. coli O157:H7 in purple soils, with the calculated td value (survival time needed to reach the detection limit of 100 CFU·g−1) ranging from 2.99 days to 26.36 days. The longest survival time of E. coli O157:H7 was observed in neutral purple soils (24.49 days), followed by alkalescent purple soil (18.62 days) and acid purple soil (3.48 days). The redundancy analysis (RDA) revealed that td values were significantly enhanced by soil nutrition (total organic carbon (OC), total nitrogen (TN), available potassium (AK) and the ratio of humic acid to fulvic acid (Ha/Fa)), but were significantly suppressed by iron and aluminum oxide. PMID:29057845

Comparing survival outcomes of gross total resection and subtotal resection with radiotherapy for craniopharyngioma: a meta-analysis.

PubMed

Wang, Guoqing; Zhang, Xiaoyang; Feng, Mengzhao; Guo, Fuyou

2018-06-01

Recent studies suggest that subtotal resection (STR) followed by radiation therapy (RT) is an appealing alternative to gross total resection (GTR) for craniopharyngioma, but it remains controversial. We conducted a meta-analysis to determine whether GTR is superior to STR with RT for craniopharyngioma. A systematic search was performed for articles published until October 2017 in the PubMed, Embase, and Cochrane Central databases. The endpoints of interest are overall survival and progression-free survival. Pooled hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) were calculated using a fixed or random-effects model. The data were analyzed using Review Manager 5.3 software. A total of 744 patients (seven cohort studies) were enrolled for analyses. There were no significant differences between the GTR and STR with RT groups when the authors compared the pooled HRs at the end of the follow-up period. Overall survival (pooled HR = 0.76, 95% CI: 0.46-1.25, P = 0.28) and progression-free survival (pooled HR = 1.52, 95% CI: 0.42-5.44, P = 0.52) were similar between the two groups. The current meta-analysis suggests that GTR and STR with RT have the similar survival outcomes for craniopharyngioma. Copyright © 2018 Elsevier Inc. All rights reserved.

Factors predicting survival in amyotrophic lateral sclerosis patients on non-invasive ventilation.

PubMed

Gonzalez Calzada, Nuria; Prats Soro, Enric; Mateu Gomez, Lluis; Giro Bulta, Esther; Cordoba Izquierdo, Ana; Povedano Panades, Monica; Dorca Sargatal, Jordi; Farrero Muñoz, Eva

2016-01-01

Non invasive ventilation (NIV) improves quality of life and extends survival in amyotrophic lateral sclerosis (ALS) patients. However, few data exist about the factors related to survival. We intended to assess the predictive factors that influence survival in patients after NIV initiation. Patients who started NIV from 2000 to 2014 and were tolerant (compliance ≥ 4 hours) were included; demographic, disease related and respiratory variables at NIV initiation were analysed. Statistical analysis was performed using the Kaplan-Meier test and Cox proportional hazard models. 213 patients were included with median survival from NIV initiation of 13.5 months. In univariate analysis, the identified risk factors for mortality were severity of bulbar involvement (HR 2), Forced Vital Capacity (FVC) % (HR 0.99) and ALSFRS-R (HR 0.97). Multivariate analysis showed that bulbar involvement (HR 1.92) and ALSFRS-R (HR 0.97) were independent predictive factors of survival in patients on NIV. In our study, the two prognostic factors in ALS patients following NIV were the severity of bulbar involvement and ALSFRS-R at the time on NIV initiation. A better assessment of bulbar involvement, including evaluation of the upper airway, and a careful titration on NIV are necessary to optimize treatment efficacy.

Palliative interventions for hepatocellular carcinoma patients: analysis of the National Cancer Database.

PubMed

Hammad, Abdulrahman Y; Robbins, Jared R; Turaga, Kiran K; Christians, Kathleen K; Gamblin, T Clark; Johnston, Fabian M

2017-01-01

Palliative therapies are provided to a subset of hepatocellular carcinoma (HCC) patients with the aim of providing symptomatic relief, better quality of life and improved survival. The present study sought to assess and compare the efficacy of different palliative therapies for HCC. The National Cancer Database (NCDB), a retrospective national database that captures approximately 70% of all patients treated for cancer in the US, was queried for patients with HCC who were deemed unresectable from 1998-2011. Patients were stratified by receipt of palliative therapy. Survival analysis was examined by log-rank test and Kaplan Meier curves, and a multivariate proportional hazards model was utilized to identify the predictors of survival. A total of 3,267 patients were identified; 287 (8.7%) received surgical palliation, 827 (25.3%) received radiotherapy (RT), 877 (26.8%) received chemotherapy, 1,067 (32.6%) received pain management therapy, while 209 (6.4%) received a combination of the previous three modalities. On multivariate analysis palliative RT was identified as a positive predictor of survival [hazards ratio (HR) 0.65; 95% CI, 0.50-0.83]. Stratifying by disease stage, palliative RT provided a significant survival benefit for patients with stage IV disease. Palliative RT appears to extend survival and should be considered for patients presenting with late stage HCC.

Mediation Analysis with Survival Outcomes: Accelerated Failure Time vs. Proportional Hazards Models.

PubMed

Gelfand, Lois A; MacKinnon, David P; DeRubeis, Robert J; Baraldi, Amanda N

2016-01-01

Survival time is an important type of outcome variable in treatment research. Currently, limited guidance is available regarding performing mediation analyses with survival outcomes, which generally do not have normally distributed errors, and contain unobserved (censored) events. We present considerations for choosing an approach, using a comparison of semi-parametric proportional hazards (PH) and fully parametric accelerated failure time (AFT) approaches for illustration. We compare PH and AFT models and procedures in their integration into mediation models and review their ability to produce coefficients that estimate causal effects. Using simulation studies modeling Weibull-distributed survival times, we compare statistical properties of mediation analyses incorporating PH and AFT approaches (employing SAS procedures PHREG and LIFEREG, respectively) under varied data conditions, some including censoring. A simulated data set illustrates the findings. AFT models integrate more easily than PH models into mediation models. Furthermore, mediation analyses incorporating LIFEREG produce coefficients that can estimate causal effects, and demonstrate superior statistical properties. Censoring introduces bias in the coefficient estimate representing the treatment effect on outcome-underestimation in LIFEREG, and overestimation in PHREG. With LIFEREG, this bias can be addressed using an alternative estimate obtained from combining other coefficients, whereas this is not possible with PHREG. When Weibull assumptions are not violated, there are compelling advantages to using LIFEREG over PHREG for mediation analyses involving survival-time outcomes. Irrespective of the procedures used, the interpretation of coefficients, effects of censoring on coefficient estimates, and statistical properties should be taken into account when reporting results.

Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer.

PubMed

Xu, Haoming; Moni, Mohammad Ali; Liò, Pietro

2015-12-01

In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient. Our methods are applied to analyse seven microarray cancer gene expression datasets: breast cancer, ovarian cancer, lung cancer, liver cancer, renal cancer and osteosarcoma. Firstly, we applied a principal component analysis to reduce the dimensionality of original gene expression data. Secondly, we applied a network regularised Cox regression model on the reduced gene expression datasets. By using normalised mutual information method and multiplex network model, we predict the comorbidities for the liver cancer based on the integration of diverse set of omics and clinical data, and we find the diseasome associations (disease-gene association) among different cancers based on the identified common significant genes. Finally, we evaluated the precision of the approach with respect to the accuracy of survival prediction using ROC curves. We report that colon cancer, liver cancer and renal cancer share the CXCL5 gene, and breast cancer, ovarian cancer and renal cancer share the CCND2 gene. Our methods are useful to predict survival of the patient and disease comorbidities more accurately and helpful for improvement of the care of patients with comorbidity. Software in Matlab and R is available on our GitHub page: https://github.com/ssnhcom/NetworkRegularisedCox.git. Copyright © 2015. Published by Elsevier Ltd.

Survival Predictions of Ceramic Crowns Using Statistical Fracture Mechanics

PubMed Central

Nasrin, S.; Katsube, N.; Seghi, R.R.; Rokhlin, S.I.

2017-01-01

This work establishes a survival probability methodology for interface-initiated fatigue failures of monolithic ceramic crowns under simulated masticatory loading. A complete 3-dimensional (3D) finite element analysis model of a minimally reduced molar crown was developed using commercially available hardware and software. Estimates of material surface flaw distributions and fatigue parameters for 3 reinforced glass-ceramics (fluormica [FM], leucite [LR], and lithium disilicate [LD]) and a dense sintered yttrium-stabilized zirconia (YZ) were obtained from the literature and incorporated into the model. Utilizing the proposed fracture mechanics–based model, crown survival probability as a function of loading cycles was obtained from simulations performed on the 4 ceramic materials utilizing identical crown geometries and loading conditions. The weaker ceramic materials (FM and LR) resulted in lower survival rates than the more recently developed higher-strength ceramic materials (LD and YZ). The simulated 10-y survival rate of crowns fabricated from YZ was only slightly better than those fabricated from LD. In addition, 2 of the model crown systems (FM and LD) were expanded to determine regional-dependent failure probabilities. This analysis predicted that the LD-based crowns were more likely to fail from fractures initiating from margin areas, whereas the FM-based crowns showed a slightly higher probability of failure from fractures initiating from the occlusal table below the contact areas. These 2 predicted fracture initiation locations have some agreement with reported fractographic analyses of failed crowns. In this model, we considered the maximum tensile stress tangential to the interfacial surface, as opposed to the more universally reported maximum principal stress, because it more directly impacts crack propagation. While the accuracy of these predictions needs to be experimentally verified, the model can provide a fundamental understanding of the importance that pre-existing flaws at the intaglio surface have on fatigue failures. PMID:28107637

Lindley frailty model for a class of compound Poisson processes

NASA Astrophysics Data System (ADS)

Kadilar, Gamze Özel; Ata, Nihal

2013-10-01

The Lindley distribution gain importance in survival analysis for the similarity of exponential distribution and allowance for the different shapes of hazard function. Frailty models provide an alternative to proportional hazards model where misspecified or omitted covariates are described by an unobservable random variable. Despite of the distribution of the frailty is generally assumed to be continuous, it is appropriate to consider discrete frailty distributions In some circumstances. In this paper, frailty models with discrete compound Poisson process for the Lindley distributed failure time are introduced. Survival functions are derived and maximum likelihood estimation procedures for the parameters are studied. Then, the fit of the models to the earthquake data set of Turkey are examined.

Survival models for harvest management of mourning dove populations

USGS Publications Warehouse

Otis, D.L.

2002-01-01

Quantitative models of the relationship between annual survival and harvest rate of migratory game-bird populations are essential to science-based harvest management strategies. I used the best available band-recovery and harvest data for mourning doves (Zenaida macroura) to build a set of models based on different assumptions about compensatory harvest mortality. Although these models suffer from lack of contemporary data, they can be used in development of an initial set of population models that synthesize existing demographic data on a management-unit scale, and serve as a tool for prioritization of population demographic information needs. Credible harvest management plans for mourning dove populations will require a long-term commitment to population monitoring and iterative population analysis.

Twenty-year follow-up study of long-term survival of limited-stage small-cell lung cancer and overview of prognostic and treatment factors.

PubMed

Tai, Patricia; Tonita, Jon; Yu, Edward; Skarsgard, David

2003-07-01

To predict the long-term survival results of clinical trials earlier than using actuarial methods and to assess the factors predictive of long-term cure in patients with limited-stage small-cell lung cancer. Between 1981 and 1998, 1417 new cases of small-cell lung cancer were diagnosed in Saskatchewan, Canada, of which 244 were limited stage and treated with curative intent. They were followed to the end of February 2002. A parametric lognormal statistical model was retrospectively validated to determine whether long-term survival rates could be estimated several years earlier than is possible using the standard life-table actuarial method. The survival time of the uncured group followed a lognormal distribution. Four 2-year periods of diagnosis were combined, and patients were followed as a cohort for an additional 2 years. The estimated 10-year cause-specific survival rate was 13% by the lognormal model. The Kaplan-Meier calculation for 10-year cause-specific survival rate was 15% +/- 3%. The data also showed that the absence of mediastinal lymphadenopathy and higher chest radiotherapy dose were significant prognostic factors on multivariate analysis (p < 0.05). Among the 163 patients given prophylactic cranial irradiation, a higher biologically effective dose to the brain did not improve survival or decrease the incidence of brain metastases. The lognormal model has been validated for the estimation of survival in patients with limited-stage small-cell lung cancer. A higher biologically effective dose to the brain did not improve survival or decrease the incidence of brain metastases.

Prediction of cancer specific survival after radical nephroureterectomy for upper tract urothelial carcinoma: development of an optimized postoperative nomogram using decision curve analysis.

PubMed

Rouprêt, Morgan; Hupertan, Vincent; Seisen, Thomas; Colin, Pierre; Xylinas, Evanguelos; Yates, David R; Fajkovic, Harun; Lotan, Yair; Raman, Jay D; Zigeuner, Richard; Remzi, Mesut; Bolenz, Christian; Novara, Giacomo; Kassouf, Wassim; Ouzzane, Adil; Rozet, François; Cussenot, Olivier; Martinez-Salamanca, Juan I; Fritsche, Hans-Martin; Walton, Thomas J; Wood, Christopher G; Bensalah, Karim; Karakiewicz, Pierre I; Montorsi, Francesco; Margulis, Vitaly; Shariat, Shahrokh F

2013-05-01

We conceived and proposed a unique and optimized nomogram to predict cancer specific survival after radical nephroureterectomy in patients with upper tract urothelial carcinoma by merging the 2 largest multicenter data sets reported in this population. The international and the French national collaborative groups on upper tract urothelial carcinoma pooled data on 3,387 patients treated with radical nephroureterectomy for whom full data for nomogram development were available. The merged study population was randomly split into the development cohort (2,371) and the external validation cohort (1,016). Cox regressions were used for univariable and multivariable analyses, and to build different models. The ultimate reduced nomogram was assessed using Harrell's concordance index (c-index) and decision curve analysis. Of the 2,371 patients in the nomogram development cohort 510 (21.5%) died of upper tract urothelial carcinoma during followup. The actuarial cancer specific survival probability at 5 years was 73.7% (95% CI 71.9-75.6). Decision curve analysis revealed that the use of the best model was associated with benefit gains relative to the prediction of cancer specific survival. The optimized nomogram included only 5 variables associated with cancer specific survival on multivariable analysis, those of age (p = 0.001), T stage (p <0.001), N stage (p = 0.001), architecture (p = 0.02) and lymphovascular invasion (p = 0.001). The discriminative accuracy of the nomogram was 0.8 (95% CI 0.77-0.86). Using standard pathological features obtained from the largest data set of upper tract urothelial carcinomas worldwide, we devised and validated an accurate and ultimate nomogram, superior to any single clinical variable, for predicting cancer specific survival after radical nephroureterectomy. Copyright © 2013 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.

The Impact of Chemoembolization Endpoints on Survival in Hepatocellular Carcinoma Patients

PubMed Central

Jin, Brian; Wang, Dingxin; Lewandowski, Robert J.; Riaz, Ahsun; Ryu, Robert K.; Sato, Kent T.; Larson, Andrew C.; Salem, Riad; Omary, Reed A.

2010-01-01

OBJECTIVE To investigate the relationship between angiographic embolic endpoints of transarterial chemoembolization (TACE) and survival in patients with hepatocellular carcinoma (HCC). MATERIALS AND METHODS This study retrospectively assessed 105 patients with surgically unresectable HCC who underwent TACE. Patients were classified according to a previously established subjective angiographic chemoembolization endpoint (SACE) scale. Only one patient was classified as SACE level 1 and thus excluded from all subsequent analysis. Survival was evaluated with Kaplan-Meier analysis. Multivariate analysis with Cox’s proportional hazard regression model was used to determine independent prognostic risk factors of survival. RESULTS Overall median survival was 21.1 months (95% confidence interval [CI], 15.9–26.4). Patients embolized to SACE levels 2 and 3 were aggregated and had a significantly higher median survival (25.6 months; 95% CI, 16.2–35.0) than patients embolized to SACE level 4 (17.1 months; 95% CI, 13.3–20.9) (p = 0.035). Multivariate analysis indicated that SACE level 4 (Hazard ratio [HR], 2.49; 95% CI, 1.41–4.42; p = 0.002), European Cooperative Oncology Group performance status > 0 (HR, 1.97; 95% CI, 1.15–3.37; p = 0.013), American Joint Committee on Cancer stage 3 or 4 (HR, 2.42; 95% CI, 1.27–4.60; p = 0.007), and Child-Pugh class B (HR, 1.94; 95% CI, 1.09–3.46; p = 0.025) were all independent negative prognostic indicators of survival. CONCLUSION Embolization to an intermediate, sub-stasis endpoint (SACE levels 2 and 3) during TACE improves survival compared to embolization to a higher, stasis endpoint (SACE level 4). Interventional oncologists should consider targeting these intermediate, sub-stasis angiographic endpoints during TACE. PMID:21427346

Efficacy of the treatment of transarterial chemoembolization combined with radiotherapy for hepatocellular carcinoma with portal vein tumor thrombus: A propensity score analysis.

PubMed

Li, Xiao-Long; Guo, Wei-Xing; Hong, Xiao-Dong; Yang, Liang; Wang, Kang; Shi, Jie; Li, Nan; Wu, Meng-Chao; Cheng, Shu-Qun

2016-10-01

The survival outcome of patients with unresectable hepatocellular carcinoma (HCC) with portal vein tumor thrombus (PVTT) who received transarterial chemoembolization (TACE) combined with radiotherapy (RT) remains unclear. A total of 112 and 735 HCC patients with PVTT undergoing TACE combined with RT and TACE alone, respectively, were evaluated. One hundred and eight pairs of matched patients were selected from each treatment arm by using a propensity score matching (PSM) analysis. Of the whole study population, TACE combined with RT showed significant survival benefits compared with TACE in all patients (median survival, 11.0 vs 4.8 months; P < 0.001), especially in patients with PVTT involving the right/left portal vein (median survival, 12.5 vs 5.2 months; P < 0.001) and main portal vein trunk (median survival, 8.9 vs 4.3 months; P < 0.001). After one-to-one PSM, 108 pairs of matched patients were selected for further analysis. In the propensity model, the median survival time was 10.9 versus 4.1 months (P < 0.001) in all patients, 12.5 versus 4.4 months (P = 0.002) in patients with PVTT involving the right/left portal vein and 8.9 versus 4.0 months (P < 0.001) in patients with PVTT involving the main portal vein trunk. The treatment, maximum lesion diameter and main trunk PVTT were the independent prognostic factors for survival at uni- and multivariate analysis. TACE combined with RT provides a significantly better survival outcome than TACE for unresectable HCC patients with PVTT, especially for patients with PVTT involving the right/left portal vein or main trunk. © 2016 The Japan Society of Hepatology.

True survival benefit of lung transplantation for cystic fibrosis patients: the Zurich experience.

PubMed

Hofer, Markus; Benden, Christian; Inci, Ilhan; Schmid, Christoph; Irani, Sarosh; Speich, Rudolf; Weder, Walter; Boehler, Annette

2009-04-01

Lung transplantation is the ultimate therapy for end-stage cystic fibrosis (CF) lung disease; however, the debate continues as to whether lung transplantation improves survival. We report post-transplant outcome in CF at our institution by comparing 5-year post-transplant survival with a calculated 5-year survival without lung transplantation, using a predictive 5-year survivorship model, and describe pre-transplant parameters influencing transplant outcome. CF patients undergoing lung transplantation at our center were included (1992 to 2007). Survival rates were calculated and compared, and univariate and multivariate Cox regression analyses were used for statistical assessment. Eighty transplants were performed in CF patients, 11 (13.8%) of whom were children. Mean age at transplant was 26.2 years (95% confidence interval: 24.4 to 28.0). The Liou raw score at transplant was -20 (95% confidence interval: -16 to -24), resulting in an estimated 5-year survival without transplantation of 33 +/- 14%, compared with a 5-year post-transplant survival of 68.2 +/- 5.6%. Further improvement was noted in the recent transplant era (since 2000), with a 5-year survival of 72.7 +/- 7.3%. Univariate analysis revealed that later year of transplant and diagnosis of diabetes influenced survival positively. Pediatric age had no negative impact. In the multivariate analysis, only diabetes influenced survival, in a positive manner. Lung transplantation performed at centers having experience with the procedure can offer a true survival benefit to patients with end-stage CF lung disease.

Probabilistic Survivability Versus Time Modeling

NASA Technical Reports Server (NTRS)

Joyner, James J., Sr.

2015-01-01

This technical paper documents Kennedy Space Centers Independent Assessment team work completed on three assessments for the Ground Systems Development and Operations (GSDO) Program to assist the Chief Safety and Mission Assurance Officer (CSO) and GSDO management during key programmatic reviews. The assessments provided the GSDO Program with an analysis of how egress time affects the likelihood of astronaut and worker survival during an emergency. For each assessment, the team developed probability distributions for hazard scenarios to address statistical uncertainty, resulting in survivability plots over time. The first assessment developed a mathematical model of probabilistic survivability versus time to reach a safe location using an ideal Emergency Egress System at Launch Complex 39B (LC-39B); the second used the first model to evaluate and compare various egress systems under consideration at LC-39B. The third used a modified LC-39B model to determine if a specific hazard decreased survivability more rapidly than other events during flight hardware processing in Kennedys Vehicle Assembly Building (VAB).Based on the composite survivability versus time graphs from the first two assessments, there was a soft knee in the Figure of Merit graphs at eight minutes (ten minutes after egress ordered). Thus, the graphs illustrated to the decision makers that the final emergency egress design selected should have the capability of transporting the flight crew from the top of LC 39B to a safe location in eight minutes or less. Results for the third assessment were dominated by hazards that were classified as instantaneous in nature (e.g. stacking mishaps) and therefore had no effect on survivability vs time to egress the VAB. VAB emergency scenarios that degraded over time (e.g. fire) produced survivability vs time graphs that were line with aerospace industry norms.

Application of hazard models for patients with breast cancer in Cuba

PubMed Central

Alfonso, Anet Garcia; de Oca, Néstor Arcia Montes

2011-01-01

There has been a rapid development in hazard models and survival analysis in the last decade. This article aims to assess the overall survival time of breast cancer in Cuba, as well as to determine plausible factors that may have a significant impact in the survival time. The data are obtained from the National Cancer Register of Cuba. The data set used in this study relates to 6381 patients diagnosed with breast cancer between January 2000 and December 2002. Follow-up data are available until the end of December 2007, by which time 2167 (33.9%) had died and 4214 (66.1%) were still alive. The adequacy of six parametric models is assessed by using their Akaike information criterion values. Five of the six parametric models (Exponential, Weibull, Log-logistic, Lognormal, and Generalized Gamma) are parameterized by using the accelerated failure-time metric, and the Gompertz model is parameterized by using the proportional hazard metric. The main result in terms of survival is found for the different categories of the clinical stage covariate. The survival time among patients who have been diagnosed at early stage of breast cancer is about 60% higher than the one among patients diagnosed at more advanced stage of the disease. Differences among provinces have not been found. The age is another significant factor, but there is no important difference between patient ages. PMID:21686138

Application of hazard models for patients with breast cancer in Cuba.

PubMed

Alfonso, Anet Garcia; de Oca, Néstor Arcia Montes

2011-01-01

There has been a rapid development in hazard models and survival analysis in the last decade. This article aims to assess the overall survival time of breast cancer in Cuba, as well as to determine plausible factors that may have a significant impact in the survival time. The data are obtained from the National Cancer Register of Cuba. The data set used in this study relates to 6381 patients diagnosed with breast cancer between January 2000 and December 2002. Follow-up data are available until the end of December 2007, by which time 2167 (33.9%) had died and 4214 (66.1%) were still alive. The adequacy of six parametric models is assessed by using their Akaike information criterion values. Five of the six parametric models (Exponential, Weibull, Log-logistic, Lognormal, and Generalized Gamma) are parameterized by using the accelerated failure-time metric, and the Gompertz model is parameterized by using the proportional hazard metric. The main result in terms of survival is found for the different categories of the clinical stage covariate. The survival time among patients who have been diagnosed at early stage of breast cancer is about 60% higher than the one among patients diagnosed at more advanced stage of the disease. Differences among provinces have not been found. The age is another significant factor, but there is no important difference between patient ages.

Surgery on spinal epidural metastases (SEM) in renal cell carcinoma: a plea for a new paradigm.

PubMed

Bakker, Nicolaas A; Coppes, Maarten H; Vergeer, Rob A; Kuijlen, Jos M A; Groen, Rob J M

2014-09-01

Prediction models for outcome of decompressive surgical resection of spinal epidural metastases (SEM) have in common that they have been developed for all types of SEM, irrespective of the type of primary tumor. It is our experience in clinical practice, however, that these models often fail to accurately predict outcome in the individual patient. To investigate whether decision making could be optimized by applying tumor-specific prediction models. For the proof of concept, we analyzed patients with SEM from renal cell carcinoma that we have operated on. Retrospective chart analysis 2006 to 2012. Twenty-one consecutive patients with symptomatic SEM of renal cell carcinoma. Predictive factors for survival. Next to established predictive factors for survival, we analyzed the predictive value of the Motzer criteria in these patients. The Motzer criteria comprise a specific and validated risk model for survival in patients with renal cell carcinoma. After multivariable analysis, only Motzer intermediate (hazard ratio [HR] 17.4, 95% confidence interval [CI] 1.82-166, p=.01) and high risk (HR 39.3, 95% CI 3.10-499, p=.005) turned out to be significantly associated with survival in patients with renal cell carcinoma that we have operated on. In this study, we have demonstrated that decision making could have been optimized by implementing the Motzer criteria next to established prediction models. We, therefore, suggest that in future, in patients with SEM from renal cell carcinoma, the Motzer criteria are also taken into account. Copyright © 2014 Elsevier Inc. All rights reserved.

Adult survival and population growth rate in Colorado big brown bats (Eptesicus fuscus)

USGS Publications Warehouse

O'Shea, T.J.; Ellison, L.E.; Stanley, T.R.

2011-01-01

We studied adult survival and population growth at multiple maternity colonies of big brown bats (Eptesicus fuscus) in Fort Collins, Colorado. We investigated hypotheses about survival using information-theoretic methods and mark-recapture analyses based on passive detection of adult females tagged with passive integrated transponders. We constructed a 3-stage life-history matrix model to estimate population growth rate (??) and assessed the relative importance of adult survival and other life-history parameters to population growth through elasticity and sensitivity analysis. Annual adult survival at 5 maternity colonies monitored from 2001 to 2005 was estimated at 0.79 (95% confidence interval [95% CI] = 0.77-0.82). Adult survival varied by year and roost, with low survival during an extreme drought year, a finding with negative implications for bat populations because of the likelihood of increasing drought in western North America due to global climate change. Adult survival during winter was higher than in summer, and mean life expectancies calculated from survival estimates were lower than maximum longevity records. We modeled adult survival with recruitment parameter estimates from the same population. The study population was growing (?? = 1.096; 95% CI = 1.057-1.135). Adult survival was the most important demographic parameter for population growth. Growth clearly had the highest elasticity to adult survival, followed by juvenile survival and adult fecundity (approximately equivalent in rank). Elasticity was lowest for fecundity of yearlings. The relative importances of the various life-history parameters for population growth rate are similar to those of large mammals. ?? 2011 American Society of Mammalogists.

Systematic mediastinal lymphadenectomy or mediastinal lymph node sampling in patients with pathological stage I NSCLC: a meta-analysis.

PubMed

Dong, Siyuan; Du, Jiang; Li, Wenya; Zhang, Shuguang; Zhong, Xinwen; Zhang, Lin

2015-02-01

To evaluate the evidence comparing systematic mediastinal lymphadenectomy (SML) and mediastinal lymph node sampling (MLS) in the treatment of pathological stage I NSCLC using meta-analytical techniques. A literature search was undertaken until January 2014 to identify the comparative studies evaluating 1-, 3-, and 5-year survival rates. The pooled odds ratios (OR) and the 95 % confidence intervals (95 % CI) were calculated with either the fixed or random effect models. One RCT study and four retrospective studies were included in our meta-analysis. These studies included a total of 711 patients: 317 treated with SML, and 394 treated with MLS. The SML and the MLS did not demonstrate a significant difference in the 1-year survival rate. There were significant statistical differences between the 3-year (P = 0.03) and 5-year survival rates (P = 0.004), which favored SML. This meta-analysis suggests that in pathological stage I NSCLC, the MLS can get the similar outcome to the SML in terms of 1-year survival rate. However, the SML is superior to MLS in terms of 3- and 5-year survival rates.

Water-Exchange-Modified Kinetic Parameters from Dynamic Contrast-Enhanced MRI as Prognostic Biomarkers of Survival in Advanced Hepatocellular Carcinoma Treated with Antiangiogenic Monotherapy

PubMed Central

Lee, Sang Ho; Hayano, Koichi; Zhu, Andrew X.; Sahani, Dushyant V.; Yoshida, Hiroyuki

2015-01-01

Background To find prognostic biomarkers in pretreatment dynamic contrast-enhanced MRI (DCE-MRI) water-exchange-modified (WX) kinetic parameters for advanced hepatocellular carcinoma (HCC) treated with antiangiogenic monotherapy. Methods Twenty patients with advanced HCC underwent DCE-MRI and were subsequently treated with sunitinib. Pretreatment DCE-MRI data on advanced HCC were analyzed using five different WX kinetic models: the Tofts-Kety (WX-TK), extended TK (WX-ETK), two compartment exchange, adiabatic approximation to tissue homogeneity (WX-AATH), and distributed parameter (WX-DP) models. The total hepatic blood flow, arterial flow fraction (γ), arterial blood flow (BF A), portal blood flow, blood volume, mean transit time, permeability-surface area product, fractional interstitial volume (v I), extraction fraction, mean intracellular water molecule lifetime (τ C), and fractional intracellular volume (v C) were calculated. After receiver operating characteristic analysis with leave-one-out cross-validation, individual parameters for each model were assessed in terms of 1-year-survival (1YS) discrimination using Kaplan-Meier analysis, and association with overall survival (OS) using univariate Cox regression analysis with permutation testing. Results The WX-TK-model-derived γ (P = 0.022) and v I (P = 0.010), and WX-ETK-model-derived τ C (P = 0.023) and v C (P = 0.042) were statistically significant prognostic biomarkers for 1YS. Increase in the WX-DP-model-derived BF A (P = 0.025) and decrease in the WX-TK, WX-ETK, WX-AATH, and WX-DP-model-derived v C (P = 0.034, P = 0.038, P = 0.028, P = 0.041, respectively) were significantly associated with an increase in OS. Conclusions The WX-ETK-model-derived v C was an effective prognostic biomarker for advanced HCC treated with sunitinib. PMID:26366997

System Engineering Approach to Assessing Integrated Survivability

DTIC Science & Technology

2009-08-01

based response for the above engagements using LS- Dyna for blast modelling, MADYMO for safety and human response, CFD software (Fluent) is used to...Simulation JFAS Joint Force Analysis Simulation JANUS Joint Army Navy Uniform Simulation LS- DYNA Livermore Software-Dynamics MADYMO...management technologies. The “don’t be killed” layer of survivability protection accounts for many of the mitigation technologies (i.e. blast

Growth and survival of Salmonella Paratyphi A in roasted marinated chicken during refrigerated storage: Effect of temperature abuse and computer simulation for cold chain management

USDA-ARS?s Scientific Manuscript database

This research was conducted to evaluate the feasibility of using a one-step dynamic numerical analysis and optimization method to directly construct a tertiary model to describe the growth and survival of Salmonella Paratyphi A (SPA) in a marinated roasted chicken product. Multiple dynamic growth a...

Subretinal transplantation of bone marrow mesenchymal stem cells delays retinal degeneration in the RCS rat model of retinal degeneration.

PubMed

Inoue, Yuji; Iriyama, Aya; Ueno, Shuji; Takahashi, Hidenori; Kondo, Mineo; Tamaki, Yasuhiro; Araie, Makoto; Yanagi, Yasuo

2007-08-01

Because there is no effective treatment for this retinal degeneration, potential application of cell-based therapy has attracted considerable attention. Several investigations support that bone marrow mesenchymal stem cells (MSCs) can be used for a broad spectrum of indications. Bone marrow MSCs exert their therapeutic effect in part by secreting trophic factors to promote cell survival. The current study investigates whether bone marrow MSCs secrete factor(s) to promote photoreceptor cell survival and whether subretinal transplantation of bone marrow MSCs promotes photoreceptor survival in a retinal degeneration model using Royal College of Surgeons (RCS) rats. In vitro, using mouse retinal cell culture, it was demonstrated that the conditioned medium of the MSCs delays photoreceptor cell apoptosis, suggesting that the secreted factor(s) from the MSCs promote photoreceptor cell survival. In vivo, the MSCs were injected into the subretinal space of the RCS rats and histological analysis, real-time RT-PCR and electrophysiological analysis demonstrated that the subretinal transplantation of MSCs delays retinal degeneration and preserves retinal function in the RCS rats. These results suggest that MSC is a useful cell source for cell-replacement therapy for some forms of retinal degeneration.

No inequalities in survival from colorectal cancer by education and socioeconomic deprivation - a population-based study in the North Region of Portugal, 2000-2002.

PubMed

Antunes, Luís; Mendonça, Denisa; Bento, Maria José; Rachet, Bernard

2016-08-05

Association between cancer survival and socioeconomic status has been reported in various countries but it has never been studied in Portugal. We aimed here to study the role of education and socioeconomic deprivation level on survival from colorectal cancer in the North Region of Portugal using a population-based cancer registry dataset. We analysed a cohort of patients aged 15-84 years, diagnosed with a colorectal cancer in the North Region of Portugal between 2000 and 2002. Education and socioeconomic deprivation level was assigned to each patient based on their area of residence. We measured socioeconomic deprivation using the recently developed European Deprivation Index. Net survival was estimated using Pohar-Perme estimator and age-adjusted excess hazard ratios were estimated using parametric flexible models. Since no deprivation-specific life tables were available, we performed a sensitivity analysis to test the robustness of the results to life tables adjusted for education and socioeconomic deprivation level. A total of 4,105 cases were included in the analysis. In male patients (56.3 %), a pattern of worse 5- and 10-year net survival in the less educated (survival gap between extreme education groups: -7 % and -10 % at 5 and 10 years, respectively) and more deprived groups (survival gap between extreme EDI groups: -5 % both at 5 and 10 years) was observed when using general life tables. No such clear pattern was found among female patients. In both sexes, when likely differences in background mortality by education or deprivation were accounted for in the sensitivity analysis, any differences in net survival between education or deprivation groups vanished. Our study shows that observed differences in survival by education and EDI level are most likely attributable to inequalities in background survival. Also, it confirms the importance of using the relevant life tables and of performing sensitivity analysis when evaluating socioeconomic inequalities in cancer survival. Comparison studies of different healthcare systems organization should be performed to better understand its influence on cancer survival inequalities.

Survival of teeth treated with cast post and cores: A retrospective analysis over an observation period of up to 19.5 years.

PubMed

Raedel, Michael; Fiedler, Cliff; Jacoby, Stephan; Boening, Klaus W

2015-07-01

Scientific data about the long-term survival of teeth treated with cast post and cores are scarce. Retrospective studies often use different target events for their analyses. A comparison is therefore complicated. For associated tooth-, jaw-, and patient-related factors little evidence exists as to their effect on survival. The purpose of this study was to extend the knowledge on the survival of teeth treated with cast post and cores for observation periods of more than 10 years. A decrease or increase in survival times according to the presence or absence of associated parameters needs to be evaluated. A retrospective evaluation was conducted of all cast post and cores inserted in 1 university clinic between January 1992 and June 2011. A Kaplan-Meier survival analysis was carried out by using extraction as the target event. The survival curves for different tooth types, the presence or absence of adjacent teeth, and the prosthetic restoration of the respective jaws were compared by using the log-rank test (α=.05). A Cox regression model was calculated for multivariate analyses. A total of 717 cast post and cores for 343 patients were recorded. The mean survival time was 13.5 years. A statistically significant decrease in survival times was found for canines (11.9 years) and premolars (13.4 years) versus molars (14.1 years), no adjacent teeth (10.6 years) versus at least 1 adjacent tooth (13.8 years), and the restoration with removable dental prostheses (12.5 years) versus fixed dental prostheses and single crowns (13.9 years). The largest reduction in survival time was found for teeth being used as an abutment for a double crown-retained removable partial dental prosthesis (telescopic denture) (9.8 years). Tooth type and adjacent tooth status remained as significant variables within the multivariate Cox regression model. Cast post and cores have an acceptable long-term survival time. Because different factors may influence survival, considering these factors in treatment planning may increase the long-term success of these restorations. Copyright © 2015 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

TEAM-HF Cost-Effectiveness Model: A Web-Based Program Designed to Evaluate the Cost-Effectiveness of Disease Management Programs in Heart Failure

PubMed Central

Reed, Shelby D.; Neilson, Matthew P.; Gardner, Matthew; Li, Yanhong; Briggs, Andrew H.; Polsky, Daniel E.; Graham, Felicia L.; Bowers, Margaret T.; Paul, Sara C.; Granger, Bradi B.; Schulman, Kevin A.; Whellan, David J.; Riegel, Barbara; Levy, Wayne C.

2015-01-01

Background Heart failure disease management programs can influence medical resource use and quality-adjusted survival. Because projecting long-term costs and survival is challenging, a consistent and valid approach to extrapolating short-term outcomes would be valuable. Methods We developed the Tools for Economic Analysis of Patient Management Interventions in Heart Failure (TEAM-HF) Cost-Effectiveness Model, a Web-based simulation tool designed to integrate data on demographic, clinical, and laboratory characteristics, use of evidence-based medications, and costs to generate predicted outcomes. Survival projections are based on a modified Seattle Heart Failure Model (SHFM). Projections of resource use and quality of life are modeled using relationships with time-varying SHFM scores. The model can be used to evaluate parallel-group and single-cohort designs and hypothetical programs. Simulations consist of 10,000 pairs of virtual cohorts used to generate estimates of resource use, costs, survival, and incremental cost-effectiveness ratios from user inputs. Results The model demonstrated acceptable internal and external validity in replicating resource use, costs, and survival estimates from 3 clinical trials. Simulations to evaluate the cost-effectiveness of heart failure disease management programs across 3 scenarios demonstrate how the model can be used to design a program in which short-term improvements in functioning and use of evidence-based treatments are sufficient to demonstrate good long-term value to the health care system. Conclusion The TEAM-HF Cost-Effectiveness Model provides researchers and providers with a tool for conducting long-term cost-effectiveness analyses of disease management programs in heart failure. PMID:26542504

Mapping of a quantitative trait locus for resistance against infectious salmon anaemia in Atlantic salmon (Salmo Salar): comparing survival analysis with analysis on affected/resistant data

PubMed Central

Moen, Thomas; Sonesson, Anna K; Hayes, Ben; Lien, Sigbjørn; Munck, Hege; Meuwissen, Theo HE

2007-01-01

Background Infectious Salmon Anaemia (ISA) is a viral disease affecting farmed Atlantic salmon (Salmo salar) worldwide. The identification of Quantitative Trait Loci (QTL) affecting resistance to the disease could improve our understanding of the genetics underlying the trait and provide a means for Marker-Assisted Selection. We previously performed a genome scan on commercial Atlantic salmon families challenge tested for ISA resistance, identifying several putative QTL. In the present study, we set out to validate the strongest of these QTL in a larger family material coming from the same challenge test, and to determine the position of the QTL by interval mapping. We also wanted to explore different ways of performing QTL analysis within a survival analysis framework (i.e. using time-to-event data), and to compare results using survival analysis with results from analysis on the dichotomous trait 'affected/resistant'. Results The QTL, located on Atlantic salmon linkage group 8 (following SALMAP notation), was confirmed in the new data set. Its most likely position was at a marker cluster containing markers BHMS130, BHMS170 and BHMS553. Significant segregation distortion was observed in the same region, but was shown to be unrelated to the QTL. A maximum likelihood procedure for identifying QTL, based on the Cox proportional hazard model, was developed. QTL mapping was also done using the Haley-Knott method (affected/resistant data), and within a variance-component framework (affected/resistant data and time-to-event data). In all cases, analysis using affected/resistant data gave stronger evidence for a QTL than did analysis using time-to-event data. Conclusion A QTL for resistance to Infectious Salmon Anaemia in Atlantic salmon was validated in this study, and its more precise location on linkage group eight was determined. The QTL explained 6% of the phenotypic variation in resistance to the disease. The linkage group also displayed significant segregation distortion. Survival models proved in this case not to be more suitable than models based on the dichotomous trait 'affected/resistant' for analysing the data. PMID:17697344

High lncRNA H19 expression as prognostic indicator: data mining in female cancers and polling analysis in non-female cancers

PubMed Central

Peng, Li; Liu, Zhao-Yang; Li, Wen-Ling; Zhang, Chao-Yang; Zhang, Ya-Qin; Pan, Xi; Chen, Jun; Li, Yue-Hui

2017-01-01

Upregulation of lncRNA H19 expression is associated with an unfavorable prognosis in some cancers. However, the prognostic value of H19 in female-specific cancers has remained uncharacterized. In this study, the prognostic power of high H19 expression in female cancer patients from the TCGA datasets was analyzed using Kaplan-Meier survival curves and Cox's proportional hazard modeling. In addition, in a meta-analysis of non-female cancer patients from TCGA datasets and 12 independent studies, hazard ratios (HRs) with 95% confidence interval (CI) for overall survival (OS) and disease-free survival (DFS)/relapse-free survival (RFS)/metastasis-free survival (MFS)/progression-free survival (PFS) were pooled to assess the prognostic value of high H19 expression. Kaplan-Meier analysis revealed that patients with uterine corpus cancer and higher H19 expression had a shorter OS (HR=2.710, p<0.05), while females with cervical cancer and increased H19 expression had a shorter RFS (HR=2.261, p<0.05). Multivariate Cox regression analysis showed that high H19 expression could independently predict a poorer prognosis in cervical cancer patients (HR=4.099, p<0.05). In the meta-analysis, patients with high H19 expression showed a poorer outcome in non-female cancer (p<0.05). These results suggest that high lncRNA H19 expression is predictive of an unfavorable prognosis in two female cancers (uterine corpus endometrioid cancer and cervical cancer) as well as in non-female cancer patients. PMID:27926484

High lncRNA H19 expression as prognostic indicator: data mining in female cancers and polling analysis in non-female cancers.

PubMed

Peng, Li; Yuan, Xiao-Qing; Liu, Zhao-Yang; Li, Wen-Ling; Zhang, Chao-Yang; Zhang, Ya-Qin; Pan, Xi; Chen, Jun; Li, Yue-Hui; Li, Guan-Cheng

2017-01-03

Upregulation of lncRNA H19 expression is associated with an unfavorable prognosis in some cancers. However, the prognostic value of H19 in female-specific cancers has remained uncharacterized. In this study, the prognostic power of high H19 expression in female cancer patients from the TCGA datasets was analyzed using Kaplan-Meier survival curves and Cox's proportional hazard modeling. In addition, in a meta-analysis of non-female cancer patients from TCGA datasets and 12 independent studies, hazard ratios (HRs) with 95% confidence interval (CI) for overall survival (OS) and disease-free survival (DFS)/relapse-free survival (RFS)/metastasis-free survival (MFS)/progression-free survival (PFS) were pooled to assess the prognostic value of high H19 expression. Kaplan-Meier analysis revealed that patients with uterine corpus cancer and higher H19 expression had a shorter OS (HR=2.710, p<0.05), while females with cervical cancer and increased H19 expression had a shorter RFS (HR=2.261, p<0.05). Multivariate Cox regression analysis showed that high H19 expression could independently predict a poorer prognosis in cervical cancer patients (HR=4.099, p<0.05). In the meta-analysis, patients with high H19 expression showed a poorer outcome in non-female cancer (p<0.05). These results suggest that high lncRNA H19 expression is predictive of an unfavorable prognosis in two female cancers (uterine corpus endometrioid cancer and cervical cancer) as well as in non-female cancer patients.

Impact and economic evaluation of a novel HIV service delivery model in rural Malawi.

PubMed

McBain, Ryan K; Petersen, Elizabeth; Tophof, Nora; Dunbar, Elizabeth L; Kalanga, Noel; Nazimera, Lawrence; Mganga, Andrew; Dullie, Luckson; Mukherjee, Joia; Wroe, Emily B

2017-09-10

We performed an impact and cost-effectiveness analysis of a novel HIV service delivery model in a high prevalence, remote district of Malawi with a population of 143 800 people. A population-based retrospective analysis of 1-year survival rates among newly enrolled HIV-positive patients at 682 health facilities throughout Malawi, comparing facilities implementing the service delivery model (n = 13) and those implementing care-as-usual (n = 669). Through district-level health surveillance data, we evaluated 1-year survival rates among HIV patients newly enrolled between July 2013 and June 2014 - representing 129 938 patients in care across 682 health facilities - using a multilevel modeling framework. The model, focused on social determinants of health, was implemented throughout Neno District at 13 facilities and compared with facilities in all other districts. Activity-based costing was used to annualize financial and economic costs from a societal perspective. Incremental cost-effectiveness ratios were expressed as quality-adjusted life-years gained. The national average 1-year survival rate for newly enrolled antiretroviral therapy clients was 78.9%: this rate was 87.9% in Neno District, compared with 78.8% across all other districts in Malawi (P < 0.001; 95% confidence interval: 0.079-0.104). The economic cost of receiving care in Neno district (n = 6541 patients) was $317/patient/year, compared with an estimated $219/patient in other districts. This translated to $906 per quality-adjusted life-year gained. Neno District's comprehensive model of care, featuring a strong focus on the community, is $98 more expensive per capita per annum but demonstrates superior 1-year survival rates, despite its remote location. Moreover, it should be considered cost-effective by traditional international standards.

Cure models for the analysis of time-to-event data in cancer studies.

PubMed

Jia, Xiaoyu; Sima, Camelia S; Brennan, Murray F; Panageas, Katherine S

2013-11-01

In settings when it is biologically plausible that some patients are cured after definitive treatment, cure models present an alternative to conventional survival analysis. Cure models can inform on the group of patients cured, by estimating the probability of cure, and identifying factors that influence it; while simultaneously focusing on time to recurrence and associated factors for the remaining patients. © 2013 Wiley Periodicals, Inc.

Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions.

PubMed

Fan, Ruzong; Wang, Yifan; Yan, Qi; Ding, Ying; Weeks, Daniel E; Lu, Zhaohui; Ren, Haobo; Cook, Richard J; Xiong, Momiao; Swaroop, Anand; Chew, Emily Y; Chen, Wei

2016-02-01

Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, here we develop Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT), which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example. © 2016 WILEY PERIODICALS, INC.

Gene-based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions

PubMed Central

Fan, Ruzong; Wang, Yifan; Yan, Qi; Ding, Ying; Weeks, Daniel E.; Lu, Zhaohui; Ren, Haobo; Cook, Richard J; Xiong, Momiao; Swaroop, Anand; Chew, Emily Y.; Chen, Wei

2015-01-01

Summary Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, we develop here Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT) which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example. PMID:26782979

Disentangling density-dependent dynamics using full annual cycle models and Bayesian model weight updating

USGS Publications Warehouse

Robinson, Orin J.; McGowan, Conor P.; Devers, Patrick K.

2017-01-01

Density dependence regulates populations of many species across all taxonomic groups. Understanding density dependence is vital for predicting the effects of climate, habitat loss and/or management actions on wild populations. Migratory species likely experience seasonal changes in the relative influence of density dependence on population processes such as survival and recruitment throughout the annual cycle. These effects must be accounted for when characterizing migratory populations via population models.To evaluate effects of density on seasonal survival and recruitment of a migratory species, we used an existing full annual cycle model framework for American black ducks Anas rubripes, and tested different density effects (including no effects) on survival and recruitment. We then used a Bayesian model weight updating routine to determine which population model best fit observed breeding population survey data between 1990 and 2014.The models that best fit the survey data suggested that survival and recruitment were affected by density dependence and that density effects were stronger on adult survival during the breeding season than during the non-breeding season.Analysis also suggests that regulation of survival and recruitment by density varied over time. Our results showed that different characterizations of density regulations changed every 8–12 years (three times in the 25-year period) for our population.Synthesis and applications. Using a full annual cycle, modelling framework and model weighting routine will be helpful in evaluating density dependence for migratory species in both the short and long term. We used this method to disentangle the seasonal effects of density on the continental American black duck population which will allow managers to better evaluate the effects of habitat loss and potential habitat management actions throughout the annual cycle. The method here may allow researchers to hone in on the proper form and/or strength of density dependence for use in models for conservation recommendations.

Chemotherapy-induced neutropenia and the prognosis of colorectal cancer: a meta-analysis of cohort studies.

PubMed

Tan, XiangZhou; Wen, QiaoCheng; Wang, Ran; Chen, ZhiKang

2017-11-01

Recently, there has been a controversial discussion about the prognostic value of chemotherapy-induced neutropenia (CIN) in colorectal cancer patients. Thus, a meta-analysis was conducted to determine the relationship between CIN and the prognosis of colorectal cancer patients. We searched the PubMed, EMBASE, and Cochrane library databases to identify studies evaluating the association between CIN and colorectal cancer prognosis. Pooled random/fixed effect models were used to calculate pooled hazard ratios (HRs) and 95% confidence intervals (CIs) to assess the association. Eight studies were selected for the meta-analysis, for a total of 2,745 patients. There was significant improved survival among colorectal cancer patients with CIN (HR = 0.62, 95% CI = 0.47-0.76). However, significant heterogeneity was found (p = 0.000, Ι 2  = 75.0%). Through subgroup analysis, we could greatly eliminate the heterogeneity and found that neutropenia was associated with better survival in stage IV colorectal cancer patients, no matter the HR calculated by overall survival (OS) or progression-free survival (PFS). Meanwhile, the prognostic value of neutropenia in stage II/III colorectal cancer can be found when the HR is calculated by disease-free survival (DFS). Additionally, we observed significant differences after stratification according to various tumor stages, endpoints, and the use of G-CSF. Our results which, based on a cohort study, indicate that CIN is associated with improved survival in patients with colorectal cancer. However, further randomized controlled trials are warranted.

Estimating piecewise exponential frailty model with changing prior for baseline hazard function

NASA Astrophysics Data System (ADS)

Thamrin, Sri Astuti; Lawi, Armin

2016-02-01

Piecewise exponential models provide a very flexible framework for modelling univariate survival data. It can be used to estimate the effects of different covariates which are influenced by the survival data. Although in a strict sense it is a parametric model, a piecewise exponential hazard can approximate any shape of a parametric baseline hazard. In the parametric baseline hazard, the hazard function for each individual may depend on a set of risk factors or explanatory variables. However, it usually does not explain all such variables which are known or measurable, and these variables become interesting to be considered. This unknown and unobservable risk factor of the hazard function is often termed as the individual's heterogeneity or frailty. This paper analyses the effects of unobserved population heterogeneity in patients' survival times. The issue of model choice through variable selection is also considered. A sensitivity analysis is conducted to assess the influence of the prior for each parameter. We used the Markov Chain Monte Carlo method in computing the Bayesian estimator on kidney infection data. The results obtained show that the sex and frailty are substantially associated with survival in this study and the models are relatively quite sensitive to the choice of two different priors.

Long non-coding RNA CCAT1 as a diagnostic and prognostic molecular marker in various cancers: a meta-analysis.

PubMed

Zhang, Zhihui; Xie, Haibiao; Liang, Daqiang; Huang, Lanbing; Liang, Feiguo; Qi, Qiang; Yang, Xinjian

2018-05-04

Long non-coding RNA colon cancer-associated transcript-1 (CCAT1) is newly found to be related with diagnoses and prognosis of cancer. This meta-analysis was performed to investigate the relationship between CCAT1 expression and clinical parameters, including survival condition, lymph node metastasis and tumor node metastasis grade. The primary literatures were collected through initial search criteria from electronic databases, including PubMed, OVID Evidence-based medicine Reviews and others (up to May 12, 2017). Eligible studies were identified and selected by the inclusion and exclusion criteria. Data was extracted and computed into Hazard ratio (HR) for the assessment of overall survival, subgroup analyses were prespecified based on the digestive tract cancer or others. Analysis of different CCAT1 expression related with lymph node metastasis or tumor node metastasis grade was conducted. Risk of bias was assessed by the Newcastle-Ottawa Scale. 9 studies were included. This meta-analysis showed that high CCAT1 expression level was related to poor overall survival, the pooled HR was 2.42 (95% confidence interval, CI: 1.86-3.16; P < 0.001; fix- effects model), similarly in the cancer type subgroups: digestive tract cancer (HR, 2.42; 95% CI, 1.79-3.29; P < 0.001; fix- effects model) and others (HR, 2.42; 95% CI, 1.42-4.13; P = 0.001; fix- effects model). The analysis showed that high CCAT1 was strongly related to positive lymph node metastasis (Odds ratio, OR: 3.24; 95% CI, 2.04-5.16; P < 0.001; fix- effects model), high tumor node metastasis stage (OR, 3.87; 95% CI, 2.53-5.92; P < 0.001; fix- effects model). In conclusion, this meta-analysis revealed that CCAT1 had potential as a diagnostic and prognostic biomarker in various cancers.

National Cancer Database Analysis of Proton Versus Photon Radiation Therapy in Non-Small Cell Lung Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Higgins, Kristin A., E-mail: kristin.higgins@emory.edu; Winship Cancer Institute, Emory University, Atlanta, Georgia; O'Connell, Kelli

Purpose: To analyze outcomes and predictors associated with proton radiation therapy for non-small cell lung cancer (NSCLC) in the National Cancer Database. Methods and Materials: The National Cancer Database was queried to capture patients with stage I-IV NSCLC treated with thoracic radiation from 2004 to 2012. A logistic regression model was used to determine the predictors for utilization of proton radiation therapy. The univariate and multivariable association with overall survival were assessed by Cox proportional hazards models along with log–rank tests. A propensity score matching method was implemented to balance baseline covariates and eliminate selection bias. Results: A total of 243,822more » patients (photon radiation therapy: 243,474; proton radiation therapy: 348) were included in the analysis. Patients in a ZIP code with a median income of <$46,000 per year were less likely to receive proton treatment, with the income cohort of $30,000 to $35,999 least likely to receive proton therapy (odds ratio 0.63 [95% confidence interval (CI) 0.44-0.90]; P=.011). On multivariate analysis of all patients, non-proton therapy was associated with significantly worse survival compared with proton therapy (hazard ratio 1.21 [95% CI 1.06-1.39]; P<.01). On propensity matched analysis, proton radiation therapy (n=309) was associated with better 5-year overall survival compared with non-proton radiation therapy (n=1549), 22% versus 16% (P=.025). For stage II and III patients, non-proton radiation therapy was associated with worse survival compared with proton radiation therapy (hazard ratio 1.35 [95% CI 1.10-1.64], P<.01). Conclusions: Thoracic radiation with protons is associated with better survival in this retrospective analysis; further validation in the randomized setting is needed to account for any imbalances in patient characteristics, including positron emission tomography–computed tomography staging.« less

Repair-dependent cell radiation survival and transformation: an integrated theory.

PubMed

Sutherland, John C

2014-09-07

The repair-dependent model of cell radiation survival is extended to include radiation-induced transformations. The probability of transformation is presumed to scale with the number of potentially lethal damages that are repaired in a surviving cell or the interactions of such damages. The theory predicts that at doses corresponding to high survival, the transformation frequency is the sum of simple polynomial functions of dose; linear, quadratic, etc, essentially as described in widely used linear-quadratic expressions. At high doses, corresponding to low survival, the ratio of transformed to surviving cells asymptotically approaches an upper limit. The low dose fundamental- and high dose plateau domains are separated by a downwardly concave transition region. Published transformation data for mammalian cells show the high-dose plateaus predicted by the repair-dependent model for both ultraviolet and ionizing radiation. For the neoplastic transformation experiments that were analyzed, the data can be fit with only the repair-dependent quadratic function. At low doses, the transformation frequency is strictly quadratic, but becomes sigmodial over a wider range of doses. Inclusion of data from the transition region in a traditional linear-quadratic analysis of neoplastic transformation frequency data can exaggerate the magnitude of, or create the appearance of, a linear component. Quantitative analysis of survival and transformation data shows good agreement for ultraviolet radiation; the shapes of the transformation components can be predicted from survival data. For ionizing radiations, both neutrons and x-rays, survival data overestimate the transforming ability for low to moderate doses. The presumed cause of this difference is that, unlike UV photons, a single x-ray or neutron may generate more than one lethal damage in a cell, so the distribution of such damages in the population is not accurately described by Poisson statistics. However, the complete sigmodial dose-response data for neoplastic transformations can be fit using the repair-dependent functions with all parameters determined only from transformation frequency data.

Stochastic response surface methodology: A study in the human health area

DOE Office of Scientific and Technical Information (OSTI.GOV)

Oliveira, Teresa A., E-mail: teresa.oliveira@uab.pt; Oliveira, Amílcar, E-mail: amilcar.oliveira@uab.pt; Centro de Estatística e Aplicações, Universidade de Lisboa

2015-03-10

In this paper we review Stochastic Response Surface Methodology as a tool for modeling uncertainty in the context of Risk Analysis. An application in the survival analysis in the breast cancer context is implemented with R software.

Outcome and prognostic factors in metastatic urothelial carcinoma patients receiving second-line chemotherapy: an analysis of real-world clinical practice data in Japan.

PubMed

Matsumoto, Ryuji; Abe, Takashige; Ishizaki, Junji; Kikuchi, Hiroshi; Harabayashi, Toru; Minami, Keita; Sazawa, Ataru; Mochizuki, Tango; Akino, Tomoshige; Murakumo, Masashi; Osawa, Takahiro; Maruyama, Satoru; Murai, Sachiyo; Shinohara, Nobuo

2018-06-25

The objective of the present study was to investigate the survival outcome and prognostic factors of metastatic urothelial carcinoma patients treated with second-line systemic chemotherapy in real-world clinical practice. Overall, 114 patients with metastatic urothelial carcinoma undergoing second-line systemic chemotherapy were included in this retrospective analysis. The dominant second-line chemotherapy was a paclitaxel-based combination regimen (60%, 68/114). We assessed the progression-free survival and overall survival times using the Kaplan-Meier method. The Cox proportional hazards model was applied to identify the factors affecting overall survival. The median progression-free survival and overall survival times were 4 and 9 months, respectively. In the multivariate analysis, an Eastern Cooperative Oncology Group performance status score greater than 0 at presentation, C-reactive protein level ≧1 mg/dl and poor response to prior chemotherapy were adverse prognostic indicators. Patients with 0, 1, 2 and 3 of those risk factors had a median overall survival of 17, 12, 7 and 3 months, respectively. The Eastern Cooperative Oncology Group performance status at presentation, C-reactive protein level and response to prior chemotherapy were prognostic factors for metastatic urothelial carcinoma patients undergoing second-line chemotherapy. In the future, this information might help guide the choice of salvage treatment, such as second-line chemotherapy or immune checkpoint inhibitors, after the failure of first-line chemotherapy.

Estimates of annual survival probabilities for adult Florida manatees (Trichechus manatus latirostris)

USGS Publications Warehouse

Langtimm, C.A.; O'Shea, T.J.; Pradel, R.; Beck, C.A.

1998-01-01

The population dynamics of large, long-lived mammals are particularly sensitive to changes in adult survival. Understanding factors affecting survival patterns is therefore critical for developing and testing theories of population dynamics and for developing management strategies aimed at preventing declines or extinction in such taxa. Few studies have used modern analytical approaches for analyzing variation and testing hypotheses about survival probabilities in large mammals. This paper reports a detailed analysis of annual adult survival in the Florida manatee (Trichechus manatus latirostris), an endangered marine mammal, based on a mark-recapture approach. Natural and boat-inflicted scars distinctively 'marked' individual manatees that were cataloged in a computer-based photographic system. Photo-documented resightings provided 'recaptures.' Using open population models, annual adult-survival probabilities were estimated for manatees observed in winter in three areas of Florida: Blue Spring, Crystal River, and the Atlantic coast. After using goodness-of-fit tests in Program RELEASE to search for violations of the assumptions of mark-recapture analysis, survival and sighting probabilities were modeled under several different biological hypotheses with Program SURGE. Estimates of mean annual probability of sighting varied from 0.948 for Blue Spring to 0.737 for Crystal River and 0.507 for the Atlantic coast. At Crystal River and Blue Spring, annual survival probabilities were best estimated as constant over the study period at 0.96 (95% CI = 0.951-0.975 and 0.900-0.985, respectively). On the Atlantic coast, where manatees are impacted more by human activities, annual survival probabilities had a significantly lower mean estimate of 0.91 (95% CI = 0.887-0.926) and varied unpredictably over the study period. For each study area, survival did not differ between sexes and was independent of relative adult age. The high constant adult-survival probabilities estimated for manatees in the Blue Spring and Crystal River areas were consistent with current mammalian life history theory and other empirical data available for large, long-lived mammals. Adult survival probabilities in these areas appeared high enough to maintain growing populations if other traits such as reproductive rates and juvenile survival were also sufficiently high lower and variable survival rates on the Atlantic coast are cause for concern.

Multivariate meta-analysis using individual participant data.

PubMed

Riley, R D; Price, M J; Jackson, D; Wardle, M; Gueyffier, F; Wang, J; Staessen, J A; White, I R

2015-06-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. © 2014 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

Mediation Analysis with Survival Outcomes: Accelerated Failure Time vs. Proportional Hazards Models

PubMed Central

Gelfand, Lois A.; MacKinnon, David P.; DeRubeis, Robert J.; Baraldi, Amanda N.

2016-01-01

Objective: Survival time is an important type of outcome variable in treatment research. Currently, limited guidance is available regarding performing mediation analyses with survival outcomes, which generally do not have normally distributed errors, and contain unobserved (censored) events. We present considerations for choosing an approach, using a comparison of semi-parametric proportional hazards (PH) and fully parametric accelerated failure time (AFT) approaches for illustration. Method: We compare PH and AFT models and procedures in their integration into mediation models and review their ability to produce coefficients that estimate causal effects. Using simulation studies modeling Weibull-distributed survival times, we compare statistical properties of mediation analyses incorporating PH and AFT approaches (employing SAS procedures PHREG and LIFEREG, respectively) under varied data conditions, some including censoring. A simulated data set illustrates the findings. Results: AFT models integrate more easily than PH models into mediation models. Furthermore, mediation analyses incorporating LIFEREG produce coefficients that can estimate causal effects, and demonstrate superior statistical properties. Censoring introduces bias in the coefficient estimate representing the treatment effect on outcome—underestimation in LIFEREG, and overestimation in PHREG. With LIFEREG, this bias can be addressed using an alternative estimate obtained from combining other coefficients, whereas this is not possible with PHREG. Conclusions: When Weibull assumptions are not violated, there are compelling advantages to using LIFEREG over PHREG for mediation analyses involving survival-time outcomes. Irrespective of the procedures used, the interpretation of coefficients, effects of censoring on coefficient estimates, and statistical properties should be taken into account when reporting results. PMID:27065906

Cystic Fibrosis Associated with Worse Survival After Liver Transplantation.

PubMed

Black, Sylvester M; Woodley, Frederick W; Tumin, Dmitry; Mumtaz, Khalid; Whitson, Bryan A; Tobias, Joseph D; Hayes, Don

2016-04-01

Survival in cystic fibrosis patients after liver transplantation and liver-lung transplantation is not well studied. To discern survival rates after liver transplantation and liver-lung transplantation in patients with and without cystic fibrosis. The United Network for Organ Sharing database was queried from 1987 to 2013. Univariate Cox proportional hazards, multivariate Cox models, and propensity score matching were performed. Liver transplant and liver-lung transplant were performed in 212 and 53 patients with cystic fibrosis, respectively. Univariate Cox proportional hazards regression identified lower survival in cystic fibrosis after liver transplant compared to a reference non-cystic fibrosis liver transplant cohort (HR 1.248; 95 % CI 1.012, 1.541; p = 0.039). Supplementary analysis found graft survival was similar across the 3 recipient categories (log-rank test: χ(2) 2.68; p = 0.262). Multivariate Cox models identified increased mortality hazard among cystic fibrosis patients undergoing liver transplantation (HR 2.439; 95 % CI 1.709, 3.482; p < 0.001) and liver-lung transplantation (HR 2.753; 95 % CI 1.560, 4.861; p < 0.001). Propensity score matching of cystic fibrosis patients undergoing liver transplantation to non-cystic fibrosis controls identified a greater mortality hazard in the cystic fibrosis cohort using a Cox proportional hazards model stratified on matched pairs (HR 3.167; 95 % CI 1.265, 7.929, p = 0.014). Liver transplantation in cystic fibrosis is associated with poorer long-term patient survival compared to non-cystic fibrosis patients, although the difference is not due to graft survival.

Cigarette smoking is associated with adverse survival among women with ovarian cancer: Results from a pooled analysis of 19 studies.

PubMed

Praestegaard, Camilla; Jensen, Allan; Jensen, Signe M; Nielsen, Thor S S; Webb, Penelope M; Nagle, Christina M; DeFazio, Anna; Høgdall, Estrid; Rossing, Mary Anne; Doherty, Jennifer A; Wicklund, Kristine G; Goodman, Marc T; Modugno, Francesmary; Moysich, Kirsten; Ness, Roberta B; Edwards, Robert; Matsuo, Keitaro; Hosono, Satoyo; Goode, Ellen L; Winham, Stacey J; Fridley, Brooke L; Cramer, Daniel W; Terry, Kathryn L; Schildkraut, Joellen M; Berchuck, Andrew; Bandera, Elisa V; Paddock, Lisa E; Massuger, Leon F; Wentzensen, Nicolas; Pharoah, Paul; Song, Honglin; Whittemore, Alice; McGuire, Valerie; Sieh, Weiva; Rothstein, Joseph; Anton-Culver, Hoda; Ziogas, Argyrios; Menon, Usha; Gayther, Simon A; Ramus, Susan J; Gentry-Maharaj, Alexandra; Wu, Anna H; Pearce, Celeste L; Pike, Malcolm; Lee, Alice W; Sutphen, Rebecca; Chang-Claude, Jenny; Risch, Harvey A; Kjaer, Susanne K

2017-06-01

Cigarette smoking is associated with an increased risk of developing mucinous ovarian tumors but whether it is associated with ovarian cancer survival overall or for the different histotypes is unestablished. Furthermore, it is unknown whether the association between cigarette smoking and survival differs according to strata of ovarian cancer stage at diagnosis. In a large pooled analysis, we evaluated the association between various measures of cigarette smoking and survival among women with epithelial ovarian cancer. We obtained data from 19 case-control studies in the Ovarian Cancer Association Consortium (OCAC), including 9,114 women diagnosed with ovarian cancer. Cox regression models were used to estimate adjusted study-specific hazard ratios (HRs), which were combined into pooled hazard ratios (pHR) with corresponding 95% confidence intervals (CIs) under random effects models. Overall, 5,149 (57%) women died during a median follow-up period of 7.0 years. Among women diagnosed with ovarian cancer, both current (pHR = 1.17, 95% CI: 1.08-1.28) and former smokers (pHR = 1.10, 95% CI: 1.02-1.18) had worse survival compared with never smoking women. In histotype-stratified analyses, associations were observed for mucinous (current smoking: pHR = 1.91, 95% CI: 1.01-3.65) and serous histotypes (current smoking: pHR = 1.11, 95% CI: 1.00-1.23; former smoking: pHR = 1.12, 95% CI: 1.04-1.20). Further, our results suggested that current smoking has a greater impact on survival among women with localized than disseminated disease. The identification of cigarette smoking as a modifiable factor associated with survival has potential clinical importance as a focus area to improve ovarian cancer prognosis. © 2017 UICC.

Cigarette smoking is associated with adverse survival among women with ovarian cancer: Results from a pooled analysis of 19 studies

PubMed Central

Præstegaard, Camilla; Jensen, Allan; Jensen, Signe M.; Nielsen, Thor S. S.; Webb, Penelope M.; Nagle, Christina M.; DeFazio, Anna; Høgdall, Estrid; Rossing, Mary Anne; Doherty, Jennifer A.; Wicklund, Kristine G.; Goodman, Marc T.; Modugno, Francesmary; Moysich, Kirsten; Ness, Roberta B.; Edwards, Robert; Matsuo, Keitaro; Hosono, Satoyo; Goode, Ellen L.; Winham, Stacey J; Fridley, Brooke L.; Cramer, Daniel W.; Terry, Kathryn L.; Schildkraut, Joellen M.; Berchuck, Andrew; Bandera, Elisa V.; Paddock, Lisa E.; Massuger, Leon F.; Wentzensen, Nicolas; Pharoah, Paul; Song, Honglin; Whittemore, Alice; McGuire, Valerie; Sieh, Weiva; Rothstein, Joseph; Anton-Culver, Hoda; Ziogas, Argyrios; Menon, Usha; Gayther, Simon A.; Ramus, Susan J.; Gentry-Maharaj, Alexandra; Wu, Anna H.; Pearce, Celeste L.; Pike, Malcolm; Lee, Alice W.; Sutphen, Rebecca; Chang-Claude, Jenny; Risch, Harvey A.; Kjaer, Susanne K.

2017-01-01

Cigarette smoking is associated with an increased risk of developing mucinous ovarian tumors but whether it is associated with ovarian cancer survival overall or for the different histotypes is unestablished. Furthermore, it is unknown whether the association between cigarette smoking and survival differs according to strata of ovarian cancer stage at diagnosis. In a large pooled analysis, we evaluated the association between various measures of cigarette smoking and survival among women with epithelial ovarian cancer. We obtained data from 19 case-control studies in the Ovarian Cancer Association Consortium (OCAC), including 9,114 women diagnosed with ovarian cancer. Cox regression models were used to estimate adjusted study-specific hazard ratios (HRs), which were combined into pooled hazard ratios (pHR) with corresponding 95% confidence intervals (CIs) under random effects models. Overall, 5,149 (57%) women died during a median follow-up period of 7.0 years. Among women diagnosed with ovarian cancer, both current (pHR = 1.17, 95% CI: 1.08–1.28) and former smokers (pHR = 1.10, 95% CI: 1.02–1.18) had worse survival compared with never smoking women. In histotype-stratified analyses, associations were observed for mucinous (current smoking: pHR = 1.91, 95% CI: 1.01–3.65) and serous histotypes (current smoking: pHR = 1.11, 95% CI: 1.00–1.23; former smoking: pHR = 1.12, 95% CI: 1.04–1.20). Further, our results suggested that current smoking has a greater impact on survival among women with localized than disseminated disease. The identification of cigarette smoking as a modifiable factor associated with survival has potential clinical importance as a focus area to improve ovarian cancer prognosis. PMID:28063166

Analysis of survival in breast cancer patients by using different parametric models

NASA Astrophysics Data System (ADS)

Enera Amran, Syahila; Asrul Afendi Abdullah, M.; Kek, Sie Long; Afiqah Muhamad Jamil, Siti

2017-09-01

In biomedical applications or clinical trials, right censoring was often arising when studying the time to event data. In this case, some individuals are still alive at the end of the study or lost to follow up at a certain time. It is an important issue to handle the censoring data in order to prevent any bias information in the analysis. Therefore, this study was carried out to analyze the right censoring data with three different parametric models; exponential model, Weibull model and log-logistic models. Data of breast cancer patients from Hospital Sultan Ismail, Johor Bahru from 30 December 2008 until 15 February 2017 was used in this study to illustrate the right censoring data. Besides, the covariates included in this study are the time of breast cancer infection patients survive t, age of each patients X1 and treatment given to the patients X2 . In order to determine the best parametric models in analysing survival of breast cancer patients, the performance of each model was compare based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood value using statistical software R. When analysing the breast cancer data, all three distributions were shown consistency of data with the line graph of cumulative hazard function resembles a straight line going through the origin. As the result, log-logistic model was the best fitted parametric model compared with exponential and Weibull model since it has the smallest value in AIC and BIC, also the biggest value in log-likelihood.

Application of the BAR score as a predictor of short- and long-term survival in liver transplantation patients.

PubMed

de Campos Junior, Ivan Dias; Stucchi, Raquel Silveira Bello; Udo, Elisabete Yoko; Boin, Ilka de Fátima Santana Ferreira

2015-01-01

The balance of risk (BAR) is a prediction system after liver transplantation. To assess the BAR system, a retrospective observational study was performed in 402 patients who had transplant surgery between 1997 and 2012. The BAR score was computed for each patient. Receiver operating characteristic curve analysis with the Hosmer-Lemeshow test was used to calculate sensitivity, specificity, and model calibration. The cutoff value with the best Youden index was selected. Statistical analysis employed the Kaplan-Meier method (log-rank test) for survival, the Mann-Whitney test for group comparison, and multiple logistic regression analysis. 3-month survival was 46% for BAR ≥ 11 and 77% for BAR <11 (p = 0.001); 12-month survival was 44% for BAR ≥ 11 and 69% for BAR <11 (p = 0.001). Factors of survival <3 months were BAR ≥ 11 [odds ratio (OR) 3.08; 95% confidence interval (CI) 1.75-5.42; p = 0.001] and intrasurgical use of packed red blood cells (RBC) above 6 units (OR 4.49; 95% CI 2.73-7.39; p = 0.001). For survival <12 months, factors were BAR ≥ 11 (OR 2.94; 95% CI 1.67-5.16; p = 0.001) and RBC >6 units (OR 2.99; 95% CI 1.92-4.64; p = 0.001). Our study contributes to the incorporation of the BAR system into Brazilian transplantation centers.

Association between response rates and survival outcomes in patients with newly diagnosed multiple myeloma. A systematic review and meta-regression analysis.

PubMed

Mainou, Maria; Madenidou, Anastasia-Vasiliki; Liakos, Aris; Paschos, Paschalis; Karagiannis, Thomas; Bekiari, Eleni; Vlachaki, Efthymia; Wang, Zhen; Murad, Mohammad Hassan; Kumar, Shaji; Tsapas, Apostolos

2017-06-01

We performed a systematic review and meta-regression analysis of randomized control trials to investigate the association between response to initial treatment and survival outcomes in patients with newly diagnosed multiple myeloma (MM). Response outcomes included complete response (CR) and the combined outcome of CR or very good partial response (VGPR), while survival outcomes were overall survival (OS) and progression-free survival (PFS). We used random-effect meta-regression models and conducted sensitivity analyses based on definition of CR and study quality. Seventy-two trials were included in the systematic review, 63 of which contributed data in meta-regression analyses. There was no association between OS and CR in patients without autologous stem cell transplant (ASCT) (regression coefficient: .02, 95% confidence interval [CI] -0.06, 0.10), in patients undergoing ASCT (-.11, 95% CI -0.44, 0.22) and in trials comparing ASCT with non-ASCT patients (.04, 95% CI -0.29, 0.38). Similarly, OS did not correlate with the combined metric of CR or VGPR, and no association was evident between response outcomes and PFS. Sensitivity analyses yielded similar results. This meta-regression analysis suggests that there is no association between conventional response outcomes and survival in patients with newly diagnosed MM. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Impact of age on the survival of patients with liver cancer: an analysis of 27,255 patients in the SEER database.

PubMed

Zhang, Wenjie; Sun, Beicheng

2015-01-20

The risk of liver cancer (LC) is regarded as age dependent. However, the influence of age on its prognosis is controversial. The aim of our study was to compare the long-term survival of younger versus older patients with LC. In this retrospective study, we searched Surveillance, Epidemiology, and End-RESULTS (SEER) population-based data and identified 27,255 patients diagnosed with LC between 1988 and 2003. These patients were categorized into younger (45 years and under) and older age (over 45 years of age) groups. Five-year cancer specific survival data was obtained. Kaplan-Meier methods and multivariable Cox regression models were used to analyze long-term survival outcomes and risk factors. There were significant differences between groups with regards to pathologic grading, histologic type, stage, and tumor size (p < 0.001). The 5-year liver cancer specific survival (LCSS) rates in the younger and older age groups were 14.5% and 8.4%, respectively (p < 0.001 by univariate and multivariate analysis). A stratified analysis of age on cancer survival showed only localized and regional stages to be validated as independent predictors, but not for advanced stages. Compared to older patients, younger patients with LC have a higher LCSS after surgery, despite the poorer biological behavior of this carcinoma.

["That flesh, pink and perishable": analysis of disease-free survival analysis in breast cancer in Gipuzkoa (Spain) in the presence of competing risks].

PubMed

Martínez-Camblor, Pablo; Larrañaga, Nerea; Sarasqueta, Cristina; Mitxelena, María José; Basterretxea, Mikel

2009-01-01

To analyze time of disease-free survival and relative survival in women diagnosed with breast cancer in the province of Gipuzkoa within the context of competing risks by assessing differences between the direct use of the Kaplan-Meier estimator and the multiple decrement method on the one hand, and relative survival on the other. All registered breast cancer cases in Gipuzkoa in 1995 and 1996 with stages other than stage IV were included. An 8-year follow-up for recurrence and a 10-year follow-up for survival were performed. Time of disease-free survival was studied by the multiple decrement model. Observed survival and survival corrected by the expected mortality in the population (relative survival) were also studied. Estimation of the probability of recurrence at 8 years with the multiple decrement method was 8.8% lower than that obtained with the Kaplan-Meier method. The difference between the observed and relative survival rates at 10 years was 10.8%. Both results show how, in this case, the Kaplan-Meier estimator overestimates both the probability of recurrence and that of mortality from the disease. Two issues are often overlooked when performing survival analyses: firstly, because of the lack of independence between survival time and censoring time, the results obtained by the Kaplan-Meier estimator are uninterpretable; secondly, it is an incontrovertible fact that one way or another, everyone causes failures. In this approach, survival analyses must take into account the probability of failure in the general population of reference. The results obtained in this study show that superficial use of the Kaplan Meier estimator overestimates both the probability of recurrence and that of mortality caused by the disease.

Adjusting survival estimates for premature transmitter failure: A case study from the Sacramento-San Joaquin Delta

USGS Publications Warehouse

Holbrook, Christopher M.; Perry, Russell W.; Brandes, Patricia L.; Adams, Noah S.

2013-01-01

In telemetry studies, premature tag failure causes negative bias in fish survival estimates because tag failure is interpreted as fish mortality. We used mark-recapture modeling to adjust estimates of fish survival for a previous study where premature tag failure was documented. High rates of tag failure occurred during the Vernalis Adaptive Management Plan’s (VAMP) 2008 study to estimate survival of fall-run Chinook salmon (Oncorhynchus tshawytscha) during migration through the San Joaquin River and Sacramento-San Joaquin Delta, California. Due to a high rate of tag failure, the observed travel time distribution was likely negatively biased, resulting in an underestimate of tag survival probability in this study. Consequently, the bias-adjustment method resulted in only a small increase in estimated fish survival when the observed travel time distribution was used to estimate the probability of tag survival. Since the bias-adjustment failed to remove bias, we used historical travel time data and conducted a sensitivity analysis to examine how fish survival might have varied across a range of tag survival probabilities. Our analysis suggested that fish survival estimates were low (95% confidence bounds range from 0.052 to 0.227) over a wide range of plausible tag survival probabilities (0.48–1.00), and this finding is consistent with other studies in this system. When tags fail at a high rate, available methods to adjust for the bias may perform poorly. Our example highlights the importance of evaluating the tag life assumption during survival studies, and presents a simple framework for evaluating adjusted survival estimates when auxiliary travel time data are available.

Dynamics of a macroscopic model characterizing mutualism of search engines and web sites

NASA Astrophysics Data System (ADS)

Wang, Yuanshi; Wu, Hong

2006-05-01

We present a model to describe the mutualism relationship between search engines and web sites. In the model, search engines and web sites benefit from each other while the search engines are derived products of the web sites and cannot survive independently. Our goal is to show strategies for the search engines to survive in the internet market. From mathematical analysis of the model, we show that mutualism does not always result in survival. We show various conditions under which the search engines would tend to extinction, persist or grow explosively. Then by the conditions, we deduce a series of strategies for the search engines to survive in the internet market. We present conditions under which the initial number of consumers of the search engines has little contribution to their persistence, which is in agreement with the results in previous works. Furthermore, we show novel conditions under which the initial value plays an important role in the persistence of the search engines and deduce new strategies. We also give suggestions for the web sites to cooperate with the search engines in order to form a win-win situation.

Genetic mixed linear models for twin survival data.

PubMed

Ha, Il Do; Lee, Youngjo; Pawitan, Yudi

2007-07-01

Twin studies are useful for assessing the relative importance of genetic or heritable component from the environmental component. In this paper we develop a methodology to study the heritability of age-at-onset or lifespan traits, with application to analysis of twin survival data. Due to limited period of observation, the data can be left truncated and right censored (LTRC). Under the LTRC setting we propose a genetic mixed linear model, which allows general fixed predictors and random components to capture genetic and environmental effects. Inferences are based upon the hierarchical-likelihood (h-likelihood), which provides a statistically efficient and unified framework for various mixed-effect models. We also propose a simple and fast computation method for dealing with large data sets. The method is illustrated by the survival data from the Swedish Twin Registry. Finally, a simulation study is carried out to evaluate its performance.

Yonsei nomogram: A predictive model of new-onset chronic kidney disease after on-clamp partial nephrectomy in patients with T1 renal tumors.

PubMed

Abdel Raheem, Ali; Shin, Tae Young; Chang, Ki Don; Santok, Glen Denmer R; Alenzi, Mohamed Jayed; Yoon, Young Eun; Ham, Won Sik; Han, Woong Kyu; Choi, Young Deuk; Rha, Koon Ho

2018-06-19

To develop a predictive nomogram for chronic kidney disease-free survival probability in the long term after partial nephrectomy. A retrospective analysis was carried out of 698 patients with T1 renal tumors undergoing partial nephrectomy at a tertiary academic institution. A multivariable Cox regression analysis was carried out based on parameters proven to have an impact on postoperative renal function. Patients with incomplete data, <12 months follow up and preoperative chronic kidney disease stage III or greater were excluded. The study end-points were to identify independent risk factors for new-onset chronic kidney disease development, as well as to construct a predictive model for chronic kidney disease-free survival probability after partial nephrectomy. The median age was 52 years, median tumor size was 2.5 cm and mean warm ischemia time was 28 min. A total of 91 patients (13.1%) developed new-onset chronic kidney disease at a median follow up of 60 months. The chronic kidney disease-free survival rates at 1, 3, 5 and 10 year were 97.1%, 94.4%, 85.3% and 70.6%, respectively. On multivariable Cox regression analysis, age (1.041, P = 0.001), male sex (hazard ratio 1.653, P < 0.001), diabetes mellitus (hazard ratio 1.921, P = 0.046), tumor size (hazard ratio 1.331, P < 0.001) and preoperative estimated glomerular filtration rate (hazard ratio 0.937, P < 0.001) were independent predictors for new-onset chronic kidney disease. The C-index for chronic kidney disease-free survival was 0.853 (95% confidence interval 0.815-0.895). We developed a novel nomogram for predicting the 5-year chronic kidney disease-free survival probability after on-clamp partial nephrectomy. This model might have an important role in partial nephrectomy decision-making and follow-up plan after surgery. External validation of our nomogram in a larger cohort of patients should be considered. © 2018 The Japanese Urological Association.

Survival Analysis and Actuarial Parameters of Sternechus subsignatus (Coleoptera: Curculionidae) Adults.

PubMed

Guillermina Socías, María; Van Nieuwenhove, Guido; Murúa, María Gabriela; Willink, Eduardo; Liljesthröm, Gerardo Gustavo

2016-04-01

The soybean stalk weevil, Sternechus subsignatus Boheman 1836 (Coleoptera: Curculionidae), is a very serious soybean pest in the Neotropical region. Both adults and larvae feed on soybean, causing significant yield losses. Adult survival was evaluated during three soybean growing seasons under controlled environmental conditions. A survival analysis was performed using a parametric survival fit approach in order to generate survival curves and obtain information that could help optimize integrated management strategies for this weevil pest. Sex of the weevils, crop season, fortnight in which weevils emerged, and their interaction were studied regarding their effect on adult survival. The results showed that females lived longer than males, but both genders were actually long-lived, reaching 224 and 176 d, respectively. Mean lifetime (l50) was 121.88±4.56 d for females and 89.58±2.72 d for males. Although variations were observed in adult longevities among emergence fortnights and soybean seasons, only in December and January fortnights of the 2007–2008 season and December fortnights of 2009–2010 did the statistically longest and shortest longevities occur, respectively. Survivorship data (lx) of adult females and males were fitted to the Weibull frequency distribution model. The survival curve was type I for both sexes, which indicated that mortality corresponded mostly to old individuals.

Semiparametric methods to contrast gap time survival functions: Application to repeat kidney transplantation.

PubMed

Shu, Xu; Schaubel, Douglas E

2016-06-01

Times between successive events (i.e., gap times) are of great importance in survival analysis. Although many methods exist for estimating covariate effects on gap times, very few existing methods allow for comparisons between gap times themselves. Motivated by the comparison of primary and repeat transplantation, our interest is specifically in contrasting the gap time survival functions and their integration (restricted mean gap time). Two major challenges in gap time analysis are non-identifiability of the marginal distributions and the existence of dependent censoring (for all but the first gap time). We use Cox regression to estimate the (conditional) survival distributions of each gap time (given the previous gap times). Combining fitted survival functions based on those models, along with multiple imputation applied to censored gap times, we then contrast the first and second gap times with respect to average survival and restricted mean lifetime. Large-sample properties are derived, with simulation studies carried out to evaluate finite-sample performance. We apply the proposed methods to kidney transplant data obtained from a national organ transplant registry. Mean 10-year graft survival of the primary transplant is significantly greater than that of the repeat transplant, by 3.9 months (p=0.023), a result that may lack clinical importance. © 2015, The International Biometric Society.

The labelling index: a prognostic factor in head and neck carcinoma.

PubMed

Chauvel, P; Courdi, A; Gioanni, J; Vallicioni, J; Santini, J; Demard, F

1989-03-01

The thymidine labelling index (LI), representing the percentage of cells in the DNA-synthesis phase, was measured in vitro prior to therapy in 87 patients with squamous cell carcinoma of the head and neck, who were treated between 1977 and 1982. The LI was not related to patient age, site of the tumour, clinical stage or histological grade. Overall survival was 44.5%. Univariate analysis demonstrated that survival was affected by the following factors: (1) age: patients older than 55 had a better outcome (p = 0.03); (2) site of the tumour (p = 0.005): laryngeal tumours had the best survival; (3) clinical stage (p = 0.05). Histological grade did not influence the survival (p = 0.41). Patients having a tumour LI higher than 15.5% (mean + 1 S.D.) had a significantly lower survival than patients with lower tumour LI (p = 0.008). A multivariate analysis using the Cox model showed that clinical stage and LI kept their prognostic impact with regard to survival. Finally, survival after relapse was lower in patients with a high tumour LI. These results demonstrate that a high tumour proliferation rate is an additional factor influencing the disease outcome in head and neck carcinoma. Patients with bad prognosis defined by this parameter could be offered a more energetic treatment.

Estimating the average treatment effect on survival based on observational data and using partly conditional modeling.

PubMed

Gong, Qi; Schaubel, Douglas E

2017-03-01

Treatments are frequently evaluated in terms of their effect on patient survival. In settings where randomization of treatment is not feasible, observational data are employed, necessitating correction for covariate imbalances. Treatments are usually compared using a hazard ratio. Most existing methods which quantify the treatment effect through the survival function are applicable to treatments assigned at time 0. In the data structure of our interest, subjects typically begin follow-up untreated; time-until-treatment, and the pretreatment death hazard are both heavily influenced by longitudinal covariates; and subjects may experience periods of treatment ineligibility. We propose semiparametric methods for estimating the average difference in restricted mean survival time attributable to a time-dependent treatment, the average effect of treatment among the treated, under current treatment assignment patterns. The pre- and posttreatment models are partly conditional, in that they use the covariate history up to the time of treatment. The pre-treatment model is estimated through recently developed landmark analysis methods. For each treated patient, fitted pre- and posttreatment survival curves are projected out, then averaged in a manner which accounts for the censoring of treatment times. Asymptotic properties are derived and evaluated through simulation. The proposed methods are applied to liver transplant data in order to estimate the effect of liver transplantation on survival among transplant recipients under current practice patterns. © 2016, The International Biometric Society.

Survival analysis of cervical cancer using stratified Cox regression

NASA Astrophysics Data System (ADS)

Purnami, S. W.; Inayati, K. D.; Sari, N. W. Wulan; Chosuvivatwong, V.; Sriplung, H.

2016-04-01

Cervical cancer is one of the mostly widely cancer cause of the women death in the world including Indonesia. Most cervical cancer patients come to the hospital already in an advanced stadium. As a result, the treatment of cervical cancer becomes more difficult and even can increase the death's risk. One of parameter that can be used to assess successfully of treatment is the probability of survival. This study raises the issue of cervical cancer survival patients at Dr. Soetomo Hospital using stratified Cox regression based on six factors such as age, stadium, treatment initiation, companion disease, complication, and anemia. Stratified Cox model is used because there is one independent variable that does not satisfy the proportional hazards assumption that is stadium. The results of the stratified Cox model show that the complication variable is significant factor which influent survival probability of cervical cancer patient. The obtained hazard ratio is 7.35. It means that cervical cancer patient who has complication is at risk of dying 7.35 times greater than patient who did not has complication. While the adjusted survival curves showed that stadium IV had the lowest probability of survival.

Mathematical modeling of efficacy and safety for anticancer drugs clinical development.

PubMed

Lavezzi, Silvia Maria; Borella, Elisa; Carrara, Letizia; De Nicolao, Giuseppe; Magni, Paolo; Poggesi, Italo

2018-01-01

Drug attrition in oncology clinical development is higher than in other therapeutic areas. In this context, pharmacometric modeling represents a useful tool to explore drug efficacy in earlier phases of clinical development, anticipating overall survival using quantitative model-based metrics. Furthermore, modeling approaches can be used to characterize earlier the safety and tolerability profile of drug candidates, and, thus, the risk-benefit ratio and the therapeutic index, supporting the design of optimal treatment regimens and accelerating the whole process of clinical drug development. Areas covered: Herein, the most relevant mathematical models used in clinical anticancer drug development during the last decade are described. Less recent models were considered in the review if they represent a standard for the analysis of certain types of efficacy or safety measures. Expert opinion: Several mathematical models have been proposed to predict overall survival from earlier endpoints and validate their surrogacy in demonstrating drug efficacy in place of overall survival. An increasing number of mathematical models have also been developed to describe the safety findings. Modeling has been extensively used in anticancer drug development to individualize dosing strategies based on patient characteristics, and design optimal dosing regimens balancing efficacy and safety.

Modeling Rabbit Responses to Single and Multiple Aerosol ...

EPA Pesticide Factsheets

Journal Article Survival models are developed here to predict response and time-to-response for mortality in rabbits following exposures to single or multiple aerosol doses of Bacillus anthracis spores. Hazard function models were developed for a multiple dose dataset to predict the probability of death through specifying dose-response functions and the time between exposure and the time-to-death (TTD). Among the models developed, the best-fitting survival model (baseline model) has an exponential dose-response model with a Weibull TTD distribution. Alternative models assessed employ different underlying dose-response functions and use the assumption that, in a multiple dose scenario, earlier doses affect the hazard functions of each subsequent dose. In addition, published mechanistic models are analyzed and compared with models developed in this paper. None of the alternative models that were assessed provided a statistically significant improvement in fit over the baseline model. The general approach utilizes simple empirical data analysis to develop parsimonious models with limited reliance on mechanistic assumptions. The baseline model predicts TTDs consistent with reported results from three independent high-dose rabbit datasets. More accurate survival models depend upon future development of dose-response datasets specifically designed to assess potential multiple dose effects on response and time-to-response. The process used in this paper to dev

Pyrotechnic Shock Analysis Using Statistical Energy Analysis

DTIC Science & Technology

2015-10-23

SEA subsystems. A couple of validation examples are provided to demonstrate the new approach. KEY WORDS : Peak Ratio, phase perturbation...Ballistic Shock Prediction Models and Techniques for Use in the Crusader Combat Vehicle Program,” 11th Annual US Army Ground Vehicle Survivability

Persistence of Pathogenic and Non-Pathogenic Escherichia coli Strains in Various Tropical Agricultural Soils of India

PubMed Central

Naganandhini, S.; Kennedy, Z. John; Uyttendaele, M.; Balachandar, D.

2015-01-01

The persistence of Shiga-like toxin producing E. coli (STEC) strains in the agricultural soil creates serious threat to human health through fresh vegetables growing on them. However, the survival of STEC strains in Indian tropical soils is not yet understood thoroughly. Additionally how the survival of STEC strain in soil diverges with non-pathogenic and genetically modified E. coli strains is also not yet assessed. Hence in the present study, the survival pattern of STEC strain (O157-TNAU) was compared with non-pathogenic (MTCC433) and genetically modified (DH5α) strains on different tropical agricultural soils and on a vegetable growing medium, cocopeat under controlled condition. The survival pattern clearly discriminated DH5α from MTCC433 and O157-TNAU, which had shorter life (40 days) than those compared (60 days). Similarly, among the soils assessed, the red laterite and tropical latosol supported longer survival of O157-TNAU and MTCC433 as compared to wetland and black cotton soils. In cocopeat, O157 recorded significantly longer survival than other two strains. The survival data were successfully analyzed using Double-Weibull model and the modeling parameters were correlated with soil physico-chemical and biological properties using principal component analysis (PCA). The PCA of all the three strains revealed that pH, microbial biomass carbon, dehydrogenase activity and available N and P contents of the soil decided the survival of E. coli strains in those soils and cocopeat. The present research work suggests that the survival of O157 differs in tropical Indian soils due to varied physico-chemical and biological properties and the survival is much shorter than those reported in temperate soils. As the survival pattern of non-pathogenic strain, MTCC433 is similar to O157-TNAU in tropical soils, the former can be used as safe model organism for open field studies. PMID:26101887

Sinonasal fibrosarcoma: analysis of the Surveillance, Epidemiology, and End Results database.

PubMed

Patel, Tapan D; Carniol, Eric T; Vázquez, Alejandro; Baredes, Soly; Liu, James K; Eloy, Jean Anderson

2016-02-01

Primary fibrosarcoma of the sinonasal region is an infrequently occurring malignant neoplasm. Fibrosarcomas are most commonly found in the extremities, with only 1% of fibrosarcomas reported in the head and neck region. This study analyzes the demographic, clinicopathologic, and survival characteristics of sinonasal fibrosarcoma (SNFS). The Surveillance, Epidemiology, and End Results (SEER) database (1973 to 2012) was queried for SNFS cases. Data were analyzed with respect to various demographic and clinicopathologic factors. Survival was analyzed using the Kaplan-Meier model. Fifty-one cases of fibrosarcoma were identified in the sinonasal region. The mean age at diagnosis was 54.5 years and the mean survival was 119.7 months. There was no gender predilection with a male-to-female ratio of 1.04:1. The maxillary sinus was the most common site of involvement (54.9%), followed by the nasal cavity (23.5%). Five-year survival analysis revealed an overall survival rate of 71.7%, disease-specific survival rate of 77.8%, and relative survival (RS) rate of 78.8%. Disease-specific survival was better among those treated with surgery (with [76.2%] or without [87.5%] adjuvant radiotherapy) than those treated with primary radiotherapy alone (33.3%) (p = 0.0069). SNFS is a rare entity. This study represents the largest series of SNFS to date. The mainstay of treatment for this tumor is surgical resection with or without radiotherapy. © 2015 ARS-AAOA, LLC.

Reduced retinoids and retinoid receptors' expression in pancreatic cancer: A link to patient survival.

PubMed

Bleul, Tim; Rühl, Ralph; Bulashevska, Svetlana; Karakhanova, Svetlana; Werner, Jens; Bazhin, Alexandr V

2015-09-01

Pancreatic ductal adenocarcinoma (PDAC) represents one of the deadliest cancers in the world. All-trans retinoic acid (ATRA) is the major physiologically active form of vitamin A, regulating expression of many genes. Disturbances of vitamin A metabolism are prevalent in some cancer cells. The main aim of this work was to investigate deeply the components of retinoid signaling in PDAC compared to in the normal pancreas and to prove the clinical importance of retinoid receptor expression. For the study, human tumor tissues obtained from PDAC patients and murine tumors from the orthotopic Panc02 model were used for the analysis of retinoids, using high performance liquid chromatography mass spectrometry and real-time RT-PCR gene expression analysis. Survival probabilities in univariate analysis were estimated using the Kaplan-Meier method and the Cox proportional hazards model was used for the multivariate analysis. In this work, we showed for the first time that the ATRA and all-trans retinol concentration is reduced in PDAC tissue compared to their normal counterparts. The expression of RARα and β as well as RXRα and β are down-regulated in PDAC tissue. This reduced expression of retinoid receptors correlates with the expression of some markers of differentiation and epithelial-to-mesenchymal transition as well as of cancer stem cell markers. Importantly, the expression of RARα and RXRβ is associated with better overall survival of PDAC patients. Thus, reduction of retinoids and their receptors is an important feature of PDAC and is associated with worse patient survival outcomes. © 2014 Wiley Periodicals, Inc.

Pre-transplant Exercise and Hematopoietic Cell Transplant Survival: a secondary analysis of Blood and Marrow Transplant Clinical Trials Network (BMT CTN 0902)

PubMed Central

Wingard, John R.; Wood, William A.; Martens, Michael; Le-Rademacher, Jennifer; Logan, Brent; Knight, Jennifer M.; Jacobsen, Paul B.; Jim, Heather; Majhail, Navneet S.; Syrjala, Karen; Rizzo, J. Douglas; Lee, Stephanie J.

2016-01-01

Blood and Marrow Transplant Clinical Trials Network (BMT CTN) Protocol 0902 evaluated whether exercise and stress management training prior to hematopoietic cell transplantation (HCT) improved physical and mental functioning after HCT. Neither overall survival nor other patient-reported transplant outcomes were improved by the training intervention. In some animal studies of HCT, moderate intensity exercise for 8 weeks before HCT has been associated with positive effects on hematopoietic progenitors resulting in improved donor engraftment and improved survival. Accordingly, we performed a secondary analysis of data from BMT CTN 0902 to determine whether exercise engagement prior to HCT was associated with engraftment and survival. There were no significant associations between self-reported pre-HCT exercise levels and engraftment or survival. There was also no effect of pre-transplant exercise on either neutrophil or platelet engraftment. These findings do not support the observations in animal models but are limited by several shortcomings that do not refute the hypothesis that exercise before HCT may be beneficial. PMID:27742574

Survival Impact of Adjuvant Radiation Therapy in Masaoka Stage II to IV Thymomas: A Systematic Review and Meta-analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lim, Yu Jin; Kim, Eunji; Kim, Hak Jae, E-mail: khjae@snu.ac.kr

Purpose: To evaluate the survival impact of postoperative radiation therapy (PORT) in stage II to IV thymomas, using systematic review and meta-analysis. Methods and Materials: A database search was conducted with EMBASE, PubMed, Web of Science, Cochrane Library, and Ovid from inception to August 2015. Thymic carcinomas were excluded, and studies comparing overall survival (OS) with and without PORT in thymomas were included. The hazard ratios (HRs) of OS were extracted, and a random-effects model was used in the pooled analysis. Results: Seven retrospective series with a total of 1724 patients were included and analyzed. Almost all of the patients underwentmore » macroscopically complete resection, and thymoma histology was confirmed by the World Health Organization criteria. In the overall analysis of stage II to IV thymomas, OS was not altered with the receipt of PORT (HR 0.79, 95% confidence interval [CI] 0.58-1.08). Although PORT was not associated with survival difference in Masaoka stage II disease (HR 1.45, 95% CI 0.83-2.55), improved OS was observed with the addition of PORT in the discrete pooled analysis of stage III to IV (HR 0.63, 95% CI 0.40-0.99). Significant heterogeneity and publication bias were not found in the analyses. Conclusions: From the present meta-analysis of sole primary thymomas, we suggest the potential OS benefit of PORT in locally advanced tumors with macroscopically complete resection, but not in stage II disease. Further investigations with sufficient survival data are needed to establish detailed treatment indications.« less

A mathematical model of salmonid spawning habitat

Treesearch

Robert N. Havis; Carlos V. Alonzo; Keith E Woeste; Russell F. Thurow

1993-01-01

A simulation model [Salmonid Spawning Analysis Model (SSAM)I was developed as a management tool to evaluate the relative impacts of stream sediment load and water temperature on salmonid egg survival. The model is usefi.il for estimating acceptable sediment loads to spawning habitat that may result from upland development, such as logging and agriculture. Software in...

Application and validation of Cox regression models in a single-center series of double kidney transplantation.

PubMed

Santori, G; Fontana, I; Bertocchi, M; Gasloli, G; Magoni Rossi, A; Tagliamacco, A; Barocci, S; Nocera, A; Valente, U

2010-05-01

A useful approach to reduce the number of discarded marginal kidneys and to increase the nephron mass is double kidney transplantation (DKT). In this study, we retrospectively evaluated the potential predictors for patient and graft survival in a single-center series of 59 DKT procedures performed between April 21, 1999, and September 21, 2008. The kidney recipients of mean age 63.27 +/- 5.17 years included 16 women (27%) and 43 men (73%). The donors of mean age 69.54 +/- 7.48 years included 32 women (54%) and 27 men (46%). The mean posttransplant dialysis time was 2.37 +/- 3.61 days. The mean hospitalization was 20.12 +/- 13.65 days. Average serum creatinine (SCr) at discharge was 1.5 +/- 0.59 mg/dL. In view of the limited numbers of recipient deaths (n = 4) and graft losses (n = 8) that occurred in our series, the proportional hazards assumption for each Cox regression model with P < .05 was tested by using correlation coefficients between transformed survival times and scaled Schoenfeld residuals, and checked with smoothed plots of Schoenfeld residuals. For patient survival, the variables that reached statistical significance were donor SCr (P = .007), donor creatinine cleararance (P = .023), and recipient age (P = .047). Each significant model passed the Schoenfeld test. By entering these variables into a multivariate Cox model for patient survival, no further significance was observed. In the univariate Cox models performed for graft survival, statistical significance was noted for donor SCr (P = .027), SCr 3 months post-DKT (P = .043), and SCr 6 months post-DKT (P = .017). All significant univariate models for graft survival passed the Schoenfeld test. A final multivariate model retained SCr at 6 months (beta = 1.746, P = .042) and donor SCr (beta = .767, P = .090). In our analysis, SCr at 6 months seemed to emerge from both univariate and multivariate Cox models as a potential predictor of graft survival among DKT. Multicenter studies with larger recipient populations and more graft losses should be performed to confirm our findings. Copyright (c) 2010 Elsevier Inc. All rights reserved.

A prognostic index for natural killer cell lymphoma after non-anthracycline-based treatment: a multicentre, retrospective analysis.

PubMed

Kim, Seok Jin; Yoon, Dok Hyun; Jaccard, Arnaud; Chng, Wee Joo; Lim, Soon Thye; Hong, Huangming; Park, Yong; Chang, Kian Meng; Maeda, Yoshinobu; Ishida, Fumihiro; Shin, Dong-Yeop; Kim, Jin Seok; Jeong, Seong Hyun; Yang, Deok-Hwan; Jo, Jae-Cheol; Lee, Gyeong-Won; Choi, Chul Won; Lee, Won-Sik; Chen, Tsai-Yun; Kim, Kiyeun; Jung, Sin-Ho; Murayama, Tohru; Oki, Yasuhiro; Advani, Ranjana; d'Amore, Francesco; Schmitz, Norbert; Suh, Cheolwon; Suzuki, Ritsuro; Kwong, Yok Lam; Lin, Tong-Yu; Kim, Won Seog

2016-03-01

The clinical outcome of extranodal natural killer T-cell lymphoma (ENKTL) has improved substantially as a result of new treatment strategies with non-anthracycline-based chemotherapies and upfront use of concurrent chemoradiotherapy or radiotherapy. A new prognostic model based on the outcomes obtained with these contemporary treatments was warranted. We did a retrospective study of patients with newly diagnosed ENKTL without any previous treatment history for the disease who were given non-anthracycline-based chemotherapies with or without upfront concurrent chemoradiotherapy or radiotherapy with curative intent. A prognostic model to predict overall survival and progression-free survival on the basis of pretreatment clinical and laboratory characteristics was developed by filling a multivariable model on the basis of the dataset with complete data for the selected risk factors for an unbiased prediction model. The final model was applied to the patients who had complete data for the selected risk factors. We did a validation analysis of the prognostic model in an independent cohort. We did multivariate analyses of 527 patients who were included from 38 hospitals in 11 countries in the training cohort. Analyses showed that age greater than 60 years, stage III or IV disease, distant lymph-node involvement, and non-nasal type disease were significantly associated with overall survival and progression-free survival. We used these data as the basis for the prognostic index of natural killer lymphoma (PINK), in which patients are stratified into low-risk (no risk factors), intermediate-risk (one risk factor), or high-risk (two or more risk factors) groups, which were associated with 3-year overall survival of 81% (95% CI 75-86), 62% (55-70), and 25% (20-34), respectively. In the 328 patients with data for Epstein-Barr virus DNA, a detectable viral DNA titre was an independent prognostic factor for overall survival. When these data were added to PINK as the basis for another prognostic index (PINK-E)-which had similar low-risk (zero or one risk factor), intermediate-risk (two risk factors), and high-risk (three or more risk factors) categories-significant associations with overall survival were noted (81% [95% CI 75-87%], 55% (44-66), and 28% (18-40%), respectively). These results were validated and confirmed in an independent cohort, although the PINK-E model was only significantly associated with the high-risk group compared with the low-risk group. PINK and PINK-E are new prognostic models that can be used to develop risk-adapted treatment approaches for patients with ENKTL being treated in the contemporary era of non-anthracycline-based therapy. Samsung Biomedical Research Institute. Copyright © 2016 Elsevier Ltd. All rights reserved.

Population Trend and Elasticities of Vital Rates for Steller Sea Lions (Eumetopias jubatus) in the Eastern Gulf of Alaska: A New Life-History Table Analysis

PubMed Central

Maniscalco, John M.; Springer, Alan M.; Adkison, Milo D.; Parker, Pamela

2015-01-01

Steller sea lion (Eumetopias jubatus) numbers are beginning to recover across most of the western distinct population segment following catastrophic declines that began in the 1970s and ended around the turn of the century. This study makes use of contemporary vital rate estimates from a trend-site rookery in the eastern Gulf of Alaska (a sub-region of the western population) in a matrix population model to estimate the trend and strength of the recovery across this region between 2003 and 2013. The modeled population trend was projected into the future based on observed variation in vital rates and a prospective elasticity analysis was conducted to determine future trends and which vital rates pose the greatest threats to recovery. The modeled population grew at a mean rate of 3.5% per yr between 2003 and 2013 and was correlated with census count data from the local rookery and throughout the eastern Gulf of Alaska. If recent vital rate estimates continue with little change, the eastern Gulf of Alaska population could be fully recovered to pre-decline levels within 23 years. With density dependent growth, the population would need another 45 years to fully recover. Elasticity analysis showed that, as expected, population growth rate (λ) was most sensitive to changes in adult survival, less sensitive to changes in juvenile survival, and least sensitive to changes in fecundity. A population decline could be expected with only a 6% decrease in adult survival, whereas a 32% decrease in fecundity would be necessary to bring about a population decline. These results have important implications for population management and suggest current research priorities should be shifted to a greater emphasis on survival rates and causes of mortality. PMID:26488901

Jaundice: an important, poorly recognized risk factor for diminished survival in patients with adenocarcinoma of the head of the pancreas

PubMed Central

Strasberg, Steven M; Gao, Feng; Sanford, Dominic; Linehan, David C; Hawkins, William G; Fields, Ryan; Carpenter, Danielle H; Brunt, Elizabeth M; Phillips, Carolyn

2014-01-01

Objectives: Jaundice impairs cellular immunity, an important defence against the dissemination of cancer. Jaundice is a common mode of presentation in pancreatic head adenocarcinoma. The purpose of this study was to determine whether there is an association between preoperative jaundice and survival in patients who have undergone resection of such tumours. Methods: Thirty possible survival risk factors were evaluated in a database of over 400 resected patients. Univariate analysis was used to determine odds ratio for death. All factors for which a P-value of <0.30 was obtained were entered into a multivariate analysis using the Cox model with backward selection. Results: Preoperative jaundice, age, positive node status, poor differentiation and lymphatic invasion were significant indicators of poor outcome in multivariate analysis. Absence of jaundice was a highly favourable prognostic factor. Interaction emerged between jaundice and nodal status. The benefit conferred by the absence of jaundice was restricted to patients in whom negative node status was present. Five-year overall survival in this group was 66%. Jaundiced patients who underwent preoperative stenting had a survival advantage. Conclusions: Preoperative jaundice is a negative risk factor in adenocarcinoma of the pancreas. Additional studies are required to determine the exact mechanism for this effect. PMID:23600768

Epinephrine in cardiac arrest: systematic review and meta-analysis

PubMed Central

Morales-Cané, Ignacio; Valverde-León, María Del Rocío; Rodríguez-Borrego, María Aurora

2016-01-01

abstract Objective: evaluate the effectiveness of epinephrine used during cardiac arrest and its effect on the survival rates and neurological condition. Method: systematic review of scientific literature with meta-analysis, using a random effects model. The following databases were used to research clinical trials and observational studies: Medline, Embase and Cochrane, from 2005 to 2015. Results: when the Return of Spontaneous Circulation (ROSC) with administration of epinephrine was compared with ROSC without administration, increased rates were found with administration (OR 2.02. 95% CI 1.49 to 2.75; I2 = 95%). Meta-analysis showed an increase in survival to discharge or 30 days after administration of epinephrine (OR 1.23; 95% IC 1.05-1.44; I2=83%). Stratification by shockable and non-shockable rhythms showed an increase in survival for non-shockable rhythm (OR 1.52; 95% IC 1.29-1.78; I2=42%). When compared with delayed administration, the administration of epinephrine within 10 minutes showed an increased survival rate (OR 2.03; 95% IC 1.77-2.32; I2=0%). Conclusion: administration of epinephrine appears to increase the rate of ROSC, but when compared with other therapies, no positive effect was found on survival rates of patients with favorable neurological status. PMID:27982306

Hazard Regression Models of Early Mortality in Trauma Centers

PubMed Central

Clark, David E; Qian, Jing; Winchell, Robert J; Betensky, Rebecca A

2013-01-01

Background Factors affecting early hospital deaths after trauma may be different from factors affecting later hospital deaths, and the distribution of short and long prehospital times may vary among hospitals. Hazard regression (HR) models may therefore be more useful than logistic regression (LR) models for analysis of trauma mortality, especially when treatment effects at different time points are of interest. Study Design We obtained data for trauma center patients from the 2008–9 National Trauma Data Bank (NTDB). Cases were included if they had complete data for prehospital times, hospital times, survival outcome, age, vital signs, and severity scores. Cases were excluded if pulseless on admission, transferred in or out, or ISS<9. Using covariates proposed for the Trauma Quality Improvement Program and an indicator for each hospital, we compared LR models predicting survival at 8 hours after injury to HR models with survival censored at 8 hours. HR models were then modified to allow time-varying hospital effects. Results 85,327 patients in 161 hospitals met inclusion criteria. Crude hazards peaked initially, then steadily declined. When hazard ratios were assumed constant in HR models, they were similar to odds ratios in LR models associating increased mortality with increased age, firearm mechanism, increased severity, more deranged physiology, and estimated hospital-specific effects. However, when hospital effects were allowed to vary by time, HR models demonstrated that hospital outliers were not the same at different times after injury. Conclusions HR models with time-varying hazard ratios reveal inconsistencies in treatment effects, data quality, and/or timing of early death among trauma centers. HR models are generally more flexible than LR models, can be adapted for censored data, and potentially offer a better tool for analysis of factors affecting early death after injury. PMID:23036828

Use of SERTS (Socio-Economic, health Resources and Technologic Supplies) models to estimate cancer survival at provincial geographical level.

PubMed

Vercelli, Marina; Lillini, Roberto; Capocaccia, Riccardo; Quaglia, Alberto

2012-12-01

The main aim of this work is to compute expected cancer survival for Italian provinces by Socio-Economic and health Resources and Technologic Supplies (SERTS) models, based on demographic, socioeconomic variables and information describing the health care system (SEH). Five-year age-standardised relative survival rates by gender for 11 cancer sites and all cancers combined of patients diagnosed in 1995-1999, were obtained from the Italian Association of Cancer Registries (CRs) database. The SEH variables describe at provincial level macro-economy, demography, labour market, health resources in 1995-2005. A principal components factor analysis was applied to the SEH variables to control their strong mutual correlation. For every considered cancer site, linear regression models were estimated considering the 5-RS% as dependent variable and the principal components factors of the SEH variables as independent variables. The model composition was correlated to the characteristics of take in charge of patients. SEH factors were correlated with the observed survival for all cancer combined and colon-rectum in both sexes, prostate, kidney and non Hodgkin's lymphomas in men, breast, corpus uteri and melanoma in women (R(2) from 40% to 85%). In the provinces without any CR the survival was very similar with that of neighbouring provinces with analogous social, economic and health characteristics. The SERTS models allowed us to interpret the survival outcome of oncologic patients with respect to the role of the socio-economic and health related system characteristics, stressing how the peculiarities of the take in charge at the province level could address the decisions regarding the allocation of resources. Copyright © 2012 Elsevier Ltd. All rights reserved.

Does Breast Cancer Drive the Building of Survival Probability Models among States? An Assessment of Goodness of Fit for Patient Data from SEER Registries

PubMed

Khan, Hafiz; Saxena, Anshul; Perisetti, Abhilash; Rafiq, Aamrin; Gabbidon, Kemesha; Mende, Sarah; Lyuksyutova, Maria; Quesada, Kandi; Blakely, Summre; Torres, Tiffany; Afesse, Mahlet

2016-12-01

Background: Breast cancer is a worldwide public health concern and is the most prevalent type of cancer in women in the United States. This study concerned the best fit of statistical probability models on the basis of survival times for nine state cancer registries: California, Connecticut, Georgia, Hawaii, Iowa, Michigan, New Mexico, Utah, and Washington. Materials and Methods: A probability random sampling method was applied to select and extract records of 2,000 breast cancer patients from the Surveillance Epidemiology and End Results (SEER) database for each of the nine state cancer registries used in this study. EasyFit software was utilized to identify the best probability models by using goodness of fit tests, and to estimate parameters for various statistical probability distributions that fit survival data. Results: Statistical analysis for the summary of statistics is reported for each of the states for the years 1973 to 2012. Kolmogorov-Smirnov, Anderson-Darling, and Chi-squared goodness of fit test values were used for survival data, the highest values of goodness of fit statistics being considered indicative of the best fit survival model for each state. Conclusions: It was found that California, Connecticut, Georgia, Iowa, New Mexico, and Washington followed the Burr probability distribution, while the Dagum probability distribution gave the best fit for Michigan and Utah, and Hawaii followed the Gamma probability distribution. These findings highlight differences between states through selected sociodemographic variables and also demonstrate probability modeling differences in breast cancer survival times. The results of this study can be used to guide healthcare providers and researchers for further investigations into social and environmental factors in order to reduce the occurrence of and mortality due to breast cancer. Creative Commons Attribution License

Validation of the prognostic value of lymph node ratio in patients with cutaneous melanoma: a population-based study of 8,177 cases.

PubMed

Mocellin, Simone; Pasquali, Sandro; Rossi, Carlo Riccardo; Nitti, Donato

2011-07-01

The proportion of positive among examined lymph nodes (lymph node ratio [LNR]) has been recently proposed as an useful and easy-to-calculate prognostic factor for patients with cutaneous melanoma. However, its independence from the standard prognostic system TNM has not been formally proven in a large series of patients. Patients with histologically proven cutaneous melanoma were identified from the Surveillance Epidemiology End Results database. Disease-specific survival was the clinical outcome of interest. The prognostic ability of conventional factors and LNR was assessed by multivariable survival analysis using the Cox regression model. Eligible patients (n = 8,177) were diagnosed with melanoma between 1998 and 2006. Among lymph node-positive cases (n = 3,872), most LNR values ranged from 1% to 10% (n = 2,187). In the whole series (≥5 lymph nodes examined) LNR significantly contributed to the Cox model independently of the TNM effect on survival (hazard ratio, 1.28; 95% confidence interval, 1.23-1.32; P < .0001). On subgroup analysis, the significant and independent prognostic value of LNR was confirmed both in patients with ≥10 lymph nodes examined (n = 4,381) and in those with TNM stage III disease (n = 3,658). In all cases, LNR increased the prognostic accuracy of the survival model. In this large series of patients, the LNR independently predicted disease-specific survival, improving the prognostic accuracy of the TNM system. Accordingly, the LNR should be taken into account for the stratification of patients' risk, both in clinical and research settings. Copyright © 2011 Mosby, Inc. All rights reserved.

Characteristics and Outcomes of Patients with Ewing Sarcoma Over 40 Years of Age at Diagnosis

PubMed Central

Karski, Erin E.; Matthay, Katherine K.; Neuhaus, John M.; Goldsby, Robert E.; DuBois, Steven G.

2012-01-01

Background The peak incidence of Ewing sarcoma (EWS) is in adolescence, with little known about patients who are ≥ 40 years at diagnosis. We describe the clinical characteristics and survival of this rare group. Methods This retrospective cohort study utilized the Surveillance Epidemiology and End Results database. 2780 patients were identified; including 383 patients diagnosed ≥ 40 years. Patient characteristics between age groups were compared using chi-squared tests. Survival from diagnosis to death was estimated via Kaplan-Meier methods, compared with log-rank tests, and modeled using multivariable Cox methods. A competing risks analysis was performed to evaluate death due to cancer. Results Patients ≥ 40 years of age were more likely to have extra-skeletal tumors (66.1% v 31.7%; p<0.001), axial tumors (64.0% v 57.2%; p=0.01), and metastatic disease at diagnosis (35.5% v 30.0%; p=0.04) compared to younger patients. Five-year survival for those age ≥ 40 and age < 40 were 40.6% and 54.3%, respectively (p<0.0001). A Cox multivariable model controlling for differences between groups confirmed inferior survival for older patients (hazard ratio for death of 2.04; 95% CI 1.63 - 2.54; p < 0.0001); though treatment data were unavailable and not controlled for in the model. A competing risks analysis confirmed increased risk of cancer-related death in older patients. Conclusion Patients ≥ 40 years at diagnosis with EWS are more likely to have extra-skeletal tumors, metastatic disease, and axial primary tumors suggesting a difference in tumor biology. Independent of differences in these characteristics, older patients also have a lower survival rate. PMID:22959474

Characteristics and outcomes of patients with Ewing sarcoma over 40 years of age at diagnosis.

PubMed

Karski, Erin E; Matthay, Katherine K; Neuhaus, John M; Goldsby, Robert E; Dubois, Steven G

2013-02-01

The peak incidence of Ewing sarcoma (EWS) is in adolescence, with little known about patients who are ≥40 years at diagnosis. We describe the clinical characteristics and survival of this rare group. This retrospective cohort study utilized the Surveillance Epidemiology and End Results database. 2780 patients were identified; including 383 patients diagnosed ≥40 years. Patient characteristics between age groups were compared using chi-squared tests. Survival from diagnosis to death was estimated via Kaplan-Meier methods, compared with log-rank tests, and modeled using multivariable Cox methods. A competing risks analysis was performed to evaluate death due to cancer. Patients ≥40 years of age were more likely to have extra-skeletal tumors (66.1% vs. 31.7%; p < 0.001), axial tumors (64.0% vs. 57.2%; p = 0.01), and metastatic disease at diagnosis (35.5% vs. 30.0%; p = 0.04) compared to younger patients. Five-year survival for those age ≥40 and age <40 were 40.6% and 54.3%, respectively (p < 0.0001). A Cox multivariable model controlling for differences between groups confirmed inferior survival for older patients (hazard ratio for death of 2.04; 95% CI 1.63-2.54; p < 0.0001); though treatment data were unavailable and not controlled for in the model. A competing risks analysis confirmed increased risk of cancer-related death in older patients. Patients ≥40 years at diagnosis with EWS are more likely to have extra-skeletal tumors, metastatic disease, and axial primary tumors suggesting a difference in tumor biology. Independent of differences in these characteristics, older patients also have a lower survival rate. Copyright © 2012 Elsevier Ltd. All rights reserved.

Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma.

PubMed

Guo, Jin-Cheng; Wu, Yang; Chen, Yang; Pan, Feng; Wu, Zhi-Yong; Zhang, Jia-Sheng; Wu, Jian-Yi; Xu, Xiu-E; Zhao, Jian-Mei; Li, En-Min; Zhao, Yi; Xu, Li-Yan

2018-04-09

Esophageal squamous cell carcinoma (ESCC) is the predominant subtype of esophageal carcinoma in China. This study was to develop a staging model to predict outcomes of patients with ESCC. Using Cox regression analysis, principal component analysis (PCA), partitioning clustering, Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, and classification and regression tree (CART) analysis, we mined the Gene Expression Omnibus database to determine the expression profiles of genes in 179 patients with ESCC from GSE63624 and GSE63622 dataset. Univariate cox regression analysis of the GSE63624 dataset revealed that 2404 protein-coding genes (PCGs) and 635 long non-coding RNAs (lncRNAs) were associated with the survival of patients with ESCC. PCA categorized these PCGs and lncRNAs into three principal components (PCs), which were used to cluster the patients into three groups. ROC analysis demonstrated that the predictive ability of PCG-lncRNA PCs when applied to new patients was better than that of the tumor-node-metastasis staging (area under ROC curve [AUC]: 0.69 vs. 0.65, P < 0.05). Accordingly, we constructed a molecular disaggregated model comprising one lncRNA and two PCGs, which we designated as the LSB staging model using CART analysis in the GSE63624 dataset. This LSB staging model classified the GSE63622 dataset of patients into three different groups, and its effectiveness was validated by analysis of another cohort of 105 patients. The LSB staging model has clinical significance for the prognosis prediction of patients with ESCC and may serve as a three-gene staging microarray.

Survival rate of AIDS disease and mortality in HIV-infected patients: a meta-analysis.

PubMed

Poorolajal, J; Hooshmand, E; Mahjub, H; Esmailnasab, N; Jenabi, E

2016-10-01

The life expectancy of patients with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) reported by several epidemiological studies is inconsistent. This meta-analysis was conducted to estimate the survival rate from HIV diagnosis to AIDS onset and from AIDS onset to death. The electronic databases PubMed, Web of Science and Scopus were searched to February 2016. In addition, the reference lists of included studies were checked to identify further references, and the database of the International AIDS Society was also searched. Cohort studies addressing the survival rate in patients diagnosed with HIV/AIDS were included in this meta-analysis. The outcomes of interest were the survival rate of patients diagnosed with HIV progressing to AIDS, and the survival rate of patients with AIDS dying from AIDS-related causes with or without highly active antiretroviral therapy (HAART). The survival rate (P) was estimated with 95% confidence intervals based on random-effects models. In total, 27,862 references were identified, and 57 studies involving 294,662 participants were included in this meta-analysis. Two, 4-, 6-, 8-, 10- and 12-year survival probabilities of progression from HIV diagnosis to AIDS onset were estimated to be 82%, 72%, 64%, 57%, 26% and 19%, respectively. Two, 4-, 6-, 8- and 10-year survival probabilities of progression from AIDS onset to AIDS-related death in patients who received HAART were estimated to be 87%, 86%, 78%, 78%, and 61%, respectively, and 2-, 4- and 6-year survival probabilities of progression from AIDS onset to AIDS-related death in patients who did not receive HAART were estimated to be 48%, 26% and 18%, respectively. Evidence of considerable heterogeneity was found. The majority of the studies had a moderate to high risk of bias. The majority of HIV-positive patients progress to AIDS within the first decade of diagnosis. Most patients who receive HAART will survive for >10 years after the onset of AIDS, whereas the majority of the patients who do not receive HAART die within 2 years of the onset of AIDS. Copyright © 2016 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

TU-CD-BRB-08: Radiomic Analysis of FDG-PET Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated with SBRT

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cui, Y; Shirato, H; Song, J

2015-06-15

Purpose: This study aims to identify novel prognostic imaging biomarkers in locally advanced pancreatic cancer (LAPC) using quantitative, high-throughput image analysis. Methods: 86 patients with LAPC receiving chemotherapy followed by SBRT were retrospectively studied. All patients had a baseline FDG-PET scan prior to SBRT. For each patient, we extracted 435 PET imaging features of five types: statistical, morphological, textural, histogram, and wavelet. These features went through redundancy checks, robustness analysis, as well as a prescreening process based on their concordance indices with respect to the relevant outcomes. We then performed principle component analysis on the remaining features (number ranged frommore » 10 to 16), and fitted a Cox proportional hazard regression model using the first 3 principle components. Kaplan-Meier analysis was used to assess the ability to distinguish high versus low-risk patients separated by median predicted survival. To avoid overfitting, all evaluations were based on leave-one-out cross validation (LOOCV), in which each holdout patient was assigned to a risk group according to the model obtained from a separate training set. Results: For predicting overall survival (OS), the most dominant imaging features were wavelet coefficients. There was a statistically significant difference in OS between patients with predicted high and low-risk based on LOOCV (hazard ratio: 2.26, p<0.001). Similar imaging features were also strongly associated with local progression-free survival (LPFS) (hazard ratio: 1.53, p=0.026) on LOOCV. In comparison, neither SUVmax nor TLG was associated with LPFS (p=0.103, p=0.433) (Table 1). Results for progression-free survival and distant progression-free survival showed similar trends. Conclusion: Radiomic analysis identified novel imaging features that showed improved prognostic value over conventional methods. These features characterize the degree of intra-tumor heterogeneity reflected on FDG-PET images, and their biological underpinnings warrant further investigation. If validated in large, prospective cohorts, this method could be used to stratify patients based on individualized risk.« less

The application of data mining techniques to oral cancer prognosis.

PubMed

Tseng, Wan-Ting; Chiang, Wei-Fan; Liu, Shyun-Yeu; Roan, Jinsheng; Lin, Chun-Nan

2015-05-01

This study adopted an integrated procedure that combines the clustering and classification features of data mining technology to determine the differences between the symptoms shown in past cases where patients died from or survived oral cancer. Two data mining tools, namely decision tree and artificial neural network, were used to analyze the historical cases of oral cancer, and their performance was compared with that of logistic regression, the popular statistical analysis tool. Both decision tree and artificial neural network models showed superiority to the traditional statistical model. However, as to clinician, the trees created by the decision tree models are relatively easier to interpret compared to that of the artificial neural network models. Cluster analysis also discovers that those stage 4 patients whose also possess the following four characteristics are having an extremely low survival rate: pN is N2b, level of RLNM is level I-III, AJCC-T is T4, and cells mutate situation (G) is moderate.

Mathematics and mallard management

USGS Publications Warehouse

Cowardin, L.M.; Johnson, D.H.

1979-01-01

Waterfowl managers can effectively use simple population models to aid in making management decisions. We present a basic model of the change in population size as related to survival and recruitment. A management technique designed to increase survival of mallards (Anas platyrhynchos) by limiting harvest on the Chippewa National Forest, Minnesota, is used to illustrate the application of models in decision making. The analysis suggests that the management technique would be of limited effectiveness. In a 2nd example, the change in mallard population in central North Dakota is related to implementing programs to create dense nesting cover with or without supplementary predator control. The analysis suggests that large tracts of land would be required to achieve a hypothetical management objective of increasing harvest by 50% while maintaining a stable population. Less land would be required if predator reduction were used in combination with cover management, but questions about effectiveness and ecological implications of large scale predator reduction remain unresolved. The use of models as a guide to planning research responsive to the needs of management is illustrated.

Population dynamics of spotted owls in the Sierra Nevada, California

USGS Publications Warehouse

Blakesley, J.A.; Seamans, M.E.; Conner, M.M.; Franklin, A.B.; White, Gary C.; Gutierrez, R.J.; Hines, J.E.; Nichols, J.D.; Munton, T.E.; Shaw, D.W.H.; Keane, J.J.; Steger, G.N.; McDonald, T.L.

2010-01-01

The California spotted owl (Strix occidentalis occidentalis) is the only spotted owl subspecies not listed as threatened or endangered under the United States Endangered Species Act despite petitions to list it as threatened. We conducted a meta-analysis of population data for 4 populations in the southern Cascades and Sierra Nevada, California, USA, from 1990 to 2005 to assist a listing evaluation by the United States Fish and Wildlife Service. Our study areas (from N to S) were on the Lassen National Forest (LAS), Eldorado National Forest (ELD), Sierra National Forest (SIE), and Sequoia and Kings Canyon National Parks (SKC). These study areas represented a broad spectrum of habitat and management conditions in these mountain ranges. We estimated apparent survival probability, reproductive output, and rate of population change for spotted owls on individual study areas and for all study areas combined (meta-analysis) using model selection or model-averaging based on maximum-likelihood estimation. We followed a formal protocol to conduct this analysis that was similar to other spotted owl meta-analyses. Consistency of field and analytical methods among our studies reduced confounding methodological effects when evaluating results. We used 991 marked spotted owls in the analysis of apparent survival. Apparent survival probability was higher for adult than for subadult owls. There was little difference in apparent survival between male and female owls. Model-averaged mean estimates of apparent survival probability of adult owls varied from 0.811 ?? 0.021 for females at LAS to 0.890 ?? 0.016 for males at SKC. Apparent survival increased over time for owls of all age classes at LAS and SIE, for adults at ELD, and for second-year subadults and adults at SKC. The meta-analysis of apparent survival, which included only adult owls, confirmed an increasing trend in survival over time. Survival rates were higher for owls on SKC than on the other study areas. We analyzed data from 1,865 observations of reproductive outcomes for female spotted owls. The proportion of subadult females among all territorial females of known age ranged from 0.00 to 0.25 among study areas and years. The proportion of subadults among female spotted owls was negatively related to reproductive output (no. of young fledged/territorial F owl) for ELD and SIE. Eldorado study area and LAS showed an alternate-year trend in reproductive output, with higher output in even-numbered years. Mean annual reproductive output was 0.988 ?? 0.154 for ELD, 0.624 ?? 0.140 for LAS, 0.478 ?? 0.106 for SIE, and 0.555 ?? 0.110 for SKC. Eldorado Study Area exhibited a declining trend and the greatest variation in reproductive output over time, whereas SIE and SKC, which had the lowest reproductive output, had the lowest temporal variation. Meta-analysis confirmed that reproductive output varied among study areas. Reproductive output was highest for adults, followed by second-year subadults, and then by first-year subadults. We used 842 marked subadult and adult owls to estimate population rate of change. Modeling indicated that ??t (??t is the finite rate of population change estimated using the reparameterized JollySeber estimator Pradel 1996) was either stationary (LAS and SIE) or increasing after an initial decrease (ELD and SKC). Mean estimated ??t for the 4 study areas was 1.007 (95 CI 0.9521.066) for ELD; 0.973 (95 CI 0.9461.001) for LAS; 0.992 (95 CI 0.9661.018) for SIE; and 1.006 (95 CI 0.9471.068) for SKC. The best meta-analysis model of population trend indicated that ?? varied across time but was similar in trend among the study areas. Our estimates of realized population change (??t; Franklin et al. 2004), which we estimated as the product 1 ?? ??3 ?? ??4 ?? .?? ??k -1, were based on estimates of ??t from individual study areas and did not require estimating annual population size for each study area. Trends represented the proportion of the population size in the first ye

A FORTRAN program for multivariate survival analysis on the personal computer.

PubMed

Mulder, P G

1988-01-01

In this paper a FORTRAN program is presented for multivariate survival or life table regression analysis in a competing risks' situation. The relevant failure rate (for example, a particular disease or mortality rate) is modelled as a log-linear function of a vector of (possibly time-dependent) explanatory variables. The explanatory variables may also include the variable time itself, which is useful for parameterizing piecewise exponential time-to-failure distributions in a Gompertz-like or Weibull-like way as a more efficient alternative to Cox's proportional hazards model. Maximum likelihood estimates of the coefficients of the log-linear relationship are obtained from the iterative Newton-Raphson method. The program runs on a personal computer under DOS; running time is quite acceptable, even for large samples.

The impact of roads on the demography of grizzly bears in Alberta.

PubMed

Boulanger, John; Stenhouse, Gordon B

2014-01-01

One of the principal factors that have reduced grizzly bear populations has been the creation of human access into grizzly bear habitat by roads built for resource extraction. Past studies have documented mortality and distributional changes of bears relative to roads but none have attempted to estimate the direct demographic impact of roads in terms of both survival rates, reproductive rates, and the interaction of reproductive state of female bears with survival rate. We applied a combination of survival and reproductive models to estimate demographic parameters for threatened grizzly bear populations in Alberta. Instead of attempting to estimate mean trend we explored factors which caused biological and spatial variation in population trend. We found that sex and age class survival was related to road density with subadult bears being most vulnerable to road-based mortality. A multi-state reproduction model found that females accompanied by cubs of the year and/or yearling cubs had lower survival rates compared to females with two year olds or no cubs. A demographic model found strong spatial gradients in population trend based upon road density. Threshold road densities needed to ensure population stability were estimated to further refine targets for population recovery of grizzly bears in Alberta. Models that considered lowered survival of females with dependant offspring resulted in lower road density thresholds to ensure stable bear populations. Our results demonstrate likely spatial variation in population trend and provide an example how demographic analysis can be used to refine and direct conservation measures for threatened species.

The Impact of Roads on the Demography of Grizzly Bears in Alberta

PubMed Central

2014-01-01

One of the principal factors that have reduced grizzly bear populations has been the creation of human access into grizzly bear habitat by roads built for resource extraction. Past studies have documented mortality and distributional changes of bears relative to roads but none have attempted to estimate the direct demographic impact of roads in terms of both survival rates, reproductive rates, and the interaction of reproductive state of female bears with survival rate. We applied a combination of survival and reproductive models to estimate demographic parameters for threatened grizzly bear populations in Alberta. Instead of attempting to estimate mean trend we explored factors which caused biological and spatial variation in population trend. We found that sex and age class survival was related to road density with subadult bears being most vulnerable to road-based mortality. A multi-state reproduction model found that females accompanied by cubs of the year and/or yearling cubs had lower survival rates compared to females with two year olds or no cubs. A demographic model found strong spatial gradients in population trend based upon road density. Threshold road densities needed to ensure population stability were estimated to further refine targets for population recovery of grizzly bears in Alberta. Models that considered lowered survival of females with dependant offspring resulted in lower road density thresholds to ensure stable bear populations. Our results demonstrate likely spatial variation in population trend and provide an example how demographic analysis can be used to refine and direct conservation measures for threatened species. PMID:25532035

Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study.

PubMed

Dawes, Timothy J W; de Marvao, Antonio; Shi, Wenzhe; Fletcher, Tristan; Watson, Geoffrey M J; Wharton, John; Rhodes, Christopher J; Howard, Luke S G E; Gibbs, J Simon R; Rueckert, Daniel; Cook, Stuart A; Wilkins, Martin R; O'Regan, Declan P

2017-05-01

Purpose To determine if patient survival and mechanisms of right ventricular failure in pulmonary hypertension could be predicted by using supervised machine learning of three-dimensional patterns of systolic cardiac motion. Materials and Methods The study was approved by a research ethics committee, and participants gave written informed consent. Two hundred fifty-six patients (143 women; mean age ± standard deviation, 63 years ± 17) with newly diagnosed pulmonary hypertension underwent cardiac magnetic resonance (MR) imaging, right-sided heart catheterization, and 6-minute walk testing with a median follow-up of 4.0 years. Semiautomated segmentation of short-axis cine images was used to create a three-dimensional model of right ventricular motion. Supervised principal components analysis was used to identify patterns of systolic motion that were most strongly predictive of survival. Survival prediction was assessed by using difference in median survival time and area under the curve with time-dependent receiver operating characteristic analysis for 1-year survival. Results At the end of follow-up, 36% of patients (93 of 256) died, and one underwent lung transplantation. Poor outcome was predicted by a loss of effective contraction in the septum and free wall, coupled with reduced basal longitudinal motion. When added to conventional imaging and hemodynamic, functional, and clinical markers, three-dimensional cardiac motion improved survival prediction (area under the receiver operating characteristic curve, 0.73 vs 0.60, respectively; P < .001) and provided greater differentiation according to difference in median survival time between high- and low-risk groups (13.8 vs 10.7 years, respectively; P < .001). Conclusion A machine-learning survival model that uses three-dimensional cardiac motion predicts outcome independent of conventional risk factors in patients with newly diagnosed pulmonary hypertension. Online supplemental material is available for this article.

A score model for predicting post-liver transplantation survival in HBV cirrhosis-related hepatocellular carcinoma recipients: a single center 5-year experience.

PubMed

Wang, Li-Ying; Zheng, Shu-Sen; Xu, Xiao; Wang, Wei-Lin; Wu, Jian; Zhang, Min; Shen, Yan; Yan, Sheng; Xie, Hai-Yang; Chen, Xin-Hua; Jiang, Tian-An; Chen, Fen

2015-02-01

The prognostic prediction of liver transplantation (LT) guides the donor organ allocation. However, there is currently no satisfactory model to predict the recipients' outcome, especially for the patients with HBV cirrhosis-related hepatocellular carcinoma (HCC). The present study was to develop a quantitative assessment model for predicting the post-LT survival in HBV-related HCC patients. Two hundred and thirty-eight LT recipients at the Liver Transplant Center, First Affiliated Hospital, Zhejiang University School of Medicine between 2008 and 2013 were included in this study. Their post-LT prognosis was recorded and multiple risk factors were analyzed using univariate and multivariate analyses in Cox regression. The score model was as follows: 0.114X(Child-Pugh score)-0.002X(positive HBV DNA detection time)+0.647X(number of tumor nodules)+0.055X(max diameter of tumor nodules)+0.231XlnAFP+0.437X(tumor differentiation grade). The receiver operating characteristic curve analysis showed that the area under the curve of the scoring model for predicting the post-LT survival was 0.887. The cut-off value was 1.27, which was associated with a sensitivity of 72.5% and a specificity of 90.7%, respectively. The quantitative score model for predicting post-LT survival proved to be sensitive and specific.

Prognostic significance of positive circumferential resection margin in esophageal cancer: a systematic review and meta-analysis.

PubMed

Wu, Jie; Chen, Qi-Xun; Teng, Li-song; Krasna, Mark J

2014-02-01

To assess the prognostic significance of positive circumferential resection margin on overall survival in patients with esophageal cancer, a systematic review and meta-analysis was performed. Studies were identified from PubMed, EMBASE, and Web of Science. Survival data were extracted from eligible studies to compare overall survival in patients with a positive circumferential resection margin with patients having a negative circumferential resection margin according to the Royal College of Pathologists (RCP) criteria and the College of American Pathologists (CAP) criteria. Survival data were pooled with hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs). A random-effects model meta-analysis on overall survival was performed. The pooled HRs for survival were 1.510 (95% CI, 1.329-1.717; p<0.001) and 2.053 (95% CI, 1.597-2.638; p<0.001) according to the RCP and CAP criteria, respectively. Positive circumferential resection margin was associated with worse survival in patients with T3 stage disease according to the RCP (HR, 1.381; 95% CI, 1.028-1.584; p=0.001) and CAP (HR, 2.457; 95% CI, 1.902-3.175; p<0.001) criteria, respectively. Positive circumferential resection margin was associated with worse survival in patients receiving neoadjuvant therapy according to the RCP (HR, 1.676; 95% CI, 1.023-2.744; p=0.040) and CAP (HR, 1.847; 95% CI, 1.226-2.78; p=0.003) criteria, respectively. Positive circumferential resection margin is associated with poor prognosis in patients with esophageal cancer, particularly in patients with T3 stage disease and patients receiving neoadjuvant therapy. Copyright © 2014 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Use of Serum Transthyretin as a Prognostic Indicator and Predictor of Outcome in Cardiac Amyloid Disease Associated With Wild-Type Transthyretin.

PubMed

Hanson, Jacquelyn L S; Arvanitis, Marios; Koch, Clarissa M; Berk, John L; Ruberg, Frederick L; Prokaeva, Tatiana; Connors, Lawreen H

2018-02-01

Wild-type transthyretin amyloidosis (ATTRwt), an underappreciated cause of heart failure in older adults, is challenging to diagnose and monitor in the absence of validated, disease-specific biomarkers. We examined the prognostic use and survival association of serum TTR (transthyretin) concentration in ATTRwt. Patients with biopsy-proven ATTRwt were retrospectively identified. Serum TTR, cardiac biomarkers, and echocardiographic parameters were assessed at baseline and follow-up evaluations. Statistical analyses included Kaplan-Meier method, Cox proportional hazard survival models, and receiver-operating characteristic curve analysis. Median serum TTR concentration at presentation was 23 mg/dL (n=116). Multivariate predictors of shorter overall survival were decreased TTR, left ventricular ejection fraction and elevated cTn-I (cardiac troponin I); an inclusive model demonstrated superior accuracy in 4-year survival prediction by receiver-operating characteristic curve analysis (area under the curve, 0.77). TTR values lower than the normal limit, <18 mg/dL, were associated with shorter survival (2.8 versus 4.1 years; P =0.03). Further, TTR values at 1- and 2-year follow-ups were significantly lower ( P <0.001) in untreated patients (n=23) compared with those treated with TTR stabilizer, diflunisal (n=12), after baseline evaluation. During 2-year follow-up, unchanged TTR corresponded to increased cTn-I ( P =0.006) in untreated patients; conversely, the diflunisal-treated group showed increased TTR ( P =0.001) and stabilized cTn-I and left ventricular ejection fraction at 1 year. In this series of biopsy-proven ATTRwt, lower baseline serum TTR concentration was associated with shorter survival as an independent predictor of outcome. Longitudinal analysis demonstrated that decreasing TTR corresponded to worsening cardiac function. These data suggest that TTR may be a useful prognostic marker and predictor of outcome in ATTRwt. © 2018 American Heart Association, Inc.

Psychometric Analysis of the Heart Failure Somatic Perception Scale as a Measure of Patient Symptom Perception.

PubMed

Jurgens, Corrine Y; Lee, Christopher S; Riegel, Barbara

Symptoms are known to predict survival among patients with heart failure (HF), but discrepancies exist between patients' and health providers' perceptions of HF symptom burden. The purpose of this study is to quantify the internal consistency, validity, and prognostic value of patient perception of a broad range of HF symptoms using an HF-specific physical symptom measure, the 18-item HF Somatic Perception Scale v. 3. Factor analysis of the HF Somatic Perception Scale was conducted in a convenience sample of 378 patients with chronic HF. Convergent validity was examined using the Physical Limitation subscale of the Kansas City Cardiomyopathy Questionnaire. Divergent validity was examined using the Self-care of HF Index self-care management score. One-year survival based on HF Somatic Perception Scale scores was quantified using Cox regression controlling for Seattle HF Model scores to account for clinical status, therapeutics, and lab values. The sample was 63% male, 85% white, 67% functionally compromised (New York Heart Association class III-IV) with a mean (SD) age of 63 (12.8) years. Internal consistency of the HF Somatic Perception Scale was α = .90. Convergent (r = -0.54, P < .0001) and divergent (r = 0.18, P > .05) validities were supported. Controlling for Seattle HF scores, HF Somatic Perception Scale was a significant predictor of 1-year survival, with those most symptomatic having worse survival (hazard ratio, 1.012; 95% confidence interval, 1.001-1.024; P = .038). Perception of HF symptom burden as measured by the HF Somatic Perception Scale is a significant predictor of survival, contributing additional prognostic value over and above objective Seattle HF Risk Model scores. This analysis suggests that assessment of a broad range of HF symptoms, or those related to dyspnea or early and subtle symptoms, may be useful in evaluating therapeutic outcomes and predicting event-free survival.

Improving Our Ability to Evaluate Underlying Mechanisms of Behavioral Onset and Other Event Occurrence Outcomes: A Discrete-Time Survival Mediation Model

PubMed Central

Fairchild, Amanda J.; Abara, Winston E.; Gottschall, Amanda C.; Tein, Jenn-Yun; Prinz, Ronald J.

2015-01-01

The purpose of this article is to introduce and describe a statistical model that researchers can use to evaluate underlying mechanisms of behavioral onset and other event occurrence outcomes. Specifically, the article develops a framework for estimating mediation effects with outcomes measured in discrete-time epochs by integrating the statistical mediation model with discrete-time survival analysis. The methodology has the potential to help strengthen health research by targeting prevention and intervention work more effectively as well as by improving our understanding of discretized periods of risk. The model is applied to an existing longitudinal data set to demonstrate its use, and programming code is provided to facilitate its implementation. PMID:24296470

The Optimal Timing of Stage-2-Palliation After the Norwood Operation.

PubMed

Meza, James M; Hickey, Edward; McCrindle, Brian; Blackstone, Eugene; Anderson, Brett; Overman, David; Kirklin, James K; Karamlou, Tara; Caldarone, Christopher; Kim, Richard; DeCampli, William; Jacobs, Marshall; Guleserian, Kristine; Jacobs, Jeffrey P; Jaquiss, Robert

2018-01-01

The effect of the timing of stage-2-palliation (S2P) on survival through single ventricle palliation remains unknown. This study investigated the optimal timing of S2P that minimizes pre-S2P attrition and maximizes post-S2P survival. The Congenital Heart Surgeons' Society's critical left ventricular outflow tract obstruction cohort was used. Survival analysis was performed using multiphase parametric hazard analysis. Separate risk factors for death after the Norwood and after S2P were identified. Based on the multivariable models, infants were stratified as low, intermediate, or high risk. Cumulative 2-year, post-Norwood survival was predicted. Optimal timing was determined using conditional survival analysis and plotted as 2-year, post-Norwood survival versus age at S2P. A Norwood operation was performed in 534 neonates from 21 institutions. The S2P was performed in 71%, at a median age of 5.1 months (IQR: 4.3 to 6.0), and 22% died after Norwood. By 5 years after S2P, 10% of infants had died. For low- and intermediate-risk infants, performing S2P after age 3 months was associated with 89% ± 3% and 82% ± 3% 2-year survival, respectively. Undergoing an interval cardiac reoperation or moderate-severe right ventricular dysfunction before S2P were high-risk features. Among high-risk infants, 2-year survival was 63% ± 5%, and even lower when S2P was performed before age 6 months. Performing S2P after age 3 months may optimize survival of low- and intermediate-risk infants. High-risk infants are unlikely to complete three-stage palliation, and early S2P may increase their risk of mortality. We infer that early referral for cardiac transplantation may increase their chance of survival. Copyright © 2018 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Parent-Child Communication and Marijuana Initiation: Evidence Using Discrete-Time Survival Analysis

PubMed Central

Nonnemaker, James M.; Silber-Ashley, Olivia; Farrelly, Matthew C.; Dench, Daniel

2012-01-01

This study supplements existing literature on the relationship between parent-child communication and adolescent drug use by exploring whether parental and/or adolescent recall of specific drug-related conversations differentially impact youth's likelihood of initiating marijuana use. Using discrete-time survival analysis, we estimated the hazard of marijuana initiation using a logit model to obtain an estimate of the relative risk of initiation. Our results suggest that parent-child communication about drug use is either not protective (no effect) or—in the case of youth reports of communication—potentially harmful (leading to increased likelihood of marijuana initiation). PMID:22958867

Development of a new outcome prediction model for Chinese patients with penile squamous cell carcinoma based on preoperative serum C-reactive protein, body mass index, and standard pathological risk factors: the TNCB score group system.

PubMed

Li, Zai-Shang; Chen, Peng; Yao, Kai; Wang, Bin; Li, Jing; Mi, Qi-Wu; Chen, Xiao-Feng; Zhao, Qi; Li, Yong-Hong; Chen, Jie-Ping; Deng, Chuang-Zhong; Ye, Yun-Lin; Zhong, Ming-Zhu; Liu, Zhuo-Wei; Qin, Zi-Ke; Lin, Xiang-Tian; Liang, Wei-Cong; Han, Hui; Zhou, Fang-Jian

2016-04-12

To determine the predictive value and feasibility of the new outcome prediction model for Chinese patients with penile squamous cell carcinoma. The 3-year disease-specific survival (DSS) survival (DSS) was 92.3% in patients with < 8.70 mg/L CRP and 54.9% in those with elevated CRP (P < 0.001). The 3-year DSS was 86.5% in patients with a BMI < 22.6 Kg/m2 and 69.9% in those with a higher BMI (P = 0.025). In a multivariate analysis, pathological T stage (P < 0.001), pathological N stage (P = 0.002), BMI (P = 0.002), and CRP (P = 0.004) were independent predictors of DSS. A new scoring model was developed, consisting of BMI, CRP, and tumor T and N classification. In our study, we found that the addition of the above-mentioned parameters significantly increased the predictive accuracy of the system of the American Joint Committee on Cancer (AJCC) anatomic stage group. The accuracy of the new prediction category was verified. A total of 172 Chinese patients with penile squamous cell cancer were analyzed retrospectively between November 2005 and November 2014. Statistical data analysis was conducted using the nonparametric method. Survival analysis was performed with the log-rank test and the Cox proportional hazard model. Based on regression estimates of significant parameters in multivariate analysis, a new BMI-, CRP- and pathologic factors-based scoring model was developed to predict disease--specific outcomes. The predictive accuracy of the model was evaluated using the internal and external validation. The present study demonstrated that the TNCB score group system maybe a precise and easy to use tool for predicting outcomes in Chinese penile squamous cell carcinoma patients.

Steroid Avoidance in Pediatric Heart Transplantation Results in Excellent Graft Survival

PubMed Central

Auerbach, Scott R.; Gralla, Jane; Campbell, David N.; Miyamoto, Shelley D.; Pietra, Biagio A.

2018-01-01

Background Maintenance steroid (MS) use in pediatric heart transplantation (HT) varies across centers. The purpose of this study was to evaluate the impact of steroid-free maintenance immunosuppression (SF) on graft outcomes in pediatric HT. Methods Patients younger than 18 years in the United States undergoing a first HT during 1990 to 2010 were analyzed for conditional 30-day graft loss (death or repeat HT) and death based on MS use by multivariable analysis. A propensity score was then given to each patient using a logistic model, and propensity matching was performed using pre-HT risk factors, induction therapy, and nonsteroid maintenance immunosuppression. Kaplan-Meier graft and patient survival probabilities by MS use were then calculated. Results Of 4894 patients, 3962 (81%) were taking MS and 932 (19%) SF. Of the 4530 alive at 30 days after HT, 3694 (82%) and 836 (18%) were in the MS and SF groups, respectively. Unmatched multivariable analysis showed no difference in 30-day conditional graft survival between MS and SF groups (hazard ratio=1.08, 95% confidence interval=0.93-1.24; P=0.33). Propensity matching resulted in 462 patients in each MS and SF group. Propensity-matched Kaplan-Meier survival analysis showed no difference in graft or patient survival between groups (P=0.3 and P=0.16, respectively). Conclusions We found no difference in graft survival between SF patients and those taking MS. An SF regimen in pediatric HT avoids potential complications of steroid use without compromising graft survival, even after accounting for pre-HT risk factors. PMID:24389908

Identification and estimation of survivor average causal effects.

PubMed

Tchetgen Tchetgen, Eric J

2014-09-20

In longitudinal studies, outcomes ascertained at follow-up are typically undefined for individuals who die prior to the follow-up visit. In such settings, outcomes are said to be truncated by death and inference about the effects of a point treatment or exposure, restricted to individuals alive at the follow-up visit, could be biased even if as in experimental studies, treatment assignment were randomized. To account for truncation by death, the survivor average causal effect (SACE) defines the effect of treatment on the outcome for the subset of individuals who would have survived regardless of exposure status. In this paper, the author nonparametrically identifies SACE by leveraging post-exposure longitudinal correlates of survival and outcome that may also mediate the exposure effects on survival and outcome. Nonparametric identification is achieved by supposing that the longitudinal data arise from a certain nonparametric structural equations model and by making the monotonicity assumption that the effect of exposure on survival agrees in its direction across individuals. A novel weighted analysis involving a consistent estimate of the survival process is shown to produce consistent estimates of SACE. A data illustration is given, and the methods are extended to the context of time-varying exposures. We discuss a sensitivity analysis framework that relaxes assumptions about independent errors in the nonparametric structural equations model and may be used to assess the extent to which inference may be altered by a violation of key identifying assumptions. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.

Identification and estimation of survivor average causal effects

PubMed Central

Tchetgen, Eric J Tchetgen

2014-01-01

In longitudinal studies, outcomes ascertained at follow-up are typically undefined for individuals who die prior to the follow-up visit. In such settings, outcomes are said to be truncated by death and inference about the effects of a point treatment or exposure, restricted to individuals alive at the follow-up visit, could be biased even if as in experimental studies, treatment assignment were randomized. To account for truncation by death, the survivor average causal effect (SACE) defines the effect of treatment on the outcome for the subset of individuals who would have survived regardless of exposure status. In this paper, the author nonparametrically identifies SACE by leveraging post-exposure longitudinal correlates of survival and outcome that may also mediate the exposure effects on survival and outcome. Nonparametric identification is achieved by supposing that the longitudinal data arise from a certain nonparametric structural equations model and by making the monotonicity assumption that the effect of exposure on survival agrees in its direction across individuals. A novel weighted analysis involving a consistent estimate of the survival process is shown to produce consistent estimates of SACE. A data illustration is given, and the methods are extended to the context of time-varying exposures. We discuss a sensitivity analysis framework that relaxes assumptions about independent errors in the nonparametric structural equations model and may be used to assess the extent to which inference may be altered by a violation of key identifying assumptions. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:24889022

Outcomes of Technical Variant Liver Transplantation versus Whole Liver Transplantation for Pediatric Patients: A Meta-Analysis.

PubMed

Ye, Hui; Zhao, Qiang; Wang, Yufang; Wang, Dongping; Zheng, Zhouying; Schroder, Paul Michael; Lu, Yao; Kong, Yuan; Liang, Wenhua; Shang, Yushu; Guo, Zhiyong; He, Xiaoshun

2015-01-01

To overcome the shortage of appropriate-sized whole liver grafts for children, technical variant liver transplantation has been practiced for decades. We perform a meta-analysis to compare the survival rates and incidence of surgical complications between pediatric whole liver transplantation and technical variant liver transplantation. To identify relevant studies up to January 2014, we searched PubMed/Medline, Embase, and Cochrane library databases. The primary outcomes measured were patient and graft survival rates, and the secondary outcomes were the incidence of surgical complications. The outcomes were pooled using a fixed-effects model or random-effects model. The one-year, three-year, five-year patient survival rates and one-year, three-year graft survival rates were significantly higher in whole liver transplantation than technical variant liver transplantation (OR = 1.62, 1.90, 1.65, 1.78, and 1.62, respectively, p<0.05). There was no significant difference in five-year graft survival rate between the two groups (OR = 1.47, p = 0.10). The incidence of portal vein thrombosis and biliary complications were significantly lower in the whole liver transplantation group (OR = 0.45 and 0.42, both p<0.05). The incidence of hepatic artery thrombosis was comparable between the two groups (OR = 1.21, p = 0.61). Pediatric whole liver transplantation is associated with better outcomes than technical variant liver transplantation. Continuing efforts should be made to minimize surgical complications to improve the outcomes of technical variant liver transplantation.

Statistical methods for astronomical data with upper limits. I - Univariate distributions

NASA Technical Reports Server (NTRS)

Feigelson, E. D.; Nelson, P. I.

1985-01-01

The statistical treatment of univariate censored data is discussed. A heuristic derivation of the Kaplan-Meier maximum-likelihood estimator from first principles is presented which results in an expression amenable to analytic error analysis. Methods for comparing two or more censored samples are given along with simple computational examples, stressing the fact that most astronomical problems involve upper limits while the standard mathematical methods require lower limits. The application of univariate survival analysis to six data sets in the recent astrophysical literature is described, and various aspects of the use of survival analysis in astronomy, such as the limitations of various two-sample tests and the role of parametric modelling, are discussed.

Using population models to evaluate management alternatives for Gulf Striped Bass

USGS Publications Warehouse

Aspinwall, Alexander P.; Irwin, Elise R.; Lloyd, M. Clint

2017-01-01

Interstate management of Gulf Striped Bass Morone saxatilis has involved a thirty-year cooperative effort involving Federal and State agencies in Georgia, Florida and Alabama (Apalachicola-Chattahoochee-Flint Gulf Striped Bass Technical Committee). The Committee has recently focused on developing an adaptive framework for conserving and restoring Gulf Striped Bass in the Apalachicola, Chattahoochee, and Flint River (ACF) system. To evaluate the consequences and tradeoffs among management activities, population models were used to inform management decisions. Stochastic matrix models were constructed with varying recruitment and stocking rates to simulate effects of management alternatives on Gulf Striped Bass population objectives. An age-classified matrix model that incorporated stock fecundity estimates and survival estimates was used to project population growth rate. In addition, combinations of management alternatives (stocking rates, Hydrilla control, harvest regulations) were evaluated with respect to how they influenced Gulf Striped Bass population growth. Annual survival and mortality rates were estimated from catch-curve analysis, while fecundity was estimated and predicted using a linear least squares regression analysis of fish length versus egg number from hatchery brood fish data. Stocking rates and stocked-fish survival rates were estimated from census data. Results indicated that management alternatives could be an effective approach to increasing the Gulf Striped Bass population. Population abundance was greatest under maximum stocking effort, maximum Hydrilla control and a moratorium. Conversely, population abundance was lowest under no stocking, no Hydrilla control and the current harvest regulation. Stocking rates proved to be an effective management strategy; however, low survival estimates of stocked fish (1%) limited the potential for population growth. Hydrilla control increased the survival rate of stocked fish and provided higher estimates of population abundances than maximizing the stocking rate. A change in the current harvest regulation (50% harvest regulation) was not an effective alternative to increasing the Gulf Striped Bass population size. Applying a moratorium to the Gulf Striped Bass fishery increased survival rates from 50% to 74% and resulted in the largest population growth of the individual management alternatives. These results could be used by the Committee to inform management decisions for other populations of Striped Bass in the Gulf Region.

Anterior Aortic Plane Systolic Excursion: A Novel Indicator of Transplant-Free Survival in Systemic Light-Chain Amyloidosis.

PubMed

Ochs, Marco M; Riffel, Johannes; Kristen, Arnt V; Hegenbart, Ute; Schönland, Stefan; Hardt, Stefan E; Katus, Hugo A; Mereles, Derliz; Buss, Sebastian J

2016-12-01

Anterior aortic plane systolic excursion (AAPSE) was evaluated in the present pilot study as a novel echocardiographic indicator of transplant-free survival in patients with systemic light-chain amyloidosis. Eighty-nine patients with light-chain amyloidosis were included in the post-hoc analysis. A subgroup of 54 patients with biopsy-proven cardiac amyloid infiltration were compared with 41 healthy individuals to evaluate the discriminative ability of echocardiographic findings. AAPSE is defined as the systolic excursion of the anterior aortic margin. To quantify AAPSE, the M-mode cursor was placed on the aortic valve plane in parasternal long-axis view at end-diastole. Index echocardiography had been performed before chemotherapy. Median follow-up duration was 2.4 years. The primary combined end point was heart transplantation or overall death. Mean AAPSE was 14 ± 2 mm in healthy individuals (mean age=57 ± 10 years; 56% men; BMI=25 ± 4 kg/m 2 ). AAPSE < 11 mm separated patients from age-, gender-, and BMI-matched control subjects with 93% sensitivity and 97% specificity. Median transplant-free survival of patients with AAPSE < 5 mm was 0.7 versus 4.8 years (P = .0001). AAPSE was an independent indicator of transplant-free survival in multivariate Cox regression (echocardiographic model: hazard ratio=0.72 [P = .03]; biomarker model: hazard ratio=0.62 [P = .0001]). Sequential regression analysis suggested incremental power of AAPSE as a marker of transplant-free survival. An ejection fraction-based model with an overall χ 2 value of 22.8 was improved by the addition of log NT-proBNP (χ 2  = 32.6, P < .005), troponin-T (χ 2  = 39.6, P < .01), and AAPSE (χ 2  = 54.0, P < .0001). AAPSE is suggested as an indicator of transplant-free survival in patients with systemic light-chain amyloidosis. AAPSE provided significant incremental value to established staging models. Copyright © 2016 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.

Integration of manatee life-history data and population modeling

USGS Publications Warehouse

Eberhardt, L.L.; O'Shea, Thomas J.; O'Shea, Thomas J.; Ackerman, B.B.; Percival, H. Franklin

1995-01-01

Aerial counts and the number of deaths have been a major focus of attention in attempts to understand the population status of the Florida manatee (Trichechus manatus latirostris). Uncertainties associated with these data have made interpretation difficult. However, knowledge of manatee life-history attributes increased and now permits the development of a population model. We describe a provisional model based on the classical approach of Lotka. Parameters in the model are based on data from'other papers in this volume and draw primarily on observations from the Crystal River, Blue Spring, and Adantic Coast areas. The model estimates X (the finite rate ofincrease) at each study area, and application ofthe delta method provides estimates of variance components and partial derivatives ofX with respectto key input parameters (reproduction, adult survival, and early survival). In some study areas, only approximations of some parameters are available. Estimates of X and coefficients of variation (in parentheses) of manatees were 1.07 (0.009) in the Crystal River, 1.06 (0.012) at Blue Spring, and 1.01 (0.012) on the Atlantic Coast. Changing adult survival has a major effect on X. Early-age survival has the smallest effect. Bootstrap comparisons of population growth estimates from trend counts in the Crystal River and at Blue Spring and the reproduction and survival data suggest that the higher, observed rates from counts are probably not due to chance. Bootstrapping for variance estimates based on reproduction and survival data from manatees at Blue Spring and in the Crystal River provided estimates of X, adult survival, and rates of reproduction that were similar to those obtained by other methods. Our estimates are preliminary and suggestimprovements for future data collection and analysis. However, results support efforts to reduce mortality as the most effective means to promote the increased growth necessary for the eventual recovery of the Florida manatee population.

Combining Gene Signatures Improves Prediction of Breast Cancer Survival

PubMed Central

Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

2011-01-01

Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast cancer survival. The presented methodology is broadly applicable to breast cancer risk assessment using any new identified gene set. PMID:21423775

A comparison between the use of Cox regression and the use of partial least squares-Cox regression to predict the survival of kidney-transplant patients

NASA Astrophysics Data System (ADS)

Solimun

2017-05-01

The aim of this research is to model survival data from kidney-transplant patients using the partial least squares (PLS)-Cox regression, which can both meet and not meet the no-multicollinearity assumption. The secondary data were obtained from research entitled "Factors affecting the survival of kidney-transplant patients". The research subjects comprised 250 patients. The predictor variables consisted of: age (X1), sex (X2); two categories, prior hemodialysis duration (X3), diabetes (X4); two categories, prior transplantation number (X5), number of blood transfusions (X6), discrepancy score (X7), use of antilymphocyte globulin(ALG) (X8); two categories, while the response variable was patient survival time (in months). Partial least squares regression is a model that connects the predictor variables X and the response variable y and it initially aims to determine the relationship between them. Results of the above analyses suggest that the survival of kidney transplant recipients ranged from 0 to 55 months, with 62% of the patients surviving until they received treatment that lasted for 55 months. The PLS-Cox regression analysis results revealed that patients' age and the use of ALG significantly affected the survival time of patients. The factor of patients' age (X1) in the PLS-Cox regression model merely affected the failure probability by 1.201. This indicates that the probability of dying for elderly patients with a kidney transplant is 1.152 times higher than that for younger patients.

Apoptotic pathways of epothilone BMS 310705.

PubMed

Uyar, Denise; Takigawa, Nagio; Mekhail, Tarek; Grabowski, Dale; Markman, Maurie; Lee, Francis; Canetta, Renzo; Peck, Ron; Bukowski, Ronald; Ganapathi, Ram

2003-10-01

BMS 310705 is a novel water-soluble analog of epothilone B currently in phase I clinical evaluation in the treatment of malignancies such as ovarian, renal, bladder, and lung carcinoma. Using an early passage cell culture model derived from the ascites of a patient clinically refractory to platinum/paclitaxel therapy, we evaluated the pathway of caspase-mediated apoptosis. Cells were treated for 1 h and subsequently evaluated for apoptosis, survival, and caspase activity. Apoptosis was determined by fluorescent microscopy. Caspase-3, -8, and -9 activities were determined by fluorometry using target tetrapeptide substrates. Mitochondrial release of cytochrome c was determined by immunoblot analysis. After treatment with BMS 310705, apoptosis was confirmed in >25% of cells at 24 h. Survival was significantly lower (P < 0.02) in cells treated with 0.05 micro M BMS 310705 vs paclitaxel. Analysis revealed an increase of caspase-9 and -3 activity; no caspase -8 activity was observed. Release of cytochrome c was detected at 12 h following treatment. SN-38 and topotecan failed to induce apoptosis. BMS 310705 induces significant apoptosis, decreases survival, and utilizes the mitochondrial-mediated pathway for apoptosis in this model.

Investigation of the factors influencing the survival of Bifidobacterium longum in model acidic solutions and fruit juices.

PubMed

Nualkaekul, Sawaminee; Salmeron, Ivan; Charalampopoulos, Dimitris

2011-12-01

The survival of Bifidobacterium longum NCIMB 8809 was studied during refrigerated storage for 6weeks in model solutions, based on which a mathematical model was constructed describing cell survival as a function of pH, citric acid, protein and dietary fibre. A Central Composite Design (CCD) was developed studying the influence of four factors at three levels, i.e., pH (3.2-4), citric acid (2-15g/l), protein (0-10g/l), and dietary fibre (0-8g/l). In total, 31 experimental runs were carried out. Analysis of variance (ANOVA) of the regression model demonstrated that the model fitted well the data. From the regression coefficients it was deduced that all four factors had a statistically significant (P<0.05) negative effect on the log decrease [log10N0 week-log10N6 week], with the pH and citric acid being the most influential ones. Cell survival during storage was also investigated in various types of juices, including orange, grapefruit, blackcurrant, pineapple, pomegranate and strawberry. The highest cell survival (less than 0.4log decrease) after 6weeks of storage was observed in orange and pineapple, both of which had a pH of about 3.8. Although the pH of grapefruit and blackcurrant was similar (pH ∼3.2), the log decrease of the former was ∼0.5log, whereas of the latter was ∼0.7log. One reason for this could be the fact that grapefruit contained a high amount of citric acid (15.3g/l). The log decrease in pomegranate and strawberry juices was extremely high (∼8logs). The mathematical model was able to predict adequately the cell survival in orange, grapefruit, blackcurrant, and pineapple juices. However, the model failed to predict the cell survival in pomegranate and strawberry, most likely due to the very high levels of phenolic compounds in these two juices. Copyright © 2011 Elsevier Ltd. All rights reserved.

Pinyon and juniper encroachment into sagebrush ecosystems impacts distribution and survival of greater sage-grouse

USGS Publications Warehouse

Coates, Peter S.; Prochazka, Brian; Ricca, Mark; Gustafson, K. Ben; Ziegler, Pilar T.; Casazza, Michael L.

2017-01-01

In sagebrush (Artemisia spp.) ecosystems, encroachment of pinyon (Pinus spp.) and juniper (Juniperus spp.; hereafter, “pinyon-juniper”) trees has increased dramatically since European settlement. Understanding the impacts of this encroachment on behavioral decisions, distributions, and population dynamics of greater sage-grouse (Centrocercus urophasianus) and other sagebrush obligate species could help benefit sagebrush ecosystem management actions. We employed a novel two-stage Bayesian model that linked avoidance across different levels of pinyon-juniper cover to sage-grouse survival. Our analysis relied on extensive telemetry data collected across 6 yr and seven subpopulations within the Bi-State Distinct Population Segment (DPS), on the border of Nevada and California. The first model stage indicated avoidance behavior for all canopy cover classes on average, but individual grouse exhibited a high degree of heterogeneity in avoidance behavior of the lowest cover class (e.g., scattered isolated trees). The second stage modeled survival as a function of estimated avoidance parameters and indicated increased survival rates for individuals that exhibited avoidance of the lowest cover class. A post hoc frailty analysis revealed the greatest increase in hazard (i.e., mortality risk) occurred in areas with scattered isolated trees consisting of relatively high primary plant productivity. Collectively, these results provide clear evidence that local sage-grouse distributions and demographic rates are influenced by pinyon-juniper, especially in habitats with higher primary productivity but relatively low and seemingly benign tree cover. Such areas may function as ecological traps that convey attractive resources but adversely affect population vital rates. To increase sage-grouse survival, our model predictions support reducing actual pinyon-juniper cover as low as 1.5%, which is lower than the published target of 4.0%. These results may represent effects of pinyon-juniper cover in areas with similar ecological conditions to those of the Bi-State DPS, where populations occur at relatively high elevations and pinyon-juniper is abundant and widespread.

A joint frailty-copula model between tumour progression and death for meta-analysis.

PubMed

Emura, Takeshi; Nakatochi, Masahiro; Murotani, Kenta; Rondeau, Virginie

2017-12-01

Dependent censoring often arises in biomedical studies when time to tumour progression (e.g., relapse of cancer) is censored by an informative terminal event (e.g., death). For meta-analysis combining existing studies, a joint survival model between tumour progression and death has been considered under semicompeting risks, which induces dependence through the study-specific frailty. Our paper here utilizes copulas to generalize the joint frailty model by introducing additional source of dependence arising from intra-subject association between tumour progression and death. The practical value of the new model is particularly evident for meta-analyses in which only a few covariates are consistently measured across studies and hence there exist residual dependence. The covariate effects are formulated through the Cox proportional hazards model, and the baseline hazards are nonparametrically modeled on a basis of splines. The estimator is then obtained by maximizing a penalized log-likelihood function. We also show that the present methodologies are easily modified for the competing risks or recurrent event data, and are generalized to accommodate left-truncation. Simulations are performed to examine the performance of the proposed estimator. The method is applied to a meta-analysis for assessing a recently suggested biomarker CXCL12 for survival in ovarian cancer patients. We implement our proposed methods in R joint.Cox package.

Effects of neck bands on survival of greater snow geese

USGS Publications Warehouse

Menu, S.; Hestbeck, J.B.; Gauthier, G.; Reed, A.

2000-01-01

Neck bands are a widely used marker in goose research. However, few studies have investigated a possible negative effect of this marker on survival. We tested the effect of neck bands on the survival of adult female greater snow geese (Chen caerulescens atlantica) by marking birds with either a neck band and a metal leg band or a leg band only on Bylot Island (Nunavut, formerly included in the Northwest Territories, Canada) from 1990 to 1996. Annual survival was estimated using leg-band recoveries in fall and winter and using neck-band sightings in spring and fall. Recapture rates were estimated using summer recaptures. Using recovery data, the selected model yielded a survival similar for the neck-banded and leg-banded only birds (S = 0.845 ?? 0.070 vs. S = 0.811 ?? 0.107). The hypothesis of equality of survival between the 2 groups was easily accepted under most constraints imposed on survival or recovery rates. However, failure to account for a different direct recovery rate for neck-banded birds would lead us to incorrectly conclude a possible negative effect of neck bands on survival. Using sighting data, mean annual survival of neck-banded birds was independently estimated at 0.833 ?? 0.057, a value very similar to that estimated with band-recovery analysis. Raw recapture rates during summer were significantly lower for neck-banded birds compared to those marked with leg bands only (4.6% vs. 12.1%), but in this analysis, survival, site fidelity, reproductive status, and recapture rates were confounded. We conclude that neck bands did not affect survival of greater snow geese, but could possibly affect other demographic traits such as breeding propensity and emigration.

Demographic analysis of dormancy and survival in the terrestrial orchid Cypripedium reginae

USGS Publications Warehouse

Kery, Marc; Gregg, Katharine B.

2004-01-01

1. We use capture-recapture models to estimate the fraction of dormant ramets, survival and state transition rates, and to identify factors affecting these rates, for the terrestrial orchid Cypripedium reginae. We studied two populations in West Virginia, USA, for 11 years and investigated relationships between grazing and demography. Abe Run's population was small, with moderate herbivory by deer and relatively constant population size. The population at Big Draft was of medium size, with heavy deer grazing, and a sharply declining number of flowering plants up to the spring before our study started, when the population was fenced. 2. We observed dormant episodes lasting from 1 to 4 years. At Abe Run and Big Draft, 32.5% and 7.4% of ramets, respectively, were dormant at least once during the study period for an average of 1.6 and 1.3 years, respectively. We estimated the annual fraction of ramets in the dormant state at 12.3% (95% CI 9.5-15.8%) at Abe Run and at 1.8% (95% CI 1.2-2.6%) at Big Draft. Transition rates between the dormant, vegetative and flowering life-states did not vary between years in either population. Most surviving ramets remained in the same state from one year to the next. Survival rates were constant at Abe Run (0.96, 95% CI 0.93-0.97), but varied between years at Big Draft (0.89-0.99, mean 0.95). 3. At Big Draft, we found neither a temporal trend in survival after cessation of grazing, nor relationships between survival and the number of spring frost days or cumulative precipitation during the current or the previous 12 months. However, analysis of precipitation on a 3-month basis revealed a positive relationship between survival and precipitation during the spring (March-May) of the previous year. 4. Relationship between climate and the population dynamics of orchids may have to be studied with a fine temporal resolution, and considering possible time lags. Capture-recapture modelling provides a comprehensive and flexible framework for demographic analysis of plants with dormancy.

Failure Time Analysis of Office System Use.

ERIC Educational Resources Information Center

Cooper, Michael D.

1991-01-01

Develops mathematical models to characterize the probability of continued use of an integrated office automation system and tests these models on longitudinal data collected from 210 individuals using the IBM Professional Office System (PROFS) at the University of California at Berkeley. Analyses using survival functions and proportional hazard…

Superior Prognostic Value of Cumulative Intracranial Tumor Volume Relative to Largest Intracranial Tumor Volume for Stereotactic Radiosurgery-Treated Brain Metastasis Patients.

PubMed

Hirshman, Brian R; Wilson, Bayard; Ali, Mir Amaan; Proudfoot, James A; Koiso, Takao; Nagano, Osamu; Carter, Bob S; Serizawa, Toru; Yamamoto, Masaaki; Chen, Clark C

2018-04-01

Two intracranial tumor volume variables have been shown to prognosticate survival of stereotactic-radiosurgery-treated brain metastasis patients: the largest intracranial tumor volume (LITV) and the cumulative intracranial tumor volume (CITV). To determine whether the prognostic value of the Scored Index for Radiosurgery (SIR) model can be improved by replacing one of its components-LITV-with CITV. We compared LITV and CITV in terms of their survival prognostication using a series of multivariable models that included known components of the SIR: age, Karnofsky Performance Score, status of extracranial disease, and the number of brain metastases. Models were compared using established statistical measures, including the net reclassification improvement (NRI > 0) and integrated discrimination improvement (IDI). The analysis was performed in 2 independent cohorts, each consisting of ∼3000 patients. In both cohorts, CITV was shown to be independently predictive of patient survival. Replacement of LITV with CITV in the SIR model improved the model's ability to predict 1-yr survival. In the first cohort, the CITV model showed an NRI > 0 improvement of 0.2574 (95% confidence interval [CI] 0.1890-0.3257) and IDI of 0.0088 (95% CI 0.0057-0.0119) relative to the LITV model. In the second cohort, the CITV model showed a NRI > 0 of 0.2604 (95% CI 0.1796-0.3411) and IDI of 0.0051 (95% CI 0.0029-0.0073) relative to the LITV model. After accounting for covariates within the SIR model, CITV offers superior prognostic value relative to LITV for stereotactic radiosurgery-treated brain metastasis patients.

The impact of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio among patients with intrahepatic cholangiocarcinoma.

PubMed

Buettner, Stefan; Spolverato, Gaya; Kimbrough, Charles W; Alexandrescu, Sorin; Marques, Hugo P; Lamelas, Jorge; Aldrighetti, Luca; Gamblin, T Clark; Maithel, Shishir K; Pulitano, Carlo; Weiss, Matthew; Bauer, Todd W; Shen, Feng; Poultsides, George A; Marsh, J Wallis; IJzermans, Jan N M; Koerkamp, Bas Groot; Pawlik, Timothy M

2018-06-11

Neutrophil-to-lymphocyte ratio and platelets-to-lymphocyte ratio may be host factors associated with prognosis. We sought to determine whether neutrophil-to-lymphocyte and platelets-to-lymphocyte ratio were associated with overall survival among patients undergoing surgery for intrahepatic cholangiocarcinoma. Patients who underwent resection for intrahepatic cholangiocarcinoma between 1990 and 2015 were identified from 12 major centers. Clinicopathologic factors and overall survival were compared among patients stratified by neutrophil-to-lymphocyte ratio and platelets-to-lymphocyte ratio. Risk factors identified on multivariable analysis were included in a prognostic model and the discrimination was assessed using Harrell's concordance index (C index). A total of 991 patients were identified. Median neutrophil-to-lymphocyte ratio and platelets-to-lymphocyte ratio were 2.7 (interquartile range [IQR]: 2.0-4.0) and 109.6 (IQR: 72.4-158.8), respectively. Preoperative neutrophil-to-lymphocyte ratio was elevated (≥5) in 100 patients (10.0%) and preoperative platelets-to-lymphocyte ratio (≥190) in 94 patients (15.2%). Patients with low and high neutrophil-to-lymphocyte ratio and platelets-to-lymphocyte ratio generally had similar baseline characteristics with regard to tumor characteristics. Overall survival was 37.7 months (95% confidence interval [CI]: 32.7-42.6); 1-, 3-, and 5-year overall survival was 78.8%, 51.6%, and 39.3%, respectively. Patients with an neutrophil-to-lymphocyte ratio <5 had a median survival of 47.1 months (95% CI: 37.9-53.3) compared with a median survival of 21.9 months (95% CI: 4.8-39.1) among patients with an neutrophil-to-lymphocyte ratio ≥5 (P = .001). In contrast, patients who had a platelets-to-lymphocyte ratio <190 vs platelets-to-lymphocyte ratio ≥190 had comparable long-term survival (P > .05). On multivariable analysis, an elevated neutrophil-to-lymphocyte ratio was independently associated with decreased overall survival (hazard ratio: 1.04, 95% CI: 1.01-1.07; P = .002). Patients could be stratified into low- versus high-risk groups based on standard tumor-specific factors such as lymph node status, tumor size, number, and vascular invasion (C index 0.62). When neutrophil-to-lymphocyte ratio was added to the prognostic model, the discriminatory ability of the model improved (C index 0.71). Elevated neutrophil-to-lymphocyte ratio was independently associated with worse overall survival and improved the prognostic estimation of long-term survival among patients with intrahepatic cholangiocarcinoma undergoing resection. Copyright © 2018 Elsevier Inc. All rights reserved.

Reducing bias in survival under non-random temporary emigration

USGS Publications Warehouse

Peñaloza, Claudia L.; Kendall, William L.; Langtimm, Catherine Ann

2014-01-01

Despite intensive monitoring, temporary emigration from the sampling area can induce bias severe enough for managers to discard life-history parameter estimates toward the terminus of the times series (terminal bias). Under random temporary emigration unbiased parameters can be estimated with CJS models. However, unmodeled Markovian temporary emigration causes bias in parameter estimates and an unobservable state is required to model this type of emigration. The robust design is most flexible when modeling temporary emigration, and partial solutions to mitigate bias have been identified, nonetheless there are conditions were terminal bias prevails. Long-lived species with high adult survival and highly variable non-random temporary emigration present terminal bias in survival estimates, despite being modeled with the robust design and suggested constraints. Because this bias is due to uncertainty about the fate of individuals that are undetected toward the end of the time series, solutions should involve using additional information on survival status or location of these individuals at that time. Using simulation, we evaluated the performance of models that jointly analyze robust design data and an additional source of ancillary data (predictive covariate on temporary emigration, telemetry, dead recovery, or auxiliary resightings) in reducing terminal bias in survival estimates. The auxiliary resighting and predictive covariate models reduced terminal bias the most. Additional telemetry data was effective at reducing terminal bias only when individuals were tracked for a minimum of two years. High adult survival of long-lived species made the joint model with recovery data ineffective at reducing terminal bias because of small-sample bias. The naïve constraint model (last and penultimate temporary emigration parameters made equal), was the least efficient, though still able to reduce terminal bias when compared to an unconstrained model. Joint analysis of several sources of data improved parameter estimates and reduced terminal bias. Efforts to incorporate or acquire such data should be considered by researchers and wildlife managers, especially in the years leading up to status assessments of species of interest. Simulation modeling is a very cost effective method to explore the potential impacts of using different sources of data to produce high quality demographic data to inform management.

Reassessment of the relationship between M-protein decrement and survival in multiple myeloma.

PubMed

Palmer, M; Belch, A; Hanson, J; Brox, L

1989-01-01

The relationship between percentage M-protein decrement and survival is assessed in 134 multiple myeloma patients. The correlation did not achieve statistical significance (P = 0.069). Multivariate analysis using the Cox proportional hazards model, including a number of previously recognised prognostic factors, showed only percentage M-protein decrement, creatinine and haemoglobin to be significantly correlated with survival. However, the R'-statistic for each of these variables was low, indicating that their prognostic power is weak. We conclude that neither the percentage M-protein decrement nor the response derived from it can be used as an accurate means of assessing the efficacy of treatment in myeloma. Mature survival data alone should be used for this purpose.

Reassessment of the relationship between M-protein decrement and survival in multiple myeloma.

PubMed Central

Palmer, M.; Belch, A.; Hanson, J.; Brox, L.

1989-01-01

The relationship between percentage M-protein decrement and survival is assessed in 134 multiple myeloma patients. The correlation did not achieve statistical significance (P = 0.069). Multivariate analysis using the Cox proportional hazards model, including a number of previously recognised prognostic factors, showed only percentage M-protein decrement, creatinine and haemoglobin to be significantly correlated with survival. However, the R'-statistic for each of these variables was low, indicating that their prognostic power is weak. We conclude that neither the percentage M-protein decrement nor the response derived from it can be used as an accurate means of assessing the efficacy of treatment in myeloma. Mature survival data alone should be used for this purpose. PMID:2757916

Primary tumor resection in metastatic breast cancer: A propensity-matched analysis, 1988-2011 SEER data base.

PubMed

Vohra, Nasreen A; Brinkley, Jason; Kachare, Swapnil; Muzaffar, Mahvish

2018-03-02

Primary tumor resection (PTR) in metastatic breast cancer is not a standard treatment modality, and its impact on survival is conflicting. The primary objective of this study was to analyze impact of PTR on survival in metastatic patients with breast cancer. A retrospective study of metastatic patients with breast cancer was conducted using the 1988-2011 Surveillance, Epidemiology, and End Results (SEER) data base. Cox proportional hazards regression models were used to evaluate the relationship between PTR and survival and to adjust for the heterogeneity between the groups, and a propensity score-matched analysis was also performed. A total of 29 916 patients with metastatic breast cancer were included in the study, and 15 129 (51%) of patients underwent primary tumor resection, and 14 787 (49%) patients did not undergo surgery. Overall, decreasing trend in PTR for metastatic breast cancer in last decades was noted. Primary tumor resection was associated with a longer median OS (34 vs 18 months). In a propensity score-matched analysis, prognosis was also more favorable in the resected group (P = .0017). Primary tumor resection in metastatic breast cancer was associated with survival improvement, and the improvement persisted in propensity-matched analysis. © 2018 Wiley Periodicals, Inc.

Dual oxidase 1: A predictive tool for the prognosis of hepatocellular carcinoma patients.

PubMed

Chen, Shengsen; Ling, Qingxia; Yu, Kangkang; Huang, Chong; Li, Ning; Zheng, Jianming; Bao, Suxia; Cheng, Qi; Zhu, Mengqi; Chen, Mingquan

2016-06-01

Dual oxidase 1 (DUOX1), which is the main source of reactive oxygen species (ROS) production in the airway, can be silenced in human lung cancer and hepatocellular carcinomas. However, the prognostic value of DUOX1 expression in hepatocellular carcinoma patients is still unclear. We investigated the prognostic value of DUOX1 expression in liver cancer patients. DUOX1 mRNA expression was determined in tumor tissues and non-tumor tissues by real‑time PCR. For evaluation of the prognostic value of DUOX1 expression, Kaplan-Meier method and Cox's proportional hazards model (univariate analysis and multivariate analysis) were employed. A simple risk score was devised by using significant variables obtained from the Cox's regression analysis to further predict the HCC patient prognosis. We observed a reduced DUOX1 mRNA level in the cancer tissues in comparison to the non‑cancer tissues. More importantly, Kaplan-Meier analysis showed that patients with high DUOX1 expression had longer disease-free survival and overall survival compared with those with low expression of DUOX1. Cox's regression analysis indicated that DUOX1 expression, age, and intrahepatic metastasis may be significant prognostic factors for disease-free survival and overall survival. Finally, we found that patients with total scores of >2 and >1 were more likely to relapse and succumb to the disease than patients whose total scores were ≤2 and ≤1. In conclusion, DUOX1 expression in liver tumors is a potential prognostic tool for patients. The risk scoring system is useful for predicting the survival of liver cancer patients after tumor resection.

NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data.

PubMed

Zou, Meng; Liu, Zhaoqi; Zhang, Xiang-Sun; Wang, Yong

2015-10-15

In prognosis and survival studies, an important goal is to identify multi-biomarker panels with predictive power using molecular characteristics or clinical observations. Such analysis is often challenged by censored, small-sample-size, but high-dimensional genomic profiles or clinical data. Therefore, sophisticated models and algorithms are in pressing need. In this study, we propose a novel Area Under Curve (AUC) optimization method for multi-biomarker panel identification named Nearest Centroid Classifier for AUC optimization (NCC-AUC). Our method is motived by the connection between AUC score for classification accuracy evaluation and Harrell's concordance index in survival analysis. This connection allows us to convert the survival time regression problem to a binary classification problem. Then an optimization model is formulated to directly maximize AUC and meanwhile minimize the number of selected features to construct a predictor in the nearest centroid classifier framework. NCC-AUC shows its great performance by validating both in genomic data of breast cancer and clinical data of stage IB Non-Small-Cell Lung Cancer (NSCLC). For the genomic data, NCC-AUC outperforms Support Vector Machine (SVM) and Support Vector Machine-based Recursive Feature Elimination (SVM-RFE) in classification accuracy. It tends to select a multi-biomarker panel with low average redundancy and enriched biological meanings. Also NCC-AUC is more significant in separation of low and high risk cohorts than widely used Cox model (Cox proportional-hazards regression model) and L1-Cox model (L1 penalized in Cox model). These performance gains of NCC-AUC are quite robust across 5 subtypes of breast cancer. Further in an independent clinical data, NCC-AUC outperforms SVM and SVM-RFE in predictive accuracy and is consistently better than Cox model and L1-Cox model in grouping patients into high and low risk categories. In summary, NCC-AUC provides a rigorous optimization framework to systematically reveal multi-biomarker panel from genomic and clinical data. It can serve as a useful tool to identify prognostic biomarkers for survival analysis. NCC-AUC is available at http://doc.aporc.org/wiki/NCC-AUC. ywang@amss.ac.cn Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Tumor angiogenesis in advanced stage ovarian carcinoma.

PubMed

Hollingsworth, H C; Kohn, E C; Steinberg, S M; Rothenberg, M L; Merino, M J

1995-07-01

Tumor angiogenesis has been found to have prognostic significance in many tumor types for predicting an increased risk of metastasis. We assessed tumor vascularity in 43 cases of advanced stage (International Federation of Gynecologists and Obstetricians stages III and IV) ovarian cancer by using the highly specific endothelial cell marker CD34. Microvessel counts and stage were associated with disease-free survival and with overall survival by Kaplan-Meier analysis. The plots show that higher stage, higher average vessel count at 200x (200x avg) and 400x (400x avg) magnification and highest vessel count at 400x (400x high) magnification confer a worse prognosis for disease-free survival. Average vessel count of less than 16 (400x avg, P2 = 0.01) and less than 45 (200x avg, P2 = 0.026) suggested a better survival. Similarly, a high vessel count of less than 20 (400x high, P2 = 0.019) conferred a better survival as well. The plots suggest that higher stage, higher average vessel count at 200x and 400x, and highest vessel count at 200x and 400x show a trend to worse overall survival as well. With the Cox proportional hazards model, stage was the best predictor of overall survival, however, the average microvessel count at 400x was found to be the best predictor of disease-free survival. These results suggest that analysis of neovascularization in advanced stage ovarian cancer may be a useful prognostic factor.

The validity of EORTC GBM prognostic calculator on survival of GBM patients in the West of Scotland.

PubMed

Teo, Mario; Clark, Brian; MacKinnon, Mairi; Stewart, Willie; Paul, James; St George, Jerome

2014-06-01

It is now accepted that the addition of temozolomide to radiotherapy in the treatment of patients with newly diagnosed glioblastoma multiforme (GBM) significantly improves survival. In 2008, a subanalysis of the original study data was performed, and an online "GBM Calculator" was made available on the European Organisation for Research and Treatment of Cancer (EORTC) website allowing users to estimate patients' survival outcomes. We tested this calculator against actual local survival data to validate its use in our patients. Prospectively collected clinical data were analysed on 105 consecutive patients receiving concurrent chemoradiotherapy following surgical treatment of GBM between December 2004 and February 2009. Using the EORTC online calculator, survival outcomes were generated for these patients and compared with their actual survival. The median overall survival for the entire cohort was 15.3 months (range 2.8-50.5 months), with 1-year and 2-year overall survival of 65.7% and 19%, respectively. This is in comparison to the median overall predictive survival of 21.3 months, with 1-year and 2-year survival of 95% and 39.5%, respectively. Case by case analysis also showed that the survival was overestimated in nearly 80% of patients. Subgroup analyses showed similar overestimation of patients' survival, except calculator Model 3 which utilised MGMT status. Use of the EORTC GBM prognostic calculator would have overestimated the survival of the majority of our patients with GBM. Uncertainty exists as to the cause of overestimation in the cohort although local socioeconomic factors might play a role. The different calculator models yielded different outcomes and the "best" predictor of survival for the cohort under study utilised the tumour MGMT status. We would strongly encourage similar local studies of validity testing prior to employing the online prognostic calculator for other population groups.

Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting

PubMed Central

Dazard, Jean-Eudes; Ishwaran, Hemant; Mehlotra, Rajeev; Weinberg, Aaron; Zimmerman, Peter

2018-01-01

Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance. Using various linear and nonlinear time-to-events survival models in simulation studies, we first show the efficiency of our approach: true pairwise interaction-effects between variables are uncovered, while they may not be accompanied with their corresponding main-effects, and may not be detected by standard semi-parametric regression modeling and test statistics used in survival analysis. Moreover, using a RSF-based cross-validation scheme for generating prediction estimators, we show that informative predictors may be inferred. We applied our approach to an HIV cohort study recording key host gene polymorphisms and their association with HIV change of tropism or AIDS progression. Altogether, this shows how linear or nonlinear pairwise statistical interactions of variables may be efficiently detected with a predictive value in observational studies with time-to-event outcomes. PMID:29453930

Survival analysis of patients with esophageal cancer using parametric cure model.

PubMed

Rasouli, Mahboube; Ghadimi, Mahmood Reza; Mahmoodi, Mahmood; Mohammad, Kazem; Zeraati, Hojjat; Hosseini, Mostafa

2011-01-01

Esophageal cancer is a major cause of mortality and morbidity in the Caspian littoral north-eastern part of Iran. The aim of this study was to calculate cure function as well as to identify the factors that are related to this function among patients with esophageal cancer in this geographical area. Three hundred fifty nine cases of esophageal cancer registered in the Babol cancer registry during the period of 1990 to 1991 (inclusive) were followed up for 15 years up to 2006. Parametric cure model was used to calculate cure fraction and investigate the factors responsible for probability of cure among patients. Sample of subjects encompassed 62.7% men and 37.3% women, with mean ages of diagnosis was 60.0 and 55.3 years, respectively. The median survival time reached about 9 months and estimated survival rates in 1, 3, and 5 years following diagnosis were 23%, 15% and 13%, respectively. Results show the family history affects the cured fraction independently of its effect on early outcome and has a significant effect on the probability of uncured. The average cure fraction was estimated to be 0.10. As the proportionality assumption of Cox model does not meet in certain circumstances, a parametric cure model can provide a better fit and a better description of survival related outcome.

Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting.

PubMed

Dazard, Jean-Eudes; Ishwaran, Hemant; Mehlotra, Rajeev; Weinberg, Aaron; Zimmerman, Peter

2018-02-17

Unraveling interactions among variables such as genetic, clinical, demographic and environmental factors is essential to understand the development of common and complex diseases. To increase the power to detect such variables interactions associated with clinical time-to-events outcomes, we borrowed established concepts from random survival forest (RSF) models. We introduce a novel RSF-based pairwise interaction estimator and derive a randomization method with bootstrap confidence intervals for inferring interaction significance. Using various linear and nonlinear time-to-events survival models in simulation studies, we first show the efficiency of our approach: true pairwise interaction-effects between variables are uncovered, while they may not be accompanied with their corresponding main-effects, and may not be detected by standard semi-parametric regression modeling and test statistics used in survival analysis. Moreover, using a RSF-based cross-validation scheme for generating prediction estimators, we show that informative predictors may be inferred. We applied our approach to an HIV cohort study recording key host gene polymorphisms and their association with HIV change of tropism or AIDS progression. Altogether, this shows how linear or nonlinear pairwise statistical interactions of variables may be efficiently detected with a predictive value in observational studies with time-to-event outcomes.

Using Cox's proportional hazards model for prognostication in carcinoma of the upper aero-digestive tract.

PubMed

Wolfensberger, M

1992-01-01

One of the major short comings of the traditional TNM system is its limited potential for prognostication. With the development of multifactorial analysis techniques, such as Cox's proportional hazards model, it has become possible to simultaneously evaluate a large number of prognostic variables. Cox's model allows both the identification of prognostically relevant variables and the quantification of their prognostic influence. These characteristics make it a helpful tool for analysis as well as for prognostication. The goal of the present study was to develop a prognostic index for patients with carcinoma of the upper aero-digestive tract which makes use of all prognostically relevant variables. To accomplish this, the survival data of 800 patients with squamous cell carcinoma of the oral cavity, oropharynx, hypopharynx or larynx were analyzed. Sixty-one variables were screened for prognostic significance; of these only 19 variables (including age, tumor location, T, N and M stages, resection margins, capsular invasion of nodal metastases, and treatment modality) were found to significantly correlate with prognosis. With the help of Cox's equation, a prognostic index (PI) was computed for every combination of prognostic factors. To test the proposed model, the prognostic index was applied to 120 patients with carcinoma of the oral cavity or oropharynx. A comparison of predicted and observed survival showed good overall correlation, although actual survival tended to be better than predicted.

Infused autograft lymphocyte-to-monocyte ratio and survival in T-cell lymphoma post-autologous peripheral blood hematopoietic stem cell transplantation.

PubMed

Porrata, Luis F; Inwards, David J; Ansell, Stephen M; Micallef, Ivana N; Johnston, Patrick B; Hogan, William J; Markovic, Svetomir N

2015-07-03

The infused autograft lymphocyte-to-monocyte ratio (A-LMR) is a prognostic factor for survival in B-cell lymphomas post-autologous peripheral hematopoietic stem cell transplantation (APHSCT). Thus, we set out to investigate if the A-LMR is also a prognostic factor for survival post-APHSCT in T-cell lymphomas. From 1998 to 2014, 109 T-cell lymphoma patients that underwent APHSCT were studied. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to identify the optimal cut-off value of A-LMR for survival analysis and k-fold cross-validation model to validate the A-LMR cut-off value. Univariate and multivariate Cox proportional hazard models were used to assess the prognostic discriminator power of A-LMR. ROC and AUC identified an A-LMR ≥ 1 as the best cut-off value and was validated by k-fold cross-validation. Multivariate analysis showed A-LMR to be an independent prognostic factor for overall survival (OS) and progression-free survival (PFS). Patients with an A-LMR ≥ 1.0 experienced a superior OS and PFS versus patients with an A-LMR < 1.0 [median OS was not reached vs 17.9 months, 5-year OS rates of 87% (95% confidence interval (CI), 75-94%) vs 26% (95% CI, 13-42%), p < 0.0001; median PFS was not reached vs 11.9 months, 5-year PFS rates of 72% (95% CI, 58-83%) vs 16% (95% CI, 6-32%), p < 0.0001]. A-LMR is also a prognostic factor for clinical outcomes in patients with T-cell lymphomas undergoing APHSCT.

Genome Wide Analysis of Inbred Mouse Lines Identifies a Locus Containing Ppar-γ as Contributing to Enhanced Malaria Survival

PubMed Central

Henson, Kerstin; Luzader, Angelina; Lindstrom, Merle; Spooner, Muriel; Steffy, Brian M.; Suzuki, Oscar; Janse, Chris; Waters, Andrew P.; Zhou, Yingyao; Wiltshire, Tim; Winzeler, Elizabeth A.

2010-01-01

The genetic background of a patient determines in part if a person develops a mild form of malaria and recovers, or develops a severe form and dies. We have used a mouse model to detect genes involved in the resistance or susceptibility to Plasmodium berghei malaria infection. To this end we first characterized 32 different mouse strains infected with P. berghei and identified survival as the best trait to discriminate between the strains. We found a locus on chromosome 6 by linking the survival phenotypes of the mouse strains to their genetic variations using genome wide analyses such as haplotype associated mapping and the efficient mixed-model for association. This new locus involved in malaria resistance contains only two genes and confirms the importance of Ppar-γ in malaria infection. PMID:20531941

Meta-analysis of the prognostic value of abnormally expressed lncRNAs in hepatocellular carcinoma.

PubMed

Qu, Zhen; Yuan, Chun-Hui; Yin, Chang-Qing; Guan, Qing; Chen, Hao; Wang, Fu-Bing

2016-01-01

Many long noncoding RNAs (lncRNAs) have been reported to be abnormally expressed in hepatocellular carcinoma (HCC), and may have the potential to serve as prognostic markers. In this study, a meta-analysis was conducted to systematically evaluate the prognostic value of various lncRNAs in HCC. Eligible literatures were systematically collected from PubMed, Embase, Web of Science, and Cochrane Library (up to December 30, 2015). The main outcomes including overall survival, relapse-free survival, and disease-free survival were analyzed. Pooled hazard ratios (HRs) and 95% confidence intervals (95% CIs) were calculated using random- or fixed-effects models. A total of 2,991 patients with HCC in People's Republic of China from 27 studies were included in the analysis. The level of lncRNAs showed a significant association with clinical outcomes. Abnormally elevated lncRNA transcription level predicted poor overall survival (HR: 1.68, 95% CI: 1.20-2.34, P=0.002; I (2)=75.5%, P=0.000) and relapse-free survival (HR: 2.08, 95% CI: 1.65-2.61, P<0.001; I (2)=24.0%, P=0.215), while no association was observed with disease-free survival of HCC patients (HR: 1.39, 95% CI: 0.51-3.78, P=0.524; I (2)=81.3%, P=0.005). Subgroup analysis further showed that lncRNA transcription level was significantly associated with tumor size (relative risk [RR]: 1.19, 95% CI: 1.01-1.39, P=0.035), microvascular invasion (RR: 1.44, 95% CI: 1.10-1.89, P=0.009), and portal vein tumor thrombus (RR: 1.50, 95% CI: 1.03-2.20, P=0.036). Publication bias and sensitivity analysis further confirmed the stability of our results. Our present meta-analysis indicates that abnormal lncRNA transcription level may serve as a promising indicator for prognostic evaluation of patients with HCC in People's Republic of China.

Smart Extraction and Analysis System for Clinical Research.

PubMed

Afzal, Muhammad; Hussain, Maqbool; Khan, Wajahat Ali; Ali, Taqdir; Jamshed, Arif; Lee, Sungyoung

2017-05-01

With the increasing use of electronic health records (EHRs), there is a growing need to expand the utilization of EHR data to support clinical research. The key challenge in achieving this goal is the unavailability of smart systems and methods to overcome the issue of data preparation, structuring, and sharing for smooth clinical research. We developed a robust analysis system called the smart extraction and analysis system (SEAS) that consists of two subsystems: (1) the information extraction system (IES), for extracting information from clinical documents, and (2) the survival analysis system (SAS), for a descriptive and predictive analysis to compile the survival statistics and predict the future chance of survivability. The IES subsystem is based on a novel permutation-based pattern recognition method that extracts information from unstructured clinical documents. Similarly, the SAS subsystem is based on a classification and regression tree (CART)-based prediction model for survival analysis. SEAS is evaluated and validated on a real-world case study of head and neck cancer. The overall information extraction accuracy of the system for semistructured text is recorded at 99%, while that for unstructured text is 97%. Furthermore, the automated, unstructured information extraction has reduced the average time spent on manual data entry by 75%, without compromising the accuracy of the system. Moreover, around 88% of patients are found in a terminal or dead state for the highest clinical stage of disease (level IV). Similarly, there is an ∼36% probability of a patient being alive if at least one of the lifestyle risk factors was positive. We presented our work on the development of SEAS to replace costly and time-consuming manual methods with smart automatic extraction of information and survival prediction methods. SEAS has reduced the time and energy of human resources spent unnecessarily on manual tasks.

Predictors of survival in patients with recurrent ovarian cancer undergoing secondary cytoreductive surgery based on the pooled analysis of an international collaborative cohort

PubMed Central

Zang, R Y; Harter, P; Chi, D S; Sehouli, J; Jiang, R; Tropé, C G; Ayhan, A; Cormio, G; Xing, Y; Wollschlaeger, K M; Braicu, E I; Rabbitt, C A; Oksefjell, H; Tian, W J; Fotopoulou, C; Pfisterer, J; du Bois, A; Berek, J S

2011-01-01

Background: This study aims to identify prognostic factors and to develop a risk model predicting survival in patients undergoing secondary cytoreductive surgery (SCR) for recurrent epithelial ovarian cancer. Methods: Individual data of 1100 patients with recurrent ovarian cancer of a progression-free interval at least 6 months who underwent SCR were pooled analysed. A simplified scoring system for each independent prognostic factor was developed according to its coefficient. Internal validation was performed to assess the discrimination of the model. Results: Complete SCR was strongly associated with the improvement of survival, with a median survival of 57.7 months, when compared with 27.0 months in those with residual disease of 0.1–1 cm and 15.6 months in those with residual disease of >1 cm, respectively (P<0.0001). Progression-free interval (⩽23.1 months vs >23.1 months, hazard ratio (HR): 1.72; score: 2), ascites at recurrence (present vs absent, HR: 1.27; score: 1), extent of recurrence (multiple vs localised disease, HR: 1.38; score: 1) as well as residual disease after SCR (R1 vs R0, HR: 1.90, score: 2; R2 vs R0, HR: 3.0, score: 4) entered into the risk model. Conclusion: This prognostic model may provide evidence to predict survival benefit from secondary cytoreduction in patients with recurrent ovarian cancer. PMID:21878937

Research on golden-winged warblers: recent progress and current needs

Treesearch

Henry M. Streby; Ronald W. Rohrbaugh; David A. Buehler; David E. Andersen; Rachel Vallender; David I. King; Tom Will

2016-01-01

Considerable advances have been made in knowledge about Golden-winged Warblers (Vermivora chrysoptera) in the past decade. Recent employment of molecular analysis, stable-isotope analysis, telemetry-based monitoring of survival and behavior, and spatially explicit modeling techniques have added to, and revised, an already broad base of published...

A Comparative Analysis of Tri-Service Accreditation Policies and Practices. Volume 1. Of the Accreditation Requirements Study Report

DTIC Science & Technology

1994-02-01

32 A-2 OTHER SURVIAC (Survivability & Vulnerability Information Analysis Center) Kevin Crosthwaite Dennis Detamore 33 J-MASS (Joint Modeling and...Crosthwaite DATE: 27 May 1993 Mr. Dennis Detamore ORGANIZATION: Booz-Allen Hamilton (SURVIAC) ORGANIZATIONAL RESPONSIBILITY: SURVIAC has the

Eigentumors for prediction of treatment failure in patients with early-stage breast cancer using dynamic contrast-enhanced MRI: a feasibility study

NASA Astrophysics Data System (ADS)

Chan, H. M.; van der Velden, B. H. M.; E Loo, C.; Gilhuijs, K. G. A.

2017-08-01

We present a radiomics model to discriminate between patients at low risk and those at high risk of treatment failure at long-term follow-up based on eigentumors: principal components computed from volumes encompassing tumors in washin and washout images of pre-treatment dynamic contrast-enhanced (DCE-) MR images. Eigentumors were computed from the images of 563 patients from the MARGINS study. Subsequently, a least absolute shrinkage selection operator (LASSO) selected candidates from the components that contained 90% of the variance of the data. The model for prediction of survival after treatment (median follow-up time 86 months) was based on logistic regression. Receiver operating characteristic (ROC) analysis was applied and area-under-the-curve (AUC) values were computed as measures of training and cross-validated performances. The discriminating potential of the model was confirmed using Kaplan-Meier survival curves and log-rank tests. From the 322 principal components that explained 90% of the variance of the data, the LASSO selected 28 components. The ROC curves of the model yielded AUC values of 0.88, 0.77 and 0.73, for the training, leave-one-out cross-validated and bootstrapped performances, respectively. The bootstrapped Kaplan-Meier survival curves confirmed significant separation for all tumors (P  <  0.0001). Survival analysis on immunohistochemical subgroups shows significant separation for the estrogen-receptor subtype tumors (P  <  0.0001) and the triple-negative subtype tumors (P  =  0.0039), but not for tumors of the HER2 subtype (P  =  0.41). The results of this retrospective study show the potential of early-stage pre-treatment eigentumors for use in prediction of treatment failure of breast cancer.

A localizing circumferential compression device increases survival after coral snake envenomation to the torso of an animal model.

PubMed

Hack, Jason B; Deguzman, Jocelyn M; Brewer, Kori L; Meggs, William J; O'Rourke, Dorcas

2011-07-01

Pressure immobilization bandages have been shown to delay onset of systemic toxicity after Eastern coral snake (Micrurus fulvius) envenomation to the distal extremity. To assess the efficacy of a novel compression device in delaying onset of systemic toxicity after truncal envenomations with Eastern coral snake (Micrurus fulvius) venom in a porcine model. With University approval, nine juvenile pigs (11 kg to 22 kg) were sedated, anesthetized, and intubated but not paralyzed to ensure continuous spontaneous respirations in a university animal laboratory. Each animal was injected subcutaneously with 10 mg of M. fulvius venom in a pre-selected area of the trunk. After 1 min, six animals had the application of a novel, localizing circumferential compression (LoCC) device applied to the bite site (treatment group) and three animals had no treatment (control group). The device was composed of a rigid polymer clay form molded into a hollow fusiform shape with an internal dimension of 8 × 5 × 3 cm and an elastic belt wrapped around the animal securing the device in place. Vital signs were recorded at 30-min intervals. End points included a respiratory rate below 3 breaths/min, oxygen saturation < 80%, or survival to 8 h. Survival to 8 h was analyzed using Fisher's exact test, with p < 0.05 indicating significance. Survival analysis was performed using the Mantel-Cox test to assess time to death with outcomes represented in a Kaplan-Meier Cumulative survival plot. Five of the six pigs in the treatment group survived 8 h (293-480 min). None of the control pigs survived to 8 h (Fisher's exact p = 0.04), with mean time of respiratory failure 322 min (272-382 min). Survival analysis showed a significant delay in time to event in the treatment group compared to the control group (p = 0.04). The LoCC device used in this study delayed the onset of systemic toxicity and significantly increased survival time after artificial truncal envenomation by Eastern coral snake venom. Copyright © 2011 Elsevier Inc. All rights reserved.

Magnetic resonance spectroscopy metabolite profiles predict survival in paediatric brain tumours.

PubMed

Wilson, Martin; Cummins, Carole L; Macpherson, Lesley; Sun, Yu; Natarajan, Kal; Grundy, Richard G; Arvanitis, Theodoros N; Kauppinen, Risto A; Peet, Andrew C

2013-01-01

Brain tumours cause the highest mortality and morbidity rate of all childhood tumour groups and new methods are required to improve clinical management. (1)H magnetic resonance spectroscopy (MRS) allows non-invasive concentration measurements of small molecules present in tumour tissue, providing clinically useful imaging biomarkers. The primary aim of this study was to investigate whether MRS detectable molecules can predict the survival of paediatric brain tumour patients. Short echo time (30ms) single voxel (1)H MRS was performed on children attending Birmingham Children's Hospital with a suspected brain tumour and 115 patients were included in the survival analysis. Patients were followed-up for a median period of 35 months and Cox-Regression was used to establish the prognostic value of individual MRS detectable molecules. A multivariate model of survival was also investigated to improve prognostic power. Lipids and scyllo-inositol predicted poor survival whilst glutamine and N-acetyl aspartate predicted improved survival (p<0.05). A multivariate model of survival based on three MRS biomarkers predicted survival with a similar accuracy to histologic grading (p<5e-5). A negative correlation between lipids and glutamine was found, suggesting a functional link between these molecules. MRS detectable biomolecules have been identified that predict survival of paediatric brain tumour patients across a range of tumour types. The evaluation of these biomarkers in large prospective studies of specific tumour types should be undertaken. The correlation between lipids and glutamine provides new insight into paediatric brain tumour metabolism that may present novel targets for therapy. Copyright © 2012 Elsevier Ltd. All rights reserved.

Demographic consequences of migratory stopover: linking red knot survival to horseshoe crab spawning abundance

USGS Publications Warehouse

McGowan, Conor P.; Hines, James E.; Nichols, James D.; Lyons, James E.; Smith, David; Kalasz, Kevin S.; Niles, Lawrence J.; Dey, Amanda D.; Clark, Nigel A.; Atkinson, Philip W.; Minton, Clive D.T.; Kendall, William

2011-01-01

Understanding how events during one period of the annual cycle carry over to affect survival and other fitness components in other periods is essential to understanding migratory bird demography and conservation needs. Previous research has suggested that western Atlantic red knot (Calidris canutus rufa) populations are greatly affected by horseshoe crab (Limulus polyphemus) egg availability at Delaware Bay stopover sites during their spring northward migration. We present a mass-based multistate, capturerecapture/resighting model linking (1) red knot stopover mass gain to horseshoe crab spawning abundance and (2) subsequent apparent annual survival to mass state at the time of departure from the Delaware Bay stopover area. The model and analysis use capture-recapture/resighting data with over 16,000 individual captures and 13,000 resightings collected in Delaware Bay over a 12 year period from 1997–2008, and the results are used to evaluate the central management hypothesis that red knot populations can be influenced by horseshoe crab harvest regulations as part of a larger adaptive management effort. Model selection statistics showed support for a positive relationship between horseshoe crab spawning abundance during the stopover and the probability of red knots gaining mass (parameter coefficient from the top model b = 1.71, SE = 0.46). Our analyses also supported the link between red knot mass and apparent annual survival, although average estimates for the two mass classes differed only slightly. The addition of arctic snow depth as a covariate influencing apparent survival improved the fit of the data to the models (parameter coefficient from the top model b = 0.50, SE = 0.08). Our results indicate that managing horseshoe crab resources in the Delaware Bay has the potential to improve red knot population status.

Predicting 6- and 12-Month Risk of Mortality in Patients With Platinum-Resistant Advanced-Stage Ovarian Cancer: Prognostic Model to Guide Palliative Care Referrals.

PubMed

Foote, Jonathan; Lopez-Acevedo, Micael; Samsa, Gregory; Lee, Paula S; Kamal, Arif H; Alvarez Secord, Angeles; Havrilesky, Laura J

2018-02-01

Predictive models are increasingly being used in clinical practice. The aim of the study was to develop a predictive model to identify patients with platinum-resistant ovarian cancer with a prognosis of less than 6 to 12 months who may benefit from immediate referral to hospice care. A retrospective chart review identified patients with platinum-resistant epithelial ovarian cancer who were treated at our institution between 2000 and 2011. A predictive model for survival was constructed based on the time from development of platinum resistance to death. Multivariate logistic regression modeling was used to identify significant survival predictors and to develop a predictive model. The following variables were included: time from diagnosis to platinum resistance, initial stage, debulking status, number of relapses, comorbidity score, albumin, hemoglobin, CA-125 levels, liver/lung metastasis, and the presence of a significant clinical event (SCE). An SCE was defined as a malignant bowel obstruction, pleural effusion, or ascites occurring on or before the diagnosis of platinum resistance. One hundred sixty-four patients met inclusion criteria. In the regression analysis, only an SCE and the presence of liver or lung metastasis were associated with poorer short-term survival (P < 0.001). Nine percent of patients with an SCE or liver or lung metastasis survived 6 months or greater and 0% survived 12 months or greater, compared with 85% and 67% of patients without an SCE or liver or lung metastasis, respectively. Patients with platinum-resistant ovarian cancer who have experienced an SCE or liver or lung metastasis have a high risk of death within 6 months and should be considered for immediate referral to hospice care.

Applying Additive Hazards Models for Analyzing Survival in Patients with Colorectal Cancer in Fars Province, Southern Iran

PubMed

Madadizadeh, Farzan; Ghanbarnejad, Amin; Ghavami, Vahid; Zare Bandamiri, Mohammad; Mohammadianpanah, Mohammad

2017-04-01

Introduction: Colorectal cancer (CRC) is a commonly fatal cancer that ranks as third worldwide and third and the fifth in Iranian women and men, respectively. There are several methods for analyzing time to event data. Additive hazards regression models take priority over the popular Cox proportional hazards model if the absolute hazard (risk) change instead of hazard ratio is of primary concern, or a proportionality assumption is not made. Methods: This study used data gathered from medical records of 561 colorectal cancer patients who were admitted to Namazi Hospital, Shiraz, Iran, during 2005 to 2010 and followed until December 2015. The nonparametric Aalen’s additive hazards model, semiparametric Lin and Ying’s additive hazards model and Cox proportional hazards model were applied for data analysis. The proportionality assumption for the Cox model was evaluated with a test based on the Schoenfeld residuals and for test goodness of fit in additive models, Cox-Snell residual plots were used. Analyses were performed with SAS 9.2 and R3.2 software. Results: The median follow-up time was 49 months. The five-year survival rate and the mean survival time after cancer diagnosis were 59.6% and 68.1±1.4 months, respectively. Multivariate analyses using Lin and Ying’s additive model and the Cox proportional model indicated that the age of diagnosis, site of tumor, stage, and proportion of positive lymph nodes, lymphovascular invasion and type of treatment were factors affecting survival of the CRC patients. Conclusion: Additive models are suitable alternatives to the Cox proportionality model if there is interest in evaluation of absolute hazard change, or no proportionality assumption is made. Creative Commons Attribution License

[Neutrophil to lymphocyte ratio in peripheral blood: a novel independent prognostic factor in patients with head and neck squamous cell carcinoma].

PubMed

Wu, F; Wu, L L; Zhu, L X

2017-01-23

Objective: To investigate whether neutrophil to lymphocyte ratio (NLR) in peripheral blood can be an independent prognostic factor in patients with head and neck squamous cell carcinoma (HNSCC). Methods: Clinical data of 97 HNSCC patients who received surgical treatment in our department between January 2008 and January 2012 were analyzed retrospectively. The 97 patients were divided into low NLR group (NLR≤5, n =69) and high NLR group (NLR>5, n =28) according to the NLR in preoperative peripheral blood. The relationships of NLR and clinicopathological features were analyzed. Kaplan-Meier method was used for univariate survival analysis and Cox proportional hazard model for multivariate survival analysis. Results: The clinical stages were significantly different between high NLR group and low NLR group ( P <0.05), however, the age, gender, location, lymph node metastasis, smoking and alcohol of the two groups showed no significant differences ( P > 0.05 of all). Univariate survival analysis showed that smoking, lymph node metastasis, clinical stage and NLR value were risk factors for 3-year overall survival (OS) rate and relapse-free survival (RFS) rate of HNSCC patients ( P <0.05). The OS rate of high NLR and low NLR groups was 42.9% and 91.3%, and the RFS rate was 44.2% and 80.1%, respectively, with a statistically significant difference ( P <0.05 for both). Cox multivariate survival analysis showed that clinical stage and NLR were independent factors for prognostic evaluation of HNSCC patients ( P <0.05 for both). Conclusions: NLR level is significantly associated with clinical stage of HNSCC. High NLR is an independent prognostic rick factor and plays an important role in prognostic evaluation of HNSCC patients.

Survival after Aortic Valve Replacement with Bovine or Porcine Valve Prostheses: A Systematic Review and Meta-Analysis.

PubMed

Glaser, Natalie; Jackson, Veronica; Franco-Cereceda, Anders; Sartipy, Ulrik

2018-05-17

 Bovine and porcine bioprostheses are commonly used for surgical aortic valve replacement. It is unknown if the long-term survival differs between the two valve types.We performed a systematic review and meta-analysis to compare survival in patients who underwent aortic valve replacement and received a bovine or a porcine prosthesis.  We performed a systematic search of Medline, Embase, Web of Science, and the Cochrane Library. Cohort studies that compared survival between patients who underwent aortic valve replacement and received either a bovine or a porcine bioprosthesis and that reported overall long-term survival with hazard ratio (HR) and 95% confidence interval (CI) were included. Two authors independently reviewed articles considered for inclusion, extracted the information from each study, and performed the quality assessment. We performed a meta-analysis using a random effects model to calculate the pooled HR (95% CI) for all-cause mortality. We did sensitivity analyses to assess the robustness of our findings.  Seven studies published between 2010 and 2015 were included, and the combined study population was 49,190 patients. Of these, 32,235 (66%) received a bovine, and 16,955 (34%) received a porcine bioprosthesis. There was no significant difference in all-cause mortality between patients who received a bovine compared with a porcine bioprosthesis (pooled HR 1.00, 95% CI: 0.92-1.09). Heterogeneity between studies was moderate (55.8%, p  = 0.04).  This systematic review and meta-analysis suggest no difference in survival between patients who received a bovine versus a porcine bioprosthesis after aortic valve replacement. Our study provides valuable evidence for the continuing use of both bovine and porcine bioprosthetic valves for surgical aortic valve replacement. Georg Thieme Verlag KG Stuttgart · New York.

Steroid use in acute liver failure.

PubMed

Karkhanis, Jamuna; Verna, Elizabeth C; Chang, Matthew S; Stravitz, R Todd; Schilsky, Michael; Lee, William M; Brown, Robert S

2014-02-01

Drug-induced and indeterminate acute liver failure (ALF) might be due to an autoimmune-like hepatitis that is responsive to corticosteroid therapy. The aim of this study was to evaluate whether corticosteroids improve survival in fulminant autoimmune hepatitis, drug-induced, or indeterminate ALF, and whether this benefit varies according to the severity of illness. We conducted a retrospective analysis of autoimmune, indeterminate, and drug-induced ALF patients in the Acute Liver Failure Study Group from 1998-2007. The primary endpoints were overall and spontaneous survival (SS, survival without transplant). In all, 361 ALF patients were studied, 66 with autoimmune (25 steroids, 41 no steroids), 164 with indeterminate (21 steroids, 143 no steroids), and 131 with drug-induced (16 steroids, 115 no steroids) ALF. Steroid use was not associated with improved overall survival (61% versus 66%, P = 0.41), nor with improved survival in any diagnosis category. Steroid use was associated with diminished survival in certain subgroups of patients, including those with the highest quartile of the Model for Endstage Liver Disease (MELD) (>40, survival 30% versus 57%, P = 0.03). In multivariate analysis controlling for steroid use and diagnosis, age (odds ratio [OR] 1.37 per decade), coma grade (OR 2.02 grade 2, 2.65 grade 3, 5.29 grade 4), MELD (OR 1.07), and pH < 7.4 (OR 3.09) were significantly associated with mortality. Although steroid use was associated with a marginal benefit in SS overall (35% versus 23%, P = 0.047), this benefit did not persistent in multivariate analysis; mechanical ventilation (OR 0.24), MELD (OR 0.93), and alanine aminotransferase (1.02) were the only significant predictors of SS. Corticosteroids did not improve overall survival or SS in drug-induced, indeterminate, or autoimmune ALF and were associated with lower survival in patients with the highest MELD scores. © 2013 by the American Association for the Study of Liver Diseases.

Trends in Incidence and Factors Affecting Survival of Patients With Cholangiocarcinoma in the United States.

PubMed

Mukkamalla, Shiva Kumar R; Naseri, Hussain M; Kim, Byung M; Katz, Steven C; Armenio, Vincent A

2018-04-01

Background: Cholangiocarcinoma (CCA) includes cancers arising from the intrahepatic and extrahepatic bile ducts. The etiology and pathogenesis of CCA remain poorly understood. This is the first study investigating both incidence patterns of CCA from 1973 through 2012 and demographic, clinical, and treatment variables affecting survival of patients with CCA. Patients and Methods: Using the SEER database, age-adjusted incidence rates were evaluated from 1973-2012 using SEER*Stat software. A retrospective cohort of 26,994 patients diagnosed with CCA from 1973-2008 was identified for survival analysis. Cox proportional hazards models were used to perform multivariate survival analysis. Results: Overall incidence of CCA increased by 65% from 1973-2012. Extrahepatic CCA (ECC) remained more common than intrahepatic CCA (ICC), whereas the incidence rates for ICC increased by 350% compared with a 20% increase seen with ECC. Men belonging to non-African American and non-Caucasian ethnicities had the highest incidence rates of CCA. This trend persisted throughout the study period, although African Americans and Caucasians saw 50% and 59% increases in incidence rates, respectively, compared with a 9% increase among other races. Median overall survival (OS) was 8 months in patients with ECC compared with 4 months in those with ICC. Our survival analysis found Hispanic women to have the best 5-year survival outcome ( P <.0001). OS diminished with age ( P <.0001), and ECC had better survival outcomes compared with ICC ( P <.0001). Patients who were married, were nonsmokers, belonged to a higher income class, and underwent surgery had better survival outcomes compared with others ( P <.0001). Conclusions: This is the most up-to-date study of CCA from the SEER registry that shows temporal patterns of increasing incidence of CCA across different races, sexes, and ethnicities. We identified age, sex, race, marital status, income, smoking status, anatomic location of CCA, tumor grade, tumor stage, radiation, and surgery as independent prognostic factors for OS in patients with CCA. Copyright © 2018 by the National Comprehensive Cancer Network.

Time from HIV seroconversion to death: a collaborative analysis of eight studies in six low and middle-income countries before highly active antiretroviral therapy

PubMed Central

Todd, Jim; Glynn, Judith R.; Marston, Milly; Lutalo, Tom; Biraro, Sam; Mwita, Wambura; Suriyanon, Vinai; Rangsin, Ram; Nelson, Kenrad E.; Sonnenberg, Pam; Fitzgerald, Dan; Karita, Etienne; Żaba, Basia

2018-01-01

Objectives To estimate survival patterns after HIV infection in adults in low and middle-income countries. Design An analysis of pooled data from eight different studies in six countries. Methods HIV seroconverters were included from eight studies (three population-based, two occupational, and three clinic cohorts) if they were at least 15 years of age, and had no more than 4 years between the last HIV-negative and subsequent HIV-positive test. Four strata were defined: East African cohorts; South African miners cohort; Thai cohorts; Haitian clinic cohort. Kaplan–Meier functions were used to estimate survival patterns, and Weibull distributions were used to model and extend survival estimates. Analyses examined the effect of site, age, and sex on survival. Results From 3823 eligible seroconverters, 1079 deaths were observed in 19 671 person-years of follow-up. Survival times varied by age and by study site. Adjusting to age 25–29 years at seroconversion, the median survival was longer in South African miners: 11.6 years [95% confidence interval (CI) 9.8–13.7] and East African cohorts: 11.1 years (95% CI 8.7–14.2) than in Haiti: 8.3 years (95% CI 3.2–21.4) and Thailand: 7.5 years (95% CI 5.4–10.4). Survival was similar for men and women, after adjustment for age at seroconversion and site. Conclusion Without antiretroviral therapy, overall survival after HIV infection in African cohorts was similar to survival in high-income countries, with a similar pattern of faster progression at older ages at seroconversion. Survival appears to be significantly worse in Thailand where other, unmeasured factors may affect progression. PMID:18032940

Survival analysis of cancer risk reduction strategies for BRCA1/2 mutation carriers.

PubMed

Kurian, Allison W; Sigal, Bronislava M; Plevritis, Sylvia K

2010-01-10

Women with BRCA1/2 mutations inherit high risks of breast and ovarian cancer; options to reduce cancer mortality include prophylactic surgery or breast screening, but their efficacy has never been empirically compared. We used decision analysis to simulate risk-reducing strategies in BRCA1/2 mutation carriers and to compare resulting survival probability and causes of death. We developed a Monte Carlo model of breast screening with annual mammography plus magnetic resonance imaging (MRI) from ages 25 to 69 years, prophylactic mastectomy (PM) at various ages, and/or prophylactic oophorectomy (PO) at ages 40 or 50 years in 25-year-old BRCA1/2 mutation carriers. With no intervention, survival probability by age 70 is 53% for BRCA1 and 71% for BRCA2 mutation carriers. The most effective single intervention for BRCA1 mutation carriers is PO at age 40, yielding a 15% absolute survival gain; for BRCA2 mutation carriers, the most effective single intervention is PM, yielding a 7% survival gain if performed at age 40 years. The combination of PM and PO at age 40 improves survival more than any single intervention, yielding 24% survival gain for BRCA1 and 11% for BRCA2 mutation carriers. PM at age 25 instead of age 40 offers minimal incremental benefit (1% to 2%); substituting screening for PM yields a similarly minimal decrement in survival (2% to 3%). Although PM at age 25 plus PO at age 40 years maximizes survival probability, substituting mammography plus MRI screening for PM seems to offer comparable survival. These results may guide women with BRCA1/2 mutations in their choices between prophylactic surgery and breast screening.

Polymorphisms of LIG4, BTBD2, HMGA2, and RTEL1 Genes Involved in the Double-Strand Break Repair Pathway Predict Glioblastoma Survival

PubMed Central

Liu, Yanhong; Shete, Sanjay; Etzel, Carol J.; Scheurer, Michael; Alexiou, George; Armstrong, Georgina; Tsavachidis, Spyros; Liang, Fu-Wen; Gilbert, Mark; Aldape, Ken; Armstrong, Terri; Houlston, Richard; Hosking, Fay; Robertson, Lindsay; Xiao, Yuanyuan; Wiencke, John; Wrensch, Margaret; Andersson, Ulrika; Melin, Beatrice S.; Bondy, Melissa

2010-01-01

Purpose Glioblastoma (GBM) is the most common and aggressive type of glioma and has the poorest survival. However, a small percentage of patients with GBM survive well beyond the established median. Therefore, identifying the genetic variants that influence this small number of unusually long-term survivors may provide important insight into tumor biology and treatment. Patients and Methods Among 590 patients with primary GBM, we evaluated associations of survival with the 100 top-ranking glioma susceptibility single nucleotide polymorphisms from our previous genome-wide association study using Cox regression models. We also compared differences in genetic variation between short-term survivors (STS; ≤ 12 months) and long-term survivors (LTS; ≥ 36 months), and explored classification and regression tree analysis for survival data. We tested results using two independent series totaling 543 GBMs. Results We identified LIG4 rs7325927 and BTBD2 rs11670188 as predictors of STS in GBM and CCDC26 rs10464870 and rs891835, HMGA2 rs1563834, and RTEL1 rs2297440 as predictors of LTS. Further survival tree analysis revealed that patients ≥ 50 years old with LIG4 rs7325927 (V) had the worst survival (median survival time, 1.2 years) and exhibited the highest risk of death (hazard ratio, 17.53; 95% CI, 4.27 to 71.97) compared with younger patients with combined RTEL1 rs2297440 (V) and HMGA2 rs1563834 (V) genotypes (median survival time, 7.8 years). Conclusion Polymorphisms in the LIG4, BTBD2, HMGA2, and RTEL1 genes, which are involved in the double-strand break repair pathway, are associated with GBM survival. PMID:20368557

Polymorphisms of LIG4, BTBD2, HMGA2, and RTEL1 genes involved in the double-strand break repair pathway predict glioblastoma survival.

PubMed

Liu, Yanhong; Shete, Sanjay; Etzel, Carol J; Scheurer, Michael; Alexiou, George; Armstrong, Georgina; Tsavachidis, Spyros; Liang, Fu-Wen; Gilbert, Mark; Aldape, Ken; Armstrong, Terri; Houlston, Richard; Hosking, Fay; Robertson, Lindsay; Xiao, Yuanyuan; Wiencke, John; Wrensch, Margaret; Andersson, Ulrika; Melin, Beatrice S; Bondy, Melissa

2010-05-10

Glioblastoma (GBM) is the most common and aggressive type of glioma and has the poorest survival. However, a small percentage of patients with GBM survive well beyond the established median. Therefore, identifying the genetic variants that influence this small number of unusually long-term survivors may provide important insight into tumor biology and treatment. Among 590 patients with primary GBM, we evaluated associations of survival with the 100 top-ranking glioma susceptibility single nucleotide polymorphisms from our previous genome-wide association study using Cox regression models. We also compared differences in genetic variation between short-term survivors (STS; or= 36 months), and explored classification and regression tree analysis for survival data. We tested results using two independent series totaling 543 GBMs. We identified LIG4 rs7325927 and BTBD2 rs11670188 as predictors of STS in GBM and CCDC26 rs10464870 and rs891835, HMGA2 rs1563834, and RTEL1 rs2297440 as predictors of LTS. Further survival tree analysis revealed that patients >or= 50 years old with LIG4 rs7325927 (V) had the worst survival (median survival time, 1.2 years) and exhibited the highest risk of death (hazard ratio, 17.53; 95% CI, 4.27 to 71.97) compared with younger patients with combined RTEL1 rs2297440 (V) and HMGA2 rs1563834 (V) genotypes (median survival time, 7.8 years). Polymorphisms in the LIG4, BTBD2, HMGA2, and RTEL1 genes, which are involved in the double-strand break repair pathway, are associated with GBM survival.

Radical Nephrectomy for Primary Retroperitoneal Liposarcoma Near the Kidney has a Beneficial Effect on Disease-Free Survival.

PubMed

Rhu, Jinsoo; Cho, Chan Woo; Lee, Kyo Won; Park, Hyojun; Park, Jae Berm; Choi, Yoon-La; Kim, Sung Joo

2018-01-01

The purpose of this study is to analyze the clinical impact of radical nephrectomy on retroperitoneal liposarcoma near the kidney. Data of patients who underwent surgery for unilateral primary retroperitoneal liposarcoma near the kidney were retrospectively collected. Patients were divided into four groups according to whether they underwent nephrectomy and combined resection of other organs. Kaplan-Meier survival analysis was used to estimate disease-free survival and overall survival. Multivariable Cox analysis was used to analyze factors related to disease-free survival and overall survival. Nephrectomy (HR = 0.260, CI = 0.078-0.873, p = 0.029) had a beneficial effect on disease-free survival, while interaction model of nephrectomy*other organ resection (HR = 4.655, CI = 1.767-12.263, p = 0.002) showed poor disease-free survival. Other organ resection was not related to disease-free survival (HR = 1.543, CI = 0.146-16.251, p = 0.718). Operation method (p = 0.007) and FNCLCC grade (p < 0.001; G2, HR = 1.833, CI = 0.684-4.915, p = 0.228; G3, HR = 9.190, CI = 3.351-25.199, p < 0.001) were significant factors for disease-free survival. While combined organ resection without nephrectomy group (HR = 1.604, CI = 0.167-15.370, p = 0.682) and radical nephrectomy with combined organ resection group (HR = 1.309, CI = 0.448-3.825, p = 0.622) did not show significant difference in disease-free survival from the mass excision only group, radical nephrectomy without combined organ resection group (HR = 0.279, CI = 0.078-0.991, p = 0.048) showed superior disease-free survival. Radical nephrectomy of unilateral primary retroperitoneal liposarcoma near the kidney has a beneficial effect on disease-free survival.

Population dynamics of mallards breeding in eastern Washington

USGS Publications Warehouse

Dugger, Bruce D.; Coluccy, John M.; Dugger, Katie M.; Fox, Trevor T.; Kraege, Donald K.; Petrie, Mark J.

2016-01-01

Variation in regional population trends for mallards breeding in the western United States indicates that additional research into factors that influence demographics could contribute to management and understanding the population demographics of mallards across North America. We estimated breeding incidence and adult female, nest, and brood survival in eastern Washington in 2006 and 2007 by monitoring female mallards with radio telemetry and tested how those parameters were influenced by study year (2006 vs. 2007), landscape type (agricultural vs. natural), and age (second year [SY] vs. after second year [ASY]). We also investigated the effects of female body condition and capture date on breeding incidence, and nest initiation date and hatch date on nest and brood survival, respectively. We included population parameters in a stage-based demographic model and conducted a perturbation analysis to identify which vital rates were most influential on population growth rate (λ). Adult female survival was best modeled with a constant weekly survival rate (0.994, SE = 0.003). Breeding incidence differed between years and was higher for birds in better body condition. Nest survival was higher for ASY females (0.276, SE = 0.118) than SY females (0.066, SE = 0.052), and higher on publicly managed lands (0.383, SE = 0.212) than agricultural (0.114, SE = 0.058) landscapes. Brood survival was best modeled with a constant rate for the 7-week monitoring period (0.50, SE = 0.155). The single variable having the greatest influence on λ was non-breeding season survival, but the combination of parameters from the breeding grounds explained a greater percent of the variance in λ. Mallard population growth rate was most sensitive to changes in non-breeding survival, nest success, brood survival, and breeding incidence. Future management decisions should focus on activities that improve these vital rates if managers want to increase the production of mallards in eastern Washington.

A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome

PubMed Central

Desrosiers, Christian; Hassan, Lama; Tanougast, Camel

2016-01-01

Objective: Predicting the survival outcome of patients with glioblastoma multiforme (GBM) is of key importance to clinicians for selecting the optimal course of treatment. The goal of this study was to evaluate the usefulness of geometric shape features, extracted from MR images, as a potential non-invasive way to characterize GBM tumours and predict the overall survival times of patients with GBM. Methods: The data of 40 patients with GBM were obtained from the Cancer Genome Atlas and Cancer Imaging Archive. The T1 weighted post-contrast and fluid-attenuated inversion-recovery volumes of patients were co-registered and segmented into delineate regions corresponding to three GBM phenotypes: necrosis, active tumour and oedema/invasion. A set of two-dimensional shape features were then extracted slicewise from each phenotype region and combined over slices to describe the three-dimensional shape of these phenotypes. Thereafter, a Kruskal–Wallis test was employed to identify shape features with significantly different distributions across phenotypes. Moreover, a Kaplan–Meier analysis was performed to find features strongly associated with GBM survival. Finally, a multivariate analysis based on the random forest model was used for predicting the survival group of patients with GBM. Results: Our analysis using the Kruskal–Wallis test showed that all but one shape feature had statistically significant differences across phenotypes, with p-value < 0.05, following Holm–Bonferroni correction, justifying the analysis of GBM tumour shapes on a per-phenotype basis. Furthermore, the survival analysis based on the Kaplan–Meier estimator identified three features derived from necrotic regions (i.e. Eccentricity, Extent and Solidity) that were significantly correlated with overall survival (corrected p-value < 0.05; hazard ratios between 1.68 and 1.87). In the multivariate analysis, features from necrotic regions gave the highest accuracy in predicting the survival group of patients, with a mean area under the receiver-operating characteristic curve (AUC) of 63.85%. Combining the features of all three phenotypes increased the mean AUC to 66.99%, suggesting that shape features from different phenotypes can be used in a synergic manner to predict GBM survival. Conclusion: Results show that shape features, in particular those extracted from necrotic regions, can be used effectively to characterize GBM tumours and predict the overall survival of patients with GBM. Advances in knowledge: Simple volumetric features have been largely used to characterize the different phenotypes of a GBM tumour (i.e. active tumour, oedema and necrosis). This study extends previous work by considering a wide range of shape features, extracted in different phenotypes, for the prediction of survival in patients with GBM. PMID:27781499

Joint modelling of repeated measurement and time-to-event data: an introductory tutorial.

PubMed

Asar, Özgür; Ritchie, James; Kalra, Philip A; Diggle, Peter J

2015-02-01

The term 'joint modelling' is used in the statistical literature to refer to methods for simultaneously analysing longitudinal measurement outcomes, also called repeated measurement data, and time-to-event outcomes, also called survival data. A typical example from nephrology is a study in which the data from each participant consist of repeated estimated glomerular filtration rate (eGFR) measurements and time to initiation of renal replacement therapy (RRT). Joint models typically combine linear mixed effects models for repeated measurements and Cox models for censored survival outcomes. Our aim in this paper is to present an introductory tutorial on joint modelling methods, with a case study in nephrology. We describe the development of the joint modelling framework and compare the results with those obtained by the more widely used approaches of conducting separate analyses of the repeated measurements and survival times based on a linear mixed effects model and a Cox model, respectively. Our case study concerns a data set from the Chronic Renal Insufficiency Standards Implementation Study (CRISIS). We also provide details of our open-source software implementation to allow others to replicate and/or modify our analysis. The results for the conventional linear mixed effects model and the longitudinal component of the joint models were found to be similar. However, there were considerable differences between the results for the Cox model with time-varying covariate and the time-to-event component of the joint model. For example, the relationship between kidney function as measured by eGFR and the hazard for initiation of RRT was significantly underestimated by the Cox model that treats eGFR as a time-varying covariate, because the Cox model does not take measurement error in eGFR into account. Joint models should be preferred for simultaneous analyses of repeated measurement and survival data, especially when the former is measured with error and the association between the underlying error-free measurement process and the hazard for survival is of scientific interest. © The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.

Valproic acid treatment attenuates caspase-3 activation and improves survival after lethal burn injury in a rodent model.

PubMed

Luo, Hong-Min; Hu, Sen; Bai, Hui-Ying; Wang, Hai-Bin; Du, Ming-Hua; Lin, Zhi-Long; Ma, Li; Wang, Huan; Lv, Yi; Sheng, Zhi-Yong

2014-01-01

Burn injury may result in multiple organ dysfunction partially because of apoptotic cell death. The authors have previously shown that valproic acid (VPA) improves survival in a dog burn model. The aim of this study is to examine whether a VPA improves survival in a rodent burn model and whether this was because of inhibition of cell apoptosis. Rats were subjected to third-degree 55% TBSA burns and randomized to treatment with a VPA (300 mg/kg) or normal saline. One group of animals was monitored for 12 hours for survival analysis; another group was killed at 6 hours after injury, and brains, hearts, and blood samples were harvested for examination. Plasma creatine kinase (CK)-MB activities and neuron-specific enolase (NSE) levels were measured to evaluate the cardiac and brain damages. The effects of a VPA on acetylation of histone H3 and caspase-3 activation were also evaluated. Major burn injury resulted in a significant decrease in the acetylation of histone H3, and there was an increase in plasma CK-MB activities, NSE concentrations, and tissue levels of activated caspase-3. A VPA treatment significantly increased the acetylation of histone H3 and survival of the animals after major burn injury. In addition, a VPA treatment significantly attenuated the plasma CK-MB activities, an NSE concentrations, and inhibited caspase-3 activation after major burn injury. These results indicate that a VPA can attenuate cardiac and brain injury, and can improve survival in a rodent model of lethal burn injury. These protective effects may be mediated in part through the inhibition of caspase-3 activation.

Prognostic factors in patients with advanced cancer: use of the patient-generated subjective global assessment in survival prediction.

PubMed

Martin, Lisa; Watanabe, Sharon; Fainsinger, Robin; Lau, Francis; Ghosh, Sunita; Quan, Hue; Atkins, Marlis; Fassbender, Konrad; Downing, G Michael; Baracos, Vickie

2010-10-01

To determine whether elements of a standard nutritional screening assessment are independently prognostic of survival in patients with advanced cancer. A prospective nested cohort of patients with metastatic cancer were accrued from different units of a Regional Palliative Care Program. Patients completed a nutritional screen on admission. Data included age, sex, cancer site, height, weight history, dietary intake, 13 nutrition impact symptoms, and patient- and physician-reported performance status (PS). Univariate and multivariate survival analyses were conducted. Concordance statistics (c-statistics) were used to test the predictive accuracy of models based on training and validation sets; a c-statistic of 0.5 indicates the model predicts the outcome as well as chance; perfect prediction has a c-statistic of 1.0. A training set of patients in palliative home care (n = 1,164) was used to identify prognostic variables. Primary disease site, PS, short-term weight change (either gain or loss), dietary intake, and dysphagia predicted survival in multivariate analysis (P < .05). A model including only patients separated by disease site and PS with high c-statistics between predicted and observed responses for survival in the training set (0.90) and validation set (0.88; n = 603). The addition of weight change, dietary intake, and dysphagia did not further improve the c-statistic of the model. The c-statistic was also not altered by substituting physician-rated palliative PS for patient-reported PS. We demonstrate a high probability of concordance between predicted and observed survival for patients in distinct palliative care settings (home care, tertiary inpatient, ambulatory outpatient) based on patient-reported information.

Analysis of radiation therapy in a model of triple-negative breast cancer brain metastasis.

PubMed

Smart, DeeDee; Garcia-Glaessner, Alejandra; Palmieri, Diane; Wong-Goodrich, Sarah J; Kramp, Tamalee; Gril, Brunilde; Shukla, Sudhanshu; Lyle, Tiffany; Hua, Emily; Cameron, Heather A; Camphausen, Kevin; Steeg, Patricia S

2015-10-01

Most cancer patients with brain metastases are treated with radiation therapy, yet this modality has not yet been meaningfully incorporated into preclinical experimental brain metastasis models. We applied two forms of whole brain radiation therapy (WBRT) to the brain-tropic 231-BR experimental brain metastasis model of triple-negative breast cancer. When compared to sham controls, WBRT as 3 Gy × 10 fractions (3 × 10) reduced the number of micrometastases and large metastases by 87.7 and 54.5 %, respectively (both p < 0.01); whereas a single radiation dose of 15 Gy × 1 (15 × 1) was less effective, reducing metastases by 58.4 % (p < 0.01) and 47.1 % (p = 0.41), respectively. Neuroinflammation in the adjacent brain parenchyma was due solely to a reaction from metastases, and not radiotherapy, while adult neurogenesis in brains was adversely affected following both radiation regimens. The nature of radiation resistance was investigated by ex vivo culture of tumor cells that survived initial WBRT ("Surviving" cultures). The Surviving cultures surprisingly demonstrated increased radiosensitivity ex vivo. In contrast, re-injection of Surviving cultures and re-treatment with a 3 × 10 WBRT regimen significantly reduced the number of large and micrometastases that developed in vivo, suggesting a role for the microenvironment. Micrometastases derived from tumor cells surviving initial 3 × 10 WBRT demonstrated a trend toward radioresistance upon repeat treatment (p = 0.09). The data confirm the potency of a fractionated 3 × 10 WBRT regimen and identify the brain microenvironment as a potential determinant of radiation efficacy. The data also nominate the Surviving cultures as a potential new translational model for radiotherapy.

Prognostic Value of Protocadherin10 (PCDH10) Methylation in Serum of Prostate Cancer Patients.

PubMed

Deng, Qiu-Kui; Lei, Yong-Gang; Lin, Ying-Li; Ma, Jian-Guo; Li, Wen-Ping

2016-02-16

BACKGROUND Prostate cancer is a heterogeneous malignancy with outcome difficult to predict. Currently, there is an urgent need to identify novel biomarkers that can accurately predict patient outcome and improve the treatment strategy. The aim of this study was to investigate the methylation status of PCDH10 in serum of prostate cancer patients and its potential relevance to clinicopathological features and prognosis. MATERIAL AND METHODS The methylation status of PCDH10 in serum of 171 primary prostate cancer patients and 65 controls was evaluated by methylation-specific PCR (MSP), after which the relationship between PCDH10 methylation and clinicopathologic features was evaluated. Kaplan-Meier survival analysis and Cox analysis were used to evaluate the correlation between PCDH10 methylation and prognosis. RESULTS PCDH10 methylation occurred frequently in serum of prostate cancer patients. Moreover, PCDH10 methylation was significantly associated with higher preoperative PSA level, advanced clinical stage, higher Gleason score, lymph node metastasis, and biochemical recurrence (BCR). In addition, patients with methylated PCDH10 had shorter BCR-free survival and overall survival than patients with unmethylated PCDH10. Univariate and multivariate Cox proportional hazards model analysis indicated that PCDH10 methylation in serum is an independent predictor of worse BCR-free survival and overall survival. CONCLUSIONS PCDH10 methylation in serum is a potential prognostic biomarker for prostate cancer.

Marital status independently predicts testis cancer survival--an analysis of the SEER database.

PubMed

Abern, Michael R; Dude, Annie M; Coogan, Christopher L

2012-01-01

Previous reports have shown that married men with malignancies have improved 10-year survival over unmarried men. We sought to investigate the effect of marital status on 10-year survival in a U.S. population-based cohort of men with testis cancer. We examined 30,789 cases of testis cancer reported to the Surveillance, Epidemiology, and End Results (SEER 17) database between 1973 and 2005. All staging were converted to the 1997 AJCC TNM system. Patients less than 18 years of age at time of diagnosis were excluded. A subgroup analysis of patients with stages I or II non-seminomatous germ cell tumors (NSGCT) was performed. Univariate analysis using t-tests and χ(2) tests compared characteristics of patients separated by marital status. Multivariate analysis was performed using a Cox proportional hazard model to generate Kaplan-Meier survival curves, with all-cause and cancer-specific mortality as the primary endpoints. 20,245 cases met the inclusion criteria. Married men were more likely to be older (38.9 vs. 31.4 years), Caucasian (94.4% vs. 92.1%), stage I (73.1% vs. 61.4%), and have seminoma as the tumor histology (57.3% vs. 43.4%). On multivariate analysis, married status (HR 0.58, P < 0.001) and Caucasian race (HR 0.66, P < 0.001) independently predicted improved overall survival, while increased age (HR 1.05, P < 0.001), increased stage (HR 1.53-6.59, P < 0.001), and lymphoid (HR 4.05, P < 0.001), or NSGCT (HR 1.89, P < 0.001) histology independently predicted death. Similarly, on multivariate analysis, married status (HR 0.60, P < 0.001) and Caucasian race (HR 0.57, P < 0.001) independently predicted improved testis cancer-specific survival, while increased age (HR 1.03, P < 0.001), increased stage (HR 2.51-15.67, P < 0.001), and NSGCT (HR 2.54, P < 0.001) histology independently predicted testis cancer-specific death. A subgroup analysis of men with stages I or II NSGCT revealed similar predictors of all-cause survival as the overall cohort, with retroperitoneal lymph node dissection (RPLND) as an additional independent predictor of overall survival (HR 0.59, P = 0.001), despite equal rates of the treatment between married and unmarried men (44.8% vs. 43.4%, P = 0.33). Marital status is an independent predictor of improved overall and cancer-specific survival in men with testis cancer. In men with stages I or II NSGCT, RPLND is an additional predictor of improved overall survival. Marital status does not appear to influence whether men undergo RPLND. Copyright © 2012 Elsevier Inc. All rights reserved.

Effects of the proposed California WaterFix North Delta Diversion on survival of juvenile Chinook salmon (Oncorhynchus tshawytscha) in the Sacramento-San Joaquin River Delta, northern California

USGS Publications Warehouse

Perry, Russell W.; Pope, Adam C.

2018-05-11

The California Department of Water Resources and Bureau of Reclamation propose new water intake facilities on the Sacramento River in northern California that would convey some of the water for export to areas south of the Sacramento-San Joaquin River Delta (hereinafter referred to as the Delta) through tunnels rather than through the Delta. The collection of water intakes, tunnels, pumping facilities, associated structures, and proposed operations are collectively referred to as California WaterFix. The water intake facilities, hereinafter referred to as the North Delta Diversion (NDD), are proposed to be located on the Sacramento River downstream of the city of Sacramento and upstream of the first major river junction where Sutter Slough branches from the Sacramento River. The NDD can divert a maximum discharge of 9,000 cubic feet per second (ft3 /s) from the Sacramento River, which reduces the amount of Sacramento River inflow into the Delta. In this report, we conduct four analyses to investigate the effect of the NDD and its proposed operation on survival of juvenile Chinook salmon (Oncorhynchus tshawytscha). All analyses used the results of a Bayesian survival model that allowed us to simulate travel time, migration routing, and survival of juvenile Chinook salmon migrating through the Delta in response to NDD operations, which affected both inflows to the Delta and operation of the Delta Cross Channel (DCC). For the first analysis, we evaluated the effect of the NDD bypass rules on salmon survival. The NDD bypass rules are a set of operational rule curves designed to provide adaptive levels of fish protection by defining allowable diversion rates as a function of (1) Sacramento River discharge as measured at Freeport, and (2) time of year when endangered runs requiring the most protection are present. We determined that all bypass rule curves except constant low-level pumping (maximum diversion of 900 ft3 /s) could cause a sizeable decrease in survival by as much as 6–10 percentage points. The maximum decrease in survival occurred at an intermediate Sacramento River flow of about 20,000–30,000 ft3 /s. Diversion rates increased rapidly as Sacramento River flows increased from 20,000 ft3 /s to 30,000 ft3 /s, until a maximum diversion rate was reached at 9,000 ft3 /s. Because through-Delta survival increases sharply over this range of Sacramento River flow before beginning to level off with further flow increases, increasing diversion rates over this flow range causes a large decrease in survival relative to no diversion.  For the second analysis, we applied the survival model to 82 years of daily simulated flows under the Proposed Action (PA) and No Action Alternative (NAA). The PA includes operation of the Central Valley Project/State Water Project with implementation of the NDD and its operations prescribed by the NDD bypass rules, whereas the NAA assumes system operations without implementation of the NDD. We also evaluated a “Level 1” (L1) scenario, which was similar to the PA scenario but applied the most protective bypass rule known as Level 1 post-pulse operations. We noted a high probability that survival under the PA scenario was lower than under the NAA scenario, and that travel time was longer under PA relative to NAA in most simulation years. However, the largest survival differences between the PA and NAA scenarios occurred during October–November and May–June. Although bypass rules are less restrictive during these periods, we determined that more frequent use of the DCC under PA led to the largest differences in survival between the two scenarios. Additionally, we noted no difference in median survival decreases between the PA and L1 scenarios, although in some years the L1 scenario had a lower survival decrease than the PA scenario. For the third analysis, we proposed a quantitative approach for developing NDD rule curves (that is, prescribed diversion flows for given inflows) by using the survival model to identify diversion rates that meet a criterion of a having a small probability of exceeding a given decrease in survival. We examined diversion rates that led to a 10% chance of exceeding a given decrease in survival for a range of absolute and relative decreases in survival. To maintain a given constant level of protection across the range of river flows, our analysis indicated that diversions had to increase at a much slower rate with respect to Sacramento River flow relative to the rule curves defined in the NDD bypass table. Additionally, we determined that diversion rates could be higher than under the bypass table rule curves at river flows less than 20,000 ft3 /s, but diversions had to be less than defined by NDD bypass rules at higher flows. For the fourth analysis, we simulated the effect of “real-time operations” on salmon survival, where bypass flow rates were determined by the presence of juvenile salmon entering the Delta, as indicated by juvenile salmon catch in a rotary screw trap upstream of the Delta. For this analysis, we evaluated NDD operations as defined by the L1 scenario and an additional scenario (Unlimited Pulse Protection [UPP]) that provided protection to an unlimited number of fish pulses. This analysis indicated that the highest catches occurred during flow pulses when daily survival was high, which caused annual survival to be weighted towards periods of high daily survival, resulting in a high annual survival. We determined that the mean annual survival decreased by 1–4 percentage points, and annual survival decreases were more frequently smaller for the UPP scenario. Additionally, because the UPP scenario protected an unlimited number of fish pulses, decreases in daily survival under the UPP scenario were less than under the L1 scenario.

Quantitative estimation of the cost of parasitic castration in a Helisoma anceps population using a matrix population model.

PubMed

Negovetich, N J; Esch, G W

2008-10-01

Larval trematodes frequently castrate their snail intermediate hosts. When castrated, the snails do not contribute offspring to the population, yet they persist and compete with the uninfected individuals for the available food resources. Parasitic castration should reduce the population growth rate lambda, but the magnitude of this decrease is unknown. The present study attempted to quantify the cost of parasitic castration at the level of the population by mathematically modeling the population of the planorbid snail Helisoma anceps in Charlie's Pond, North Carolina. Analysis of the model identified the life-history trait that most affects lambda, and the degree to which parasitic castration can lower lambda. A period matrix product model was constructed with estimates of fecundity, survival, growth rates, and infection probabilities calculated in a previous study. Elasticity analysis was performed by increasing the values of the life-history traits by 10% and recording the percentage change in lambda. Parasitic castration resulted in a 40% decrease in lambda of H. anceps. Analysis of the model suggests that decreasing the size at maturity was more effective at reducing the cost of castration than increasing survival or growth rates of the snails. The current matrix model was the first to mathematically describe a snail population, and the predictions of the model are in agreement with published research.

Prognostic implications of 62Cu-diacetyl-bis (N4-methylthiosemicarbazone) PET/CT in patients with glioma.

PubMed

Toriihara, Akira; Ohtake, Makoto; Tateishi, Kensuke; Hino-Shishikura, Ayako; Yoneyama, Tomohiro; Kitazume, Yoshio; Inoue, Tomio; Kawahara, Nobutaka; Tateishi, Ukihide

2018-05-01

The potential of positron emission tomography/computed tomography using 62 Cu-diacetyl-bis (N 4 -methylthiosemicarbazone) ( 62 Cu-ATSM PET/CT), which was originally developed as a hypoxic tracer, to predict therapeutic resistance and prognosis has been reported in various cancers. Our purpose was to investigate prognostic value of 62 Cu-ATSM PET/CT in patients with glioma, compared to PET/CT using 2-deoxy-2-[ 18 F]fluoro-D-glucose ( 18 F-FDG). 56 patients with glioma of World Health Organization grade 2-4 were enrolled. All participants had undergone both 62 Cu-ATSM PET/CT and 18 F-FDG PET/CT within mean 33.5 days prior to treatment. Maximum standardized uptake value and tumor/background ratio were calculated within areas of increased radiotracer uptake. The prognostic significance for progression-free survival and overall survival were assessed by log-rank test and Cox's proportional hazards model. Disease progression and death were confirmed in 37 and 27 patients in follow-up periods, respectively. In univariate analysis, there was significant difference of both progression-free survival and overall survival in age, tumor grade, history of chemoradiotherapy, maximum standardized uptake value and tumor/background ratio calculated using 62 Cu-ATSM PET/CT. Multivariate analysis revealed that maximum standardized uptake value calculated using 62 Cu-ATSM PET/CT was an independent predictor of both progression-free survival and overall survival (p < 0.05). In a subgroup analysis including patients of grade 4 glioma, only the maximum standardized uptake values calculated using 62 Cu-ATSM PET/CT showed significant difference of progression-free survival (p < 0.05). 62 Cu-ATSM PET/CT is a more promising imaging method to predict prognosis of patients with glioma compared to 18 F-FDG PET/CT.

Tamoxifen therapy improves overall survival in luminal A subtype of ductal carcinoma in situ: a study based on nationwide Korean Breast Cancer Registry database.

PubMed

Hwang, Ki-Tae; Kim, Eun-Kyu; Jung, Sung Hoo; Lee, Eun Sook; Kim, Seung Il; Lee, Seokwon; Park, Heung Kyu; Kim, Jongjin; Oh, Sohee; Kim, Young A

2018-06-01

To determine the prognostic role of tamoxifen therapy for patients with ductal carcinoma in situ (DCIS) according to molecular subtypes. Data of 14,944 patients with DCIS were analyzed. Molecular subtypes were classified into four categories based on expression of estrogen receptor (ER)/progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2). Kaplan-Meier estimator was used for overall survival analysis while Cox proportional hazards model was used for univariate and multivariate analyses. Luminal A subtype (ER/PR+, HER2-) showed higher (P = .009) survival rate than triple-negative (TN) subtype. Tamoxifen therapy group showed superior (P < .001) survival than no-tamoxifen therapy group. It had survival benefit only for luminal A subtype (P = .001). Tamoxifen therapy resulted in higher survival rate in subgroups with positive ER (P = .006), positive PR (P = .009), and negative HER2 (P < .001). In luminal A subtype, tamoxifen therapy showed lower hazard ratio (HR) compared to no-tamoxifen therapy (HR, 0.420; 95% CI 0.250-0.705; P = .001). Tamoxifen therapy was a significant independent factor by multivariate analysis (HR, 0.538; 95% CI 0.306-0.946; P = .031) as well as univariate analysis. Tamoxifen therapy group showed superior prognosis than the no-tamoxifen therapy group. Its prognostic influence was only effective for luminal A subtype. Patients with luminal A subtype showed higher survival rate than those with TN subtype. Active tamoxifen therapy is recommended for DCIS patients with luminal A subtype, and routine tests for ER, PR, and HER2 should be considered for DCIS.

Beta blockers and cancer prognosis - The role of immortal time bias: A systematic review and meta-analysis.

PubMed

Weberpals, Janick; Jansen, Lina; Carr, Prudence R; Hoffmeister, Michael; Brenner, Hermann

2016-06-01

Findings from experimental and observational studies have suggested beneficial effects of beta blocker (BB) use on cancer survival. Nevertheless, results have been inconclusive and there have been concerns that the observed associations might have resulted from immortal time bias (ITB). We conducted a systematic review and meta-analysis to summarize existing evidence, paying particular attention to this potential source of bias. A systematic literature search was performed in PubMed and Web of Science. Studies investigating the association between BB use and overall or cancer-specific survival were included. Summary estimates were derived from meta-analyses using random effects models. The potential influence of ITB was investigated. We identified 30 eligible studies including 88,026 cancer patients in total. We deemed 11 studies to be at high or unclear risk of ITB. Including all studies in the meta-analysis, BB users had a significantly better overall (hazard ratio (HR) 0.88, 95% CI 0.79-0.97) and cancer-specific (HR 0.75, 95% CI 0.64-0.88) survival. Excluding the studies deemed to be prone to ITB resulted in HRs (95% CIs) of 1.00 (0.93-1.07) and 0.90 (0.83-0.98), respectively. Analyses on cancer site and BB type did not show beneficial associations besides overall survival among melanoma patients. However, melanoma-specific survival was not improved. We found no clinically meaningful evidence for an association between BB use and survival after excluding studies with a possible ITB. Our results support suggestions that the proposed beneficial effect of BBs on cancer survival might be based on ITB. Copyright © 2016 Elsevier Ltd. All rights reserved.

Red blood cell distribution width as a predictor of survival in nasal-type, extranodal natural killer/T-cell lymphoma

PubMed Central

He, Qiao; Cai, Shaolei; Li, Shi; Zeng, Jian; Zhang, Qing; Gao, Yu; Yu, Sisi

2017-01-01

We retrospectively enrolled 191 nasal-type, extranodal natural killer/T-cell lymphoma (ENKTL) patients newly diagnosed from 2008 to 2016 at the Sichuan Cancer Hospital, in order to evaluate the relationship between disease outcomes, demographic and clinical factors, and red blood cell distribution width (RDW). C-index, fisher's exact test, univariate analysis, and cox regression analysis were applied. The median age of patients was 44 years and 134 (70%) were men. The cutoff of RDW was 46.2 fL determined by Cutoff Finder. Patients with RDW≤46.2 fL had significantly better progression-free survival (PFS) (3-year PFS, 80.4% vs. 63.1%; P=0.01) and overall survival (OS) (3-year OS, 83.2% vs. 65.5%; P=0.004) than those with RDW>46.2 fL. Multivariate analysis demonstrated that elevated RDW is an independent adverse predictor of OS (P=0.021, HR=2.04). RDW is an independent predictor of survival outcomes in ENKTL, which we found to be superior to both the prognostic index of natural killer lymphoma (PINK) and the Korean Prognostic Index (KPI) in discriminating patients with different outcomes in low-risk and high-risk groups (all P < 0.05). The new models combining RDW with the International Prognostic Index (IPI), KPI, and PINK showed more powerful prognostic value than corresponding original models. RDW represents an easily available and inexpensive marker for risk stratification in patients with ENKTL treated with radiotherapy-based treatment. Further prospective studies are warranted to confirm the prognostic value of RDW in ENKTL. PMID:29190934

Red blood cell distribution width as a predictor of survival in nasal-type, extranodal natural killer/T-cell lymphoma.

PubMed

Luo, Huaichao; Quan, Xiaoying; Song, Xiao-Yu; Zhang, Li; Yin, Yilin; He, Qiao; Cai, Shaolei; Li, Shi; Zeng, Jian; Zhang, Qing; Gao, Yu; Yu, Sisi

2017-11-03

We retrospectively enrolled 191 nasal-type, extranodal natural killer/T-cell lymphoma (ENKTL) patients newly diagnosed from 2008 to 2016 at the Sichuan Cancer Hospital, in order to evaluate the relationship between disease outcomes, demographic and clinical factors, and red blood cell distribution width (RDW). C-index, fisher's exact test, univariate analysis, and cox regression analysis were applied. The median age of patients was 44 years and 134 (70%) were men. The cutoff of RDW was 46.2 fL determined by Cutoff Finder. Patients with RDW≤46.2 fL had significantly better progression-free survival (PFS) (3-year PFS, 80.4% vs. 63.1%; P =0.01) and overall survival (OS) (3-year OS, 83.2% vs. 65.5%; P =0.004) than those with RDW>46.2 fL. Multivariate analysis demonstrated that elevated RDW is an independent adverse predictor of OS ( P =0.021, HR=2.04). RDW is an independent predictor of survival outcomes in ENKTL, which we found to be superior to both the prognostic index of natural killer lymphoma (PINK) and the Korean Prognostic Index (KPI) in discriminating patients with different outcomes in low-risk and high-risk groups (all P < 0.05). The new models combining RDW with the International Prognostic Index (IPI), KPI, and PINK showed more powerful prognostic value than corresponding original models. RDW represents an easily available and inexpensive marker for risk stratification in patients with ENKTL treated with radiotherapy-based treatment. Further prospective studies are warranted to confirm the prognostic value of RDW in ENKTL.

Meta-analysis and cost effective analysis of portal-superior mesenteric vein resection during pancreatoduodenectomy: Impact on margin status and survival.

PubMed

Bell, Richard; Ao, Braden Te; Ironside, Natasha; Bartlett, Adam; Windsor, John A; Pandanaboyana, Sanjay

2017-03-01

The benefit of portal-superior mesenteric vein resection (PSMVR) with pancreatoduodenectomy (PD) remains controversial. This study assesses the impact of PSMVR on resection margin status and survival. An electronic search was performed to identify relevant articles. Pooled odds ratios were calculated for outcomes using the fixed or random-effects models for meta-analysis. A decision analytical model was developed for estimating cost effectiveness. Sixteen studies with 4145 patients who underwent pancreatoduodenectomy were included: 1207 patients had PSMVR and 2938 patients had no PSMVR. The R1 resection rate and post-operative mortality was significantly higher in PSMVR group (OR1.59[1.35, 1.86] p=<0.0001, and OR1.72 [1.02,2.92] p = 0.04 respectively). The overall survival at 5-years was worse in the PSMVR group (HR0.20 [0.07,0.55] P = 0.020). Tumour size (p = 0.030) and perineural invasion (P = 0.009) were higher in the PSMVR group. Not performing PSMVR yielded cost savings of $1617 per additional month alive without reduction in overall outcome. On the basis of retrospective data this study shows that PD with PSMVR is associated with a higher R1 rate, lower 5-year survival and is not cost-effective. It appears that PD with PSMVR can only be justified if R0 resection can be achieved. The continuing challenge is accurate selection of these patients. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prognostic role of tumor-infiltrating lymphocytes in gastric cancer: a meta-analysis

PubMed Central

Shao, Yingjie; Xu, Bin; Chen, Lujun; Zhou, Qi; Hu, Wenwei; Zhang, Dachuan; Wu, Changping; Tao, Min; Zhu, Yibei; Jiang, Jingting

2017-01-01

Background In patients with gastric cancer, the prognostic value of tumor-infiltrating lymphocytes (TILs) is still controversial. A meta-analysis was performed to evaluate the prognostic value of TILs in gastric cancer. Materials and methods We identify studies from PubMed, Embase and the Cochrane Library to assess the prognostic effect of TILs in patients with gastric cancer. Fixed-effects models or random-effects models were used estimate the pooled hazard ratios (HRs) for overall survival (OS) and disease-free survival (DFS), which depend on the heterogeneity. Results A total of 31 observational studies including 4,185 patients were enrolled. For TILs subsets, the amount of CD8+, FOXP3+, CD3+, CD57+, CD20+, CD45RO+, Granzyme B+ and T-bet+ lymphocytes was significantly associated with improved survival (P < 0.05); moreover, the amount of CD3+ TILs in intra-tumoral compartment (IT) was the most significant prognostic marker (pooled HR = 0.52; 95% CI = 0.43–0.63; P < 0.001). However, CD4+ TILs was not statistically associated with patients’ survival. FOXP3+ TILs showed bidirectional prognostic roles which had positive effect in IT (pooled HR = 1.57; 95% CI = 1.04–2.37; P = 0.033) and negative effect in extra-tumoral compartment (ET) (pooled HR = 0.76; 95% CI = 0.60–0.96; P = 0.022). Conclusions This meta-analysis suggests that some TIL subsets could serve as prognostic biomarkers in gastric cancer. High-quality randomized controlled trials are needed to decide if these TILs could serve as targets for immunotherapy in gastric cancer. PMID:28915679

Tissue Platinum Concentration and Tumor Response in Non–Small-Cell Lung Cancer

PubMed Central

Kim, Eric S.; Lee, J. Jack; He, Guangan; Chow, Chi-Wan; Fujimoto, Junya; Kalhor, Neda; Swisher, Stephen G.; Wistuba, Ignacio I.; Stewart, David J.; Siddik, Zahid H.

2012-01-01

Purpose Platinum resistance is a major limitation in the treatment of advanced non–small-cell lung cancer (NSCLC). Reduced intracellular drug accumulation is one of the most consistently identified features of platinum-resistant cell lines, but clinical data are limited. We assessed the effects of tissue platinum concentrations on response and survival in NSCLC. Patients and Methods We measured total platinum concentrations by flameless atomic absorption spectrophotometry in 44 archived fresh-frozen NSCLC specimens from patients who underwent surgical resection after neoadjuvant platinum-based chemotherapy. Tissue platinum concentration was correlated with percent reduction in tumor size on post- versus prechemotherapy computed tomography scans. The relationship between tissue platinum concentration and survival was assessed by univariate and multicovariate Cox proportional hazards regression model analysis and Kaplan-Meier analysis. Results Tissue platinum concentration correlated significantly with percent reduction in tumor size (P < .001). The same correlations were seen with cisplatin, carboplatin, and all histology subgroups. Furthermore, there was no significant impact of potential variables such as number of cycles and time lapse from last chemotherapy on platinum concentration. Patients with higher platinum concentration had longer time to recurrence (P = .034), progression-free survival (P = .018), and overall survival (P = .005) in the multicovariate Cox model analysis after adjusting for number of cycles. Conclusion This clinical study established a relationship between tissue platinum concentration and response in NSCLC. It suggests that reduced platinum accumulation might be an important mechanism of platinum resistance in the clinical setting. Further studies investigating factors that modulate intracellular platinum concentration are warranted. PMID:22891266

Bacterial Infections Change Natural History of Cirrhosis Irrespective of Liver Disease Severity.

PubMed

Dionigi, Elena; Garcovich, Matteo; Borzio, Mauro; Leandro, Gioacchino; Majumdar, Avik; Tsami, Aikaterini; Arvaniti, Vasiliki; Roccarina, Davide; Pinzani, Massimo; Burroughs, Andrew K; O'Beirne, James; Tsochatzis, Emmanuel A

2017-04-01

We assessed the prognostic significance of infections in relation to current prognostic scores and explored if infection could be considered per se a distinct clinical stage in the natural history of cirrhosis. We included consecutive patients with cirrhosis admitted to a tertiary referral liver unit for at least 48 h over a 2-year period. Diagnosis of infection was based on positive cultures or strict established criteria. We used competing risk analysis and propensity score matching for data analysis. 501 patients (63% male, 48% alcoholic liver disease, median Model of End-stage Liver Disease (MELD)=17) underwent 781 admissions over the study period. Portal hypertensive bleeding and complicated ascites were the commonest reasons of admission. The incidence of proven bacterial infection was 25.6% (60% community acquired and 40% nosocomial). Survival rates at 3, 6, 12, and 30 months were 83%, 77%, 71%, and 62% in patients without diagnosis of infection, vs. 50%, 46%, 41%, and 34% in patients with diagnosis of infection. Overall survival was independently associated with MELD score (hazards ratio (HR) 1.099), intensive care (ITU) stay (HR 1.967) and bacterial infection (HR 2.226). Bacterial infection was an independent predictor of survival even when patients who died within the first 30 days were excluded from the analysis in Cox regression (HR 2.013) and competing risk Cox models in all patients (HR 1.46) and propensity risk score-matched infected and non-infected patients (HR 1.67). Infection most likely represents a distinct prognostic stage of cirrhosis, which affects survival irrespective of disease severity, even after recovery from the infective episode.

Parent-child communication and marijuana initiation: evidence using discrete-time survival analysis.

PubMed

Nonnemaker, James M; Silber-Ashley, Olivia; Farrelly, Matthew C; Dench, Daniel

2012-12-01

This study supplements existing literature on the relationship between parent-child communication and adolescent drug use by exploring whether parental and/or adolescent recall of specific drug-related conversations differentially impact youth's likelihood of initiating marijuana use. Using discrete-time survival analysis, we estimated the hazard of marijuana initiation using a logit model to obtain an estimate of the relative risk of initiation. Our results suggest that parent-child communication about drug use is either not protective (no effect) or - in the case of youth reports of communication - potentially harmful (leading to increased likelihood of marijuana initiation). Copyright © 2012 Elsevier Ltd. All rights reserved.

Estimates of annual survival, growth, and recruitment of a white-tailed ptarmigan population in Colorado over 43 years

USGS Publications Warehouse

Wann, Greg; Aldridge, Cameron L.; Braun, Clait E.

2014-01-01

Long-term datasets for high-elevation species are rare, and considerable uncertainty exists in understanding how high-elevation populations have responded to recent climate warming. We present estimates of demographic vital rates from a 43-year population study of white-tailed ptarmigan (Lagopus leucura), a species endemic to alpine habitats in western North America. We used capture-recapture models to estimate annual rates of apparent survival, population growth, and recruitment for breeding-age ptarmigan, and we fit winter weather covariates to models in an attempt to explain annual variation. There were no trends in survival over the study period but there was strong support for age and sex effects. The average rate of annual growth suggests a relatively stable breeding-age population ( λ ¯ = 1.036), but there was considerable variation between years for both population growth and recruitment rates. Winter weather covariates only explained a small amount of variation in female survival and were not an important predictor of male survival. Cumulative winter precipitation was found to have a quadratic effect on female survival, with survival being highest during years of average precipitation. Cumulative winter precipitation was positively correlated with population growth and recruitment rates, although this covariate only explained a small amount of annual variation in these rates and there was considerable uncertainty among the models tested. Our results provide evidence for an alpine-endemic population that has not experienced extirpation or drastic declines. However, more information is needed to understand risks and vulnerabilities of warming effects on juveniles as our analysis was confined to determination of vital rates for breeding-age birds.

Summary of survival data from juvenile coho salmon in the Klamath River, northern California, 2009

USGS Publications Warehouse

Beeman, John W.; Juhnke, Steven D.

2009-01-01

A study of the effects of the discharge from Iron Gate Dam on the Klamath River on juvenile coho salmon during their seaward migration began in 2005. Estimates of fish survival through various reaches of the river downstream of the dam were completed in 2006, 2007, 2008, and 2009. This report describes the estimates of survival during 2009, and is a complement to similar reports for 2006, 2007, and 2008. For each year, a series of numerical models were evaluated to determine apparent survival and recapture probabilities of radio-tagged fish in several river reaches between Iron Gate Hatchery at river kilometer 309 and a site at river kilometer 33. The evaluations indicate that the primary differences among years are in the survivals through reaches upstream of the confluence of the Scott River with the Klamath River. Data from 2009, one of two years when fish from both hatchery and wild origins were available for analysis, indicate that survival of wild and hatchery fish are similar.

Evolutionary dynamics of fearfulness and boldness.

PubMed

Ji, Ting; Zhang, Boyu; Sun, Yuehua; Tao, Yi

2009-02-21

A negative relationship between reproductive effort and survival is consistent with life-history. Evolutionary dynamics and evolutionarily stable strategy (ESS) for the trade-off between survival and reproduction are investigated using a simple model with two phenotypes, fearfulness and boldness. The dynamical stability of the pure strategy model and analysis of ESS conditions reveal that: (i) the simple coexistence of fearfulness and boldness is impossible; (ii) a small population size is favorable to fearfulness, but a large population size is favorable to boldness, i.e., neither fearfulness, nor boldness is always favored by natural selection; and (iii) the dynamics of population density is crucial for a proper understanding of the strategy dynamics.

Evaluation of long-term survival: use of diagnostics and robust estimators with Cox's proportional hazards model.

PubMed

Valsecchi, M G; Silvestri, D; Sasieni, P

1996-12-30

We consider methodological problems in evaluating long-term survival in clinical trials. In particular we examine the use of several methods that extend the basic Cox regression analysis. In the presence of a long term observation, the proportional hazard (PH) assumption may easily be violated and a few long term survivors may have a large effect on parameter estimates. We consider both model selection and robust estimation in a data set of 474 ovarian cancer patients enrolled in a clinical trial and followed for between 7 and 12 years after randomization. Two diagnostic plots for assessing goodness-of-fit are introduced. One shows the variation in time of parameter estimates and is an alternative to PH checking based on time-dependent covariates. The other takes advantage of the martingale residual process in time to represent the lack of fit with a metric of the type 'observed minus expected' number of events. Robust estimation is carried out by maximizing a weighted partial likelihood which downweights the contribution to estimation of influential observations. This type of complementary analysis of long-term results of clinical studies is useful in assessing the soundness of the conclusions on treatment effect. In the example analysed here, the difference in survival between treatments was mostly confined to those individuals who survived at least two years beyond randomization.

Nomograms Predicting Progression-Free Survival, Overall Survival, and Pelvic Recurrence in Locally Advanced Cervical Cancer Developed From an Analysis of Identifiable Prognostic Factors in Patients From NRG Oncology/Gynecologic Oncology Group Randomized Trials of Chemoradiotherapy

PubMed Central

Rose, Peter G.; Java, James; Whitney, Charles W.; Stehman, Frederick B.; Lanciano, Rachelle; Thomas, Gillian M.; DiSilvestro, Paul A.

2015-01-01

Purpose To evaluate the prognostic factors in locally advanced cervical cancer limited to the pelvis and develop nomograms for 2-year progression-free survival (PFS), 5-year overall survival (OS), and pelvic recurrence. Patients and Methods We retrospectively reviewed 2,042 patients with locally advanced cervical carcinoma enrolled onto Gynecologic Oncology Group clinical trials of concurrent cisplatin-based chemotherapy and radiotherapy. Nomograms for 2-year PFS, five-year OS, and pelvic recurrence were created as visualizations of Cox proportional hazards regression models. The models were validated by bootstrap-corrected, relatively unbiased estimates of discrimination and calibration. Results Multivariable analysis identified prognostic factors including histology, race/ethnicity, performance status, tumor size, International Federation of Gynecology and Obstetrics stage, tumor grade, pelvic node status, and treatment with concurrent cisplatin-based chemotherapy. PFS, OS, and pelvic recurrence nomograms had bootstrap-corrected concordance indices of 0.62, 0.64, and 0.73, respectively, and were well calibrated. Conclusion Prognostic factors were used to develop nomograms for 2-year PFS, 5-year OS, and pelvic recurrence for locally advanced cervical cancer clinically limited to the pelvis treated with concurrent cisplatin-based chemotherapy and radiotherapy. These nomograms can be used to better estimate individual and collective outcomes. PMID:25732170

Effects of desiccation stress on adult female longevity in Aedes aegypti and Ae. albopictus (Diptera: Culicidae): results of a systematic review and pooled survival analysis.

PubMed

Schmidt, Chris A; Comeau, Genevieve; Monaghan, Andrew J; Williamson, Daniel J; Ernst, Kacey C

2018-04-25

Transmission dynamics of mosquito-borne viruses such as dengue, Zika and chikungunya are affected by the longevity of the adult female mosquito. Environmental conditions influence the survival of adult female Aedes mosquitoes, the primary vectors of these viruses. While the association of temperature with Aedes mortality has been relatively well-explored, the role of humidity is less established. The current study's goals were to compile knowledge of the influence of humidity on adult survival in the important vector species Aedes aegypti and Ae. albopictus, and to quantify this relationship while accounting for the modifying effect of temperature. We performed a systematic literature review to identify studies reporting experimental results informing the relationships among temperature, humidity and adult survival in Ae. aegypti and Ae. albopictus. Using a novel simulation approach to harmonize disparate survival data, we conducted pooled survival analyses via stratified and mixed effects Cox regression to estimate temperature-dependent associations between humidity and mortality risk for these species across a broad range of temperatures and vapor pressure deficits. After screening 1517 articles, 17 studies (one in semi-field and 16 in laboratory settings) met inclusion criteria and collectively reported results for 192 survival experiments. We review and synthesize relevant findings from these studies. Our stratified model estimated a strong temperature-dependent association of humidity with mortality in both species, though associations were not significant for Ae. albopictus in the mixed effects model. Lowest mortality risks were estimated around 27.5 °C and 21.5 °C for Ae. aegypti and Ae. albopictus, respectively, and mortality increased non-linearly with decreasing humidity. Aedes aegypti had a survival advantage relative to Ae. albopictus in the stratified model under most conditions, but species differences were not significant in the mixed effects model. Humidity is associated with mortality risk in adult female Ae. aegypti in controlled settings. Data are limited at low humidities, temperature extremes, and for Ae. albopictus, and further studies should be conducted to reduce model uncertainty in these contexts. Desiccation is likely an important factor in Aedes population dynamics and viral transmission in arid regions. Models of Aedes-borne virus transmission may be improved by more comprehensively representing humidity effects.

Invasive micropapillary carcinoma of the breast has a better long-term survival than invasive ductal carcinoma of the breast in spite of its aggressive clinical presentations: a comparison based on large population database and case-control analysis.

PubMed

Chen, Hongliang; Wu, Kejin; Wang, Maoli; Wang, Fuwen; Zhang, Mingdi; Zhang, Peng

2017-12-01

There are controversies in the comparison of overall survival between invasive micropapillary carcinoma of the breast (IMPC) and invasive ductal carcinoma (IDC). The objective of this study was to compare the long-term survival outcome between non-metastatic IMPC and IDC. The Surveillance, Epidemiology, and End Results database was searched to identify women with non-metastatic IMPC and IDC diagnosed between 2001 and 2013. Comparisons of patient and tumor characteristics were performed using Pearson's chi-square. The propensity score matching method was applied with each IMPC matched to one IDC. Breast cancer-specific survival (BCSS) and overall survival (OS) were estimated using the Kaplan-Meier product limit method and compared across groups using the log-rank statistic. Multivariate analysis was performed through Cox models. IMPC was presented with aggressive clinical presentations such as larger tumor, more positive lymph nodes, and more advanced stage compared with IDC. A higher rate of estrogen receptor (ER)/progesterone receptor (PR) positivity was also observed in IMPC. With a median follow-up of 64 months, IMPC had a better BCSS (P = 0.031) and OS (P = 0.012) compared with IDC. In a case-control analysis IMPC was still an independent favorable prognostic factor for BCSS (HR = 0.410, P < 0.001, 95% CI: 0.293-0.572) and OS (HR = 0.497, P < 0.001, 95% CI: 0.387-0.637). In subgroup analysis, IMPC always showed a better survival outcome compared with IDC except in AJCC stage I and histologic grade I disease. IMPC has a better long-term survival outcome compared with IDC in spite of its highly aggressive clinical presentation. © 2017 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Effects of aurothiomalate treatment on canine osteosarcoma in a murine xenograft model.

PubMed

Scharf, Valery F; Farese, James P; Siemann, Dietmar W; Abbott, Jeffrey R; Kiupel, Matti; Salute, Marc E; Milner, Rowan J

2014-03-01

Osteosarcoma is a highly fatal cancer, with most patients ultimately succumbing to metastatic disease. The purpose of this study was to evaluate the effects of the antirheumatoid drug aurothiomalate on canine and human osteosarcoma cells and on canine osteosarcoma growth and metastasis in a mouse xenograft model. We hypothesized that aurothiomalate would decrease osteosarcoma cell survival, tumor cellular proliferation, tumor growth, and metastasis. After performing clonogenic assays, aurothiomalate or a placebo was administered to 54 mice inoculated with canine osteosarcoma. Survival, tumor growth, embolization, metastasis, histopathology, cell proliferation marker Ki67, and apoptosis marker caspase-3 were compared between groups. Statistical analysis was carried out using the Kaplan-Meier method with the log-rank test and one-way analysis of variance with the Tukey's test or Dunn's method. Aurothiomalate caused dose-dependent inhibition of osteosarcoma cell survival (P<0.001) and decreased tumor growth (P<0.001). Pulmonary macrometastasis and Ki67 labeling were reduced with low-dose aurothiomalate (P=0.033 and 0.005, respectively), and tumor emboli and pulmonary micrometastases were decreased with high-dose aurothiomalate (P=0.010 and 0.011, respectively). There was no difference in survival, tumor development, ulceration, mitotic indices, tumor necrosis, nonpulmonary metastases, and caspase-3 labeling. Aurothiomalate treatment inhibited osteosarcoma cell survival and reduced tumor cell proliferation, growth, embolization, and pulmonary metastasis. Given aurothiomalate's established utility in canine and human medicine, our results suggest that this compound may hold promise as an adjunctive therapy for osteosarcoma. Further translational research is warranted to better characterize the dose response of canine and human osteosarcoma to aurothiomalate.

Embedding Multilevel Survival Analysis of Dyadic Social Interaction in Structural Equation Models: Hazard Rates as Both Outcomes and Predictors

PubMed Central

Snyder, James

2014-01-01

Objective Demonstrate multivariate multilevel survival analysis within a larger structural equation model. Test the 3 hypotheses that when confronted by a negative parent, child rates of angry, sad/fearful, and positive emotion will increase, decrease, and stay the same, respectively, for antisocial compared with normal children. This same pattern will predict increases in future antisocial behavior. Methods Parent–child dyads were videotaped in the fall of kindergarten in the laboratory and antisocial behavior ratings were obtained in the fall of kindergarten and third grade. Results Kindergarten antisocial predicted less child sad/fear and child positive but did not predict child anger given parent negative. Less child positive and more child neutral given parent negative predicted increases in third-grade antisocial behavior. Conclusions The model is a useful analytic tool for studying rates of social behavior. Lack of positive affect or excess neutral affect may be a new risk factor for child antisocial behavior. PMID:24133296

Surrogate marker analysis in cancer clinical trials through time-to-event mediation techniques.

PubMed

Vandenberghe, Sjouke; Duchateau, Luc; Slaets, Leen; Bogaerts, Jan; Vansteelandt, Stijn

2017-01-01

The meta-analytic approach is the gold standard for validation of surrogate markers, but has the drawback of requiring data from several trials. We refine modern mediation analysis techniques for time-to-event endpoints and apply them to investigate whether pathological complete response can be used as a surrogate marker for disease-free survival in the EORTC 10994/BIG 1-00 randomised phase 3 trial in which locally advanced breast cancer patients were randomised to either taxane or anthracycline based neoadjuvant chemotherapy. In the mediation analysis, the treatment effect is decomposed into an indirect effect via pathological complete response and the remaining direct effect. It shows that only 4.2% of the treatment effect on disease-free survival after five years is mediated by the treatment effect on pathological complete response. There is thus no evidence from our analysis that pathological complete response is a valuable surrogate marker to evaluate the effect of taxane versus anthracycline based chemotherapies on progression free survival of locally advanced breast cancer patients. The proposed analysis strategy is broadly applicable to mediation analyses of time-to-event endpoints, is easy to apply and outperforms existing strategies in terms of precision as well as robustness against model misspecification.

Advanced techniques for modeling avian nest survival

USGS Publications Warehouse

Dinsmore, S.J.; White, Gary C.; Knopf, F.L.

2002-01-01

Estimation of avian nest survival has traditionally involved simple measures of apparent nest survival or Mayfield constant-nest-survival models. However, these methods do not allow researchers to build models that rigorously assess the importance of a wide range of biological factors that affect nest survival. Models that incorporate greater detail, such as temporal variation in nest survival and covariates representative of individual nests represent a substantial improvement over traditional estimation methods. In an attempt to improve nest survival estimation procedures, we introduce the nest survival model now available in the program MARK and demonstrate its use on a nesting study of Mountain Plovers (Charadrius montanus Townsend) in Montana, USA. We modeled the daily survival of Mountain Plover nests as a function of the sex of the incubating adult, nest age, year, linear and quadratic time trends, and two weather covariates (maximum daily temperature and daily precipitation) during a six-year study (1995–2000). We found no evidence for yearly differences or an effect of maximum daily temperature on the daily nest survival of Mountain Plovers. Survival rates of nests tended by female and male plovers differed (female rate = 0.33; male rate = 0.49). The estimate of the additive effect for males on nest survival rate was 0.37 (95% confidence limits were 0.03, 0.71) on a logit scale. Daily survival rates of nests increased with nest age; the estimate of daily nest-age change in survival in the best model was 0.06 (95% confidence limits were 0.04, 0.09) on a logit scale. Daily precipitation decreased the probability that the nest would survive to the next day; the estimate of the additive effect of daily precipitation on the nest survival rate was −1.08 (95% confidence limits were −2.12, −0.13) on a logit scale. Our approach to modeling daily nest-survival rates allowed several biological factors of interest to be easily included in nest survival models and allowed us to generate more biologically meaningful estimates of nest survival.

Interactive effects of water diversion and climate change for juvenile chinook salmon in the lemhi river basin (USA.).

PubMed

Walters, Annika W; Bartz, Krista K; McClure, Michelle M

2013-12-01

The combined effects of water diversion and climate change are a major conservation challenge for freshwater ecosystems. In the Lemhi Basin, Idaho (U.S.A.), water diversion causes changes in streamflow, and climate change will further affect streamflow and temperature. Shifts in streamflow and temperature regimes can affect juvenile salmon growth, movement, and survival. We examined the potential effects of water diversion and climate change on juvenile Chinook salmon (Oncorhynchus tshawytscha), a species listed as threatened under the U.S. Endangered Species Act (ESA). To examine the effects for juvenile survival, we created a model relating 19 years of juvenile survival data to streamflow and temperature and found spring streamflow and summer temperature were good predictors of juvenile survival. We used these models to project juvenile survival for 15 diversion and climate-change scenarios. Projected survival was 42-58% lower when streamflows were diverted than when streamflows were undiverted. For diverted streamflows, 2040 climate-change scenarios (ECHO-G and CGCM3.1 T47) resulted in an additional 11-39% decrease in survival. We also created models relating habitat carrying capacity to streamflow and made projections for diversion and climate-change scenarios. Habitat carrying capacity estimated for diverted streamflows was 17-58% lower than for undiverted streamflows. Climate-change scenarios resulted in additional decreases in carrying capacity for the dry (ECHO-G) climate model. Our results indicate climate change will likely pose an additional stressor that should be considered when evaluating the effects of anthropogenic actions on salmon population status. Thus, this type of analysis will be especially important for evaluating effects of specific actions on a particular species. Efectos Interactivos de la Desviación del Agua y el Cambio Climático en Individuos Juveniles de Salmón Chinook en la Cuenca del Río Lemhi (E.U.A.). Conservation Biology © 2013 Society for Conservation Biology No claim to original US government works.

Active compression-decompression cardiopulmonary resuscitation (CPR) versus standard CPR for cardiac arrest patients: a meta-analysis

PubMed Central

Luo, Xu-rui; Zhang, Hui-li; Chen, Geng-jin; Ding, Wen-shu; Huang, Liang

2013-01-01

BACKGROUND: Active compression-decompression cardiopulmonary resuscitation (ACDCPR) has been popular in the treatment of patients with cardiac arrest (CA). However, the effect of ACD-CPR versus conventional standard CPR (S-CRP) is contriversial. This study was to analyze the efficacy and safety of ACD-CPR versus S-CRP in treating CA patients. METHODS: Randomized or quasi-randomized controlled trials published from January 1990 to March 2011 were searched with the phrase “active compression-decompression cardiopulmonary resuscitation and cardiac arrest” in PubMed, EmBASE, and China Biomedical Document Databases. The Cochrane Library was searched for papers of meta-analysis. Restoration of spontaneous circulation (ROSC) rate, survival rate to hospital admission, survival rate at 24 hours, and survival rate to hospital discharge were considered primary outcomes, and complications after CPR were viewed as secondary outcomes. Included studies were critically appraised and estimates of effects were calculated according to the model of fixed or random effects. Inconsistency across the studies was evaluated using the I2 statistic method. Sensitivity analysis was made to determine statistical heterogeneity. RESULTS: Thirteen studies met the criteria for this meta-analysis. The studies included 396 adult CA patients treated by ACD-CPR and 391 patients by S-CRP. Totally 234 CA patients were found out hospitals, while the other 333 CA patients were in hospitals. Two studies were evaluated with high-quality methodology and the rest 11 studies were of poor quality. ROSC rate, survival rate at 24 hours and survival rate to hospital discharge with favorable neurological function indicated that ACD-CPR is superior to S-CRP, with relative risk (RR) values of 1.39 (95% CI 0.99–1.97), 1.94 (95% CI 1.45–2.59) and 2.80 (95% CI 1.60–5.24). No significant differences were found in survival rate to hospital admission and survival rate to hospital discharge for ACD-CPR versus S-CRP with RR values of 1.06 (95% CI 0.76–1.60) and 1.00 (95% CI 0.73–1.38). CONCLUSION: Quality controlled studies confirmed the superiority of ACD-CPR to S-CRP in terms of ROSC rate and survival rate at 24 hours. Compared with S-CRP, ACD-CPR could not improve survival rate to hospital admission or survival rate to hospital discharge. PMID:25215130

Exploring Stage I non-small-cell lung cancer: development of a prognostic model predicting 5-year survival after surgical resection†.

PubMed

Guerrera, Francesco; Errico, Luca; Evangelista, Andrea; Filosso, Pier Luigi; Ruffini, Enrico; Lisi, Elena; Bora, Giulia; Asteggiano, Elena; Olivetti, Stefania; Lausi, Paolo; Ardissone, Francesco; Oliaro, Alberto

2015-06-01

Despite impressive results in diagnosis and treatment of non-small-cell lung cancer (NSCLC), more than 30% of patients with Stage I NSCLC die within 5 years after surgical treatment. Identification of prognostic factors to select patients with a poor prognosis and development of tailored treatment strategies are then advisable. The aim of our study was to design a model able to define prognosis in patients with Stage I NSCLC, submitted to surgery with curative intent. A retrospective analysis of two surgical registries was performed. Predictors of survival were investigated using the Cox model with shared frailty (accounting for the within-centre correlation). Candidate predictors were: age, gender, smoking habit, morbidity, previous malignancy, Eastern Cooperative Oncology Group performance status, clinical N stage, maximum standardized uptake value (SUV(max)), forced expiratory volume in 1 s, carbon monoxide lung diffusion capacity (DLCO), extent of surgical resection, systematic lymphadenectomy, vascular invasion, pathological T stage, histology and histological grading. The final model included predictors with P < 0.20, after a backward selection. Missing data in evaluated predictors were multiple-imputed and combined estimates were obtained from 10 imputed data sets. Analysis was performed on 848 consecutive patients. The median follow-up was 48 months. Two hundred and nine patients died (25%), with a 5-year overall survival (OS) rate of 74%. The final Cox model demonstrated that mortality was significantly associated with age, male sex, presence of cardiac comorbidities, DLCO (%), SUV(max), systematic nodal dissection, presence of microscopic vascular invasion, pTNM stage and histological grading. The final model showed a fair discrimination ability (C-statistic = 0.69): the calibration of the model indicated a good agreement between observed and predicted survival. We designed an effective prognostic model based on clinical, pathological and surgical covariates. Our preliminary results need to be refined and validated in a larger patient population, in order to provide an easy-to-use prognostic tool for Stage I NSCLC patients. © The Author 2014. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Modeling receptor kinetics in the analysis of survival data for organophosphorus pesticides.

PubMed

Jager, Tjalling; Kooijman, Sebastiaan A L M

2005-11-01

Acute ecotoxicological tests usually focus on survival at a standardized exposure time. However, LC50's decrease in time in a manner that depends both on the chemical and on the organism. DEBtox is an existing approach to analyze toxicity data in time, based on hazard modeling (the internal concentration increases the probability to die). However, certain chemicals elicit their response through (irreversible) interaction with a specific receptor, such as inhibition of acetylcholinesterase (AChE). Effects therefore do not solely depend on the actual internal concentration, but also on its (recent) past. In this paper, the DEBtox method is extended with a simple mechanistic model to deal with receptor interactions. We analyzed data from the literature for organophosphorus pesticides in guppies, fathead minnows, and springtails. Overall, the observed survival patterns do not clearly differ from those of chemicals with a less-specific mode of action. However, using the receptor model, resulting parameter estimates are easier to interpret in terms of underlying mechanisms and reveal similarities between the various pesticides. We observed thatthe no-effect concentration estimated from the receptor model is basically identical to the value from standard DEBtox, illustrating the robustness of this summary statistic.

Association of a Locus in the CAMTA1 Gene With Survival in Patients With Sporadic Amyotrophic Lateral Sclerosis.

PubMed

Fogh, Isabella; Lin, Kuang; Tiloca, Cinzia; Rooney, James; Gellera, Cinzia; Diekstra, Frank P; Ratti, Antonia; Shatunov, Aleksey; van Es, Michael A; Proitsi, Petroula; Jones, Ashley; Sproviero, William; Chiò, Adriano; McLaughlin, Russell Lewis; Sorarù, Gianni; Corrado, Lucia; Stahl, Daniel; Del Bo, Roberto; Cereda, Cristina; Castellotti, Barbara; Glass, Jonathan D; Newhouse, Steven; Dobson, Richard; Smith, Bradley N; Topp, Simon; van Rheenen, Wouter; Meininger, Vincent; Melki, Judith; Morrison, Karen E; Shaw, Pamela J; Leigh, P Nigel; Andersen, Peter M; Comi, Giacomo P; Ticozzi, Nicola; Mazzini, Letizia; D'Alfonso, Sandra; Traynor, Bryan J; Van Damme, Philip; Robberecht, Wim; Brown, Robert H; Landers, John E; Hardiman, Orla; Lewis, Cathryn M; van den Berg, Leonard H; Shaw, Christopher E; Veldink, Jan H; Silani, Vincenzo; Al-Chalabi, Ammar; Powell, John

2016-07-01

Amyotrophic lateral sclerosis (ALS) is a devastating adult-onset neurodegenerative disorder with a poor prognosis and a median survival of 3 years. However, a significant proportion of patients survive more than 10 years from symptom onset. To identify gene variants influencing survival in ALS. This genome-wide association study (GWAS) analyzed survival in data sets from several European countries and the United States that were collected by the Italian Consortium for the Genetics of ALS and the International Consortium on Amyotrophic Lateral Sclerosis Genetics. The study population included 4256 patients with ALS (3125 [73.4%] deceased) with genotype data extended to 7 174 392 variants by imputation analysis. Samples of DNA were collected from January 1, 1993, to December 31, 2009, and analyzed from March 1, 2014, to February 28, 2015. Cox proportional hazards regression under an additive model with adjustment for age at onset, sex, and the first 4 principal components of ancestry, followed by meta-analysis, were used to analyze data. Survival distributions for the most associated genetic variants were assessed by Kaplan-Meier analysis. Among the 4256 patients included in the analysis (2589 male [60.8%] and 1667 female [39.2%]; mean [SD] age at onset, 59 [12] years), the following 2 novel loci were significantly associated with ALS survival: at 10q23 (rs139550538; P = 1.87 × 10-9) and in the CAMTA1 gene at 1p36 (rs2412208, P = 3.53 × 10-8). At locus 10q23, the adjusted hazard ratio for patients with the rs139550538 AA or AT genotype was 1.61 (95% CI, 1.38-1.89; P = 1.87 × 10-9), corresponding to an 8-month reduction in survival compared with TT carriers. For rs2412208 CAMTA1, the adjusted hazard ratio for patients with the GG or GT genotype was 1.17 (95% CI, 1.11-1.24; P = 3.53 × 10-8), corresponding to a 4-month reduction in survival compared with TT carriers. This GWAS robustly identified 2 loci at genome-wide levels of significance that influence survival in patients with ALS. Because ALS is a rare disease and prevention is not feasible, treatment that modifies survival is the most realistic strategy. Therefore, identification of modifier genes that might influence ALS survival could improve the understanding of the biology of the disease and suggest biological targets for pharmaceutical intervention. In addition, genetic risk scores for survival could be used as an adjunct to clinical trials to account for the genetic contribution to survival.

Association of a Locus in the CAMTA1 Gene With Survival in Patients With Sporadic Amyotrophic Lateral Sclerosis

PubMed Central

Fogh, Isabella; Lin, Kuang; Tiloca, Cinzia; Rooney, James; Gellera, Cinzia; Diekstra, Frank P.; Ratti, Antonia; Shatunov, Aleksey; van Es, Michael A.; Proitsi, Petroula; Jones, Ashley; Sproviero, William; Chiò, Adriano; McLaughlin, Russell Lewis; Sorarù, Gianni; Corrado, Lucia; Stahl, Daniel; Bo, Roberto Del; Cereda, Cristina; Castellotti, Barbara; Glass, Jonathan D.; Newhouse, Steven; Dobson, Richard; Smith, Bradley N.; Topp, Simon; van Rheenen, Wouter; Meininger, Vincent; Melki, Judith; Morrison, Karen E.; Shaw, Pamela J.; Leigh, P. Nigel; Andersen, Peter M.; Comi, Giacomo P.; Ticozzi, Nicola; Mazzini, Letizia; D’Alfonso, Sandra; Traynor, Bryan J.; Van Damme, Philip; Robberecht, Wim; Brown, Robert H.; Landers, John E.; Hardiman, Orla; Lewis, Cathryn M.; van den Berg, Leonard H.; Shaw, Christopher E.; Veldink, Jan H.; Silani, Vincenzo; Al-Chalabi, Ammar; Powell, John

2017-01-01

IMPORTANCE Amyotrophic lateral sclerosis (ALS) is a devastating adult-onset neurodegenerative disorder with a poor prognosis and a median survival of 3 years. However, a significant proportion of patients survive more than 10 years from symptom onset. OBJECTIVE To identify gene variants influencing survival in ALS. DESIGN, SETTING, AND PARTICIPANTS This genome-wide association study (GWAS) analyzed survival in data sets from several European countries and the United States that were collected by the Italian Consortium for the Genetics of ALS and the International Consortium on Amyotrophic Lateral Sclerosis Genetics. The study population included 4256 patients with ALS (3125 [73.4%] deceased) with genotype data extended to 7 174 392 variants by imputation analysis. Samples of DNA were collected from January 1, 1993, to December 31, 2009, and analyzed from March 1, 2014, to February 28, 2015. MAIN OUTCOMES AND MEASURES Cox proportional hazards regression under an additive model with adjustment for age at onset, sex, and the first 4 principal components of ancestry, followed by meta-analysis, were used to analyze data. Survival distributions for the most associated genetic variants were assessed by Kaplan-Meier analysis. RESULTS Among the 4256 patients included in the analysis (2589 male [60.8%] and 1667 female [39.2%]; mean [SD] age at onset, 59 [12] years), the following 2 novel loci were significantly associated with ALS survival: at 10q23 (rs139550538; P = 1.87 × 10−9) and in the CAMTA1 gene at 1p36 (rs2412208, P = 3.53 × 10−8). At locus 10q23, the adjusted hazard ratio for patients with the rs139550538 AA or AT genotype was 1.61 (95% CI, 1.38–1.89; P = 1.87 × 10−9), corresponding to an 8-month reduction in survival compared with TT carriers. For rs2412208 CAMTA1, the adjusted hazard ratio for patients with the GG or GT genotype was 1.17 (95% CI, 1.11–1.24; P = 3.53 × 10−8), corresponding to a 4-month reduction in survival compared with TT carriers. CONCLUSIONS AND RELEVANCE This GWAS robustly identified 2 loci at genome-wide levels of significance that influence survival in patients with ALS. Because ALS is a rare disease and prevention is not feasible, treatment that modifies survival is the most realistic strategy. Therefore, identification of modifier genes that might influence ALS survival could improve the understanding of the biology of the disease and suggest biological targets for pharmaceutical intervention. In addition, genetic risk scores for survival could be used as an adjunct to clinical trials to account for the genetic contribution to survival. PMID:27244217

National Practice Patterns for Clinical T1N0 Nasopharyngeal Cancer in the Elderly: A National Cancer Data Base Analysis.

PubMed

Post, Carl M; Lin, Chi; Adeberg, Sebastian; Gupta, Mrigank; Zhen, Weining; Verma, Vivek

2018-03-01

The standard of care for T1N0 nasopharyngeal cancer (NPC) is definitive radiation therapy (RT). However, practice patterns in the elderly may not necessarily follow national guidelines. Herein, we investigated national practice patterns for T1N0 NPC. The National Cancer Data Base (NCDB) was queried for clinical T1N0 primary NPC cases (2004-2013) in patients ≥70 years old. Patient, tumor, and treatment parameters were extracted. Kaplan-Meier analysis was used to compare overall survival (OS) between patients receiving RT versus those under observation. Logistic regression was used to examine variables associated with receipt of RT. Cox proportional hazards modeling determined variables associated with OS. Landmark analysis of patients surviving 1 year or more was performed to assess survival differences between groups. In total, data of 147 patients were analyzed. RT was delivered to 89 patients (61%), whereas 58 (39%) patients underwent observation. On multivariable analysis, older patients were less likely to receive RT (p=0.003), but there were no differences between groups in terms of Charlson-Deyo comorbidity index. Median and 5-year OS in patients receiving RT versus those under observation were 71 and 33 months, and 59% and 48% (p=0.011), respectively. For patients surviving 1 year or more (n=96), there was a strong trend showing that receipt of RT was associated with better median and 5-year OS. This National Data Base analysis shows that observation is relatively common for T1N0 NPC in the elderly, but is associated with poorer survival. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Survival analysis for the missing censoring indicator model using kernel density estimation techniques

PubMed Central

Subramanian, Sundarraman

2008-01-01

This article concerns asymptotic theory for a new estimator of a survival function in the missing censoring indicator model of random censorship. Specifically, the large sample results for an inverse probability-of-non-missingness weighted estimator of the cumulative hazard function, so far not available, are derived, including an almost sure representation with rate for a remainder term, and uniform strong consistency with rate of convergence. The estimator is based on a kernel estimate for the conditional probability of non-missingness of the censoring indicator. Expressions for its bias and variance, in turn leading to an expression for the mean squared error as a function of the bandwidth, are also obtained. The corresponding estimator of the survival function, whose weak convergence is derived, is asymptotically efficient. A numerical study, comparing the performances of the proposed and two other currently existing efficient estimators, is presented. PMID:18953423

Survival analysis for the missing censoring indicator model using kernel density estimation techniques.

PubMed

Subramanian, Sundarraman

2006-01-01

This article concerns asymptotic theory for a new estimator of a survival function in the missing censoring indicator model of random censorship. Specifically, the large sample results for an inverse probability-of-non-missingness weighted estimator of the cumulative hazard function, so far not available, are derived, including an almost sure representation with rate for a remainder term, and uniform strong consistency with rate of convergence. The estimator is based on a kernel estimate for the conditional probability of non-missingness of the censoring indicator. Expressions for its bias and variance, in turn leading to an expression for the mean squared error as a function of the bandwidth, are also obtained. The corresponding estimator of the survival function, whose weak convergence is derived, is asymptotically efficient. A numerical study, comparing the performances of the proposed and two other currently existing efficient estimators, is presented.

An identifiable model for informative censoring

USGS Publications Warehouse

Link, W.A.; Wegman, E.J.; Gantz, D.T.; Miller, J.J.

1988-01-01

The usual model for censored survival analysis requires the assumption that censoring of observations arises only due to causes unrelated to the lifetime under consideration. It is easy to envision situations in which this assumption is unwarranted, and in which use of the Kaplan-Meier estimator and associated techniques will lead to unreliable analyses.

Is Intra-Aortic Balloon Pump Counterpulsation Sufficient to Treat Patients in Cardiogenic Shock, Undergoing Primary Percutaneous Coronary Intervention

PubMed Central

Paton, Maria; Ashton, Lisa; Pearson, Ian; Sivananthan, Mohan

2015-01-01

Background A high number of patients do not survive primary percutaneous coronary intervention (PCI) complicated by cardiogenic shock (CS), even when assisted with intra-aortic balloon pump (IABP) counterpulsation. There is no accepted consensus on who may most benefit from IABP counterpulsation, although previous retrospective studies have reported predictors of survival for patients undergoing PCI and cardiac surgery. To date, a risk model for emergency primary PCI patients has not been ascertained. The objective of this study was to identify independent predictors for in-hospital survival, to create a standardized risk model to predict patients who may require IABP insertion during primary PCI. Method Retrospective data were from 165 patients who had undergone primary PCI with IABP due to CS complicating acute myocardial infarction (AMI), from September 2007 to 2010, and underwent logistic regression analysis, to evaluate the incremental risk factors associated with survival. Results The overall in-hospital mortality was 32.1% (53 patients). The incremental independent predictors for in-hospital survival were: patient age of less than 60 years (OR: 0.303, 95% CI: 0.11 - 0.83, P < 0.02) and the use of IABP support alone, as opposed to in adjunction with inotropic support (OR: 3.177, 95% CI: 1.159 - 8.708, P < 0.025). Conclusion This study illustrated an age of less than 60 years, and the use of IABP alone, to be independent predictors of in-hospital survival in patients with CS complicating AMI who undergo primary PCI assisted by IABP. No specific risk model could be determined. PMID:28197255

Is Intra-Aortic Balloon Pump Counterpulsation Sufficient to Treat Patients in Cardiogenic Shock, Undergoing Primary Percutaneous Coronary Intervention.

PubMed

Paton, Maria; Ashton, Lisa; Pearson, Ian; Sivananthan, Mohan

2015-12-01

A high number of patients do not survive primary percutaneous coronary intervention (PCI) complicated by cardiogenic shock (CS), even when assisted with intra-aortic balloon pump (IABP) counterpulsation. There is no accepted consensus on who may most benefit from IABP counterpulsation, although previous retrospective studies have reported predictors of survival for patients undergoing PCI and cardiac surgery. To date, a risk model for emergency primary PCI patients has not been ascertained. The objective of this study was to identify independent predictors for in-hospital survival, to create a standardized risk model to predict patients who may require IABP insertion during primary PCI. Retrospective data were from 165 patients who had undergone primary PCI with IABP due to CS complicating acute myocardial infarction (AMI), from September 2007 to 2010, and underwent logistic regression analysis, to evaluate the incremental risk factors associated with survival. The overall in-hospital mortality was 32.1% (53 patients). The incremental independent predictors for in-hospital survival were: patient age of less than 60 years (OR: 0.303, 95% CI: 0.11 - 0.83, P < 0.02) and the use of IABP support alone, as opposed to in adjunction with inotropic support (OR: 3.177, 95% CI: 1.159 - 8.708, P < 0.025). This study illustrated an age of less than 60 years, and the use of IABP alone, to be independent predictors of in-hospital survival in patients with CS complicating AMI who undergo primary PCI assisted by IABP. No specific risk model could be determined.

Survival in Adult Lung Transplant Recipients Receiving Pediatric Versus Adult Donor Allografts.

PubMed

Hayes, Don; Whitson, Bryan A; Ghadiali, Samir N; Lloyd, Eric A; Tobias, Joseph D; Mansour, Heidi M; Black, Sylvester M

2015-10-01

Recent evidence showed that pediatric donor lungs increased rates of allograft failure in adult lung transplant recipients; however, the influence on survival is unclear. The United Network for Organ Sharing (UNOS) database was queried from 2005 to 2013 for adult lung transplant recipients (≥18 years) to assess survival differences among donor age categories (<18 years, 18 to 29 years, 30 to 59 years, ≥60 years). Of 12,297 adult lung transplants, 12,209 were used for univariate Cox models and Kaplan-Meier (KM) analysis and 11,602 for multivariate Cox models. A total of 1,187 adult recipients received pediatric donor lungs compared with 11,110 receiving adult donor organs. Univariate and multivariate Cox models found no difference in survival between donor ages 0 to 17 and donor ages 18 to 29, whereas donor ages 60 and older were significantly associated with increased mortality hazard, relative to the modal category of donor ages 30 to 59 (adjusted hazard ratio = 1.381; 95% confidence interval = 1.188% to 1.606%; p < 0.001). Interactions between recipient and donor age range found that the oldest donor age range was negatively associated with survival among middle-aged (30 to 59) and older (≥60) lung transplant recipients. Pediatric donor lung allografts were not negatively associated with survival in adult lung transplant recipients; however, the oldest donor age range was associated with increased mortality hazard for adult lung transplant recipients. Copyright © 2015 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Obesity adversely affects survival in pancreatic cancer patients.

PubMed

McWilliams, Robert R; Matsumoto, Martha E; Burch, Patrick A; Kim, George P; Halfdanarson, Thorvardur R; de Andrade, Mariza; Reid-Lombardo, Kaye; Bamlet, William R

2010-11-01

Higher body-mass index (BMI) has been implicated as a risk factor for developing pancreatic cancer, but its effect on survival has not been thoroughly investigated. The authors assessed the association of BMI with survival in a sample of pancreatic cancer patients and used epidemiologic and clinical information to understand the contribution of diabetes and hyperglycemia. A survival analysis using Cox proportional hazards by usual adult BMI was performed on 1861 unselected patients with pancreatic adenocarcinoma; analyses were adjusted for covariates that included clinical stage, age, and sex. Secondary analyses incorporated self-reported diabetes and fasting blood glucose in the survival model. BMI as a continuous variable was inversely associated with survival from pancreatic adenocarcinoma (hazard ratio [HR], 1.019 for each increased unit of BMI [kg/m2], P<.001) after adjustment for age, stage, and sex. In analysis by National Institutes of Health BMI category, BMIs of 30 to 34.99 kg/m2 (HR, 1.14; 95% confidence interval [CI], 0.98-1.33), 35 to 39.99 kg/m2 (HR 1.32, 95% CI 1.08-1.62), and ≥40 (HR 1.60, 95% CI 1.26-2.04) were associated with decreased survival compared with normal BMI of 18.5 to 24.99 kg/m2 (overall trend test P<.001). Fasting blood glucose and diabetes did not affect the results. Higher BMI is associated with decreased survival in pancreatic cancer. Although the mechanism of this association remains undetermined, diabetes and hyperglycemia do not appear to account for the observed association. Copyright © 2010 American Cancer Society.

Analysis of individual- and time-specific covariate effects on survival of Serinus serinus in north-eastern Spain

USGS Publications Warehouse

Conroy, M.J.; Senar, J.C.; Domenech, J.

2002-01-01

We developed models for the analysis of recapture data for 2678 serins (Serinus serinus) ringed in north-eastern Spain since 1985. We investigated several time- and individual-specific factors as potential predictors of overall mortality and dispersal patterns, and of gender and age differences in these patterns. Time-specific covariates included minimum daily temperature, days below freezing, and abundance of a strong competitor, siskins (Carduelis spinus) during winter, and maximum temperature and rainfall during summer. Individual covariates included body mass (i.e. body condition), and wing length (i.e. flying ability), and interactions between body mass and environmental factors. We found little support of a predictive relationship between environmental factors and survival, but good evidence of relationships between body mass and survival, especially for juveniles. Juvenile survival appears to vary in a curvilinear manner with increasing mass, suggesting that there may exist an optimal mass beyond which increases are detrimental. The mass-survival relationship does seem to be influenced by at least one environmental factor, namely the abundance of wintering siskins. When siskins are abundant, increases in body mass appear to relate strongly to increasing survival. When siskin numbers are average or low the relationship is largely reversed, suggesting that the presence of strong competition mitigates the otherwise largely negative aspects of greater body mass. Wing length in juveniles also appears to be related positively to survival, perhaps largely due to the influence of a few unusually large juveniles with adult-like survival. Further work is needed to test these relationships, ideally under experimentation.

[Survival following the first admission in an integrated dual disorders treatment ward (IDDTW): preliminary results from a cohort study].

PubMed

Gimelfarb, Yuri; Becatel, Ety; Wolf, Aviva; Baruch, Yehuda

2014-01-01

Dual disorders (co-occurring severe mental illness [SMI] and substance abuse disorders in the same person) are extremely common among patients receiving mental health services. Dual disorders are associated with increased all-cause mortality, as compared with patients with SMI. Scientific evidence is lacking on the survival of dual disorders subjects, who had psychiatric inpatient care. To determine the long term survival rates of patients after the first admission in an IDDTW and to identify their baseline predictors. The charts of 258 subjects admitted to IDDTW during the period 2002-2004 were assessed at least 8 years after the first admission. Psychiatric diagnoses were established and grouped according to the International Statistical Classification of Diseases and Related Health Problems 10th edition (ICD-10). The Kaplan-Meier survival analysis was used to estimate the cumulative survival rates, and the predictive values of different variables were assessed by Cox proportional-hazards regression model. The cumulative 1-, 2-, 4-, 6- and 8-year survival rates of all subjects were 98.06%, 96.51%, 91.47, 86.43% and 81.78%, respectively, without statistically significant differences between subgroups of psychiatric diagnoses. Multivariate Cox regression analysis revealed that the age at death was the only independent predictor of all-cause mortality (hazard ratio = .96; 95% confidence interval .93 to .99; p < .009). Those of young age are at a particularly low risk of long term survival. More targeted health care is required to address the specific needs of this vulnerable subgroup. Further research of survival into specific risk groups is required.

Cardiovascular Risk Reduction is Important for Improving Patient and Graft Survival After Ligation and Bypass Surgery for Popliteal Artery Aneurysm.

PubMed

Dattani, N; Ali, M; Aber, A; Kannan, R Yap; Choke, E C; Bown, M J; Sayers, R D; Davies, R S

2017-07-01

To report outcomes following ligation and bypass (LGB) surgery for popliteal artery aneurysm (PAA) and study factors influencing patient and graft survival. A retrospective review of patients undergoing LGB surgery for PAA between September 1999 and August 2012 at a tertiary referral vascular unit was performed. Primary graft patency (PGP), primary-assisted graft patency (PAGP), and secondary graft patency (SGP) rates were calculated using survival analyses. Patient, graft aneurysm-free survival (GAFS), aneurysm reperfusion-free survival (ARFS), and amputation-free survival (AFS) rates were also calculated. Log-rank testing and Cox proportional hazards modeling were used to perform univariate and multivariate analysis of influencing factors, respectively. Eighty-four LGB repairs in 69 patients (mean age 71.3 years, 68 males) were available for study. The 5-year PGP, PAGP, SGP, and patient survival rates were 58.1%, 84.4%, 85.2%, and 81.1%, respectively. On multivariate analysis, the principal determinants of PGP were urgency of operation ( P = .009) and smoking status ( P = .019). The principal determinants of PAGP were hyperlipidemia status ( P = .048) and of SGP were hyperlipidemia ( P = .042) and cerebrovascular disease (CVD) status ( P = .045). The principal determinants of patient survival were previous myocardial infarction ( P = .004) and CVD ( P = .001). The 5-year GAFS, ARFS, and AFS rates were 87.9%, 91.6%, and 96.1%, respectively. This study has shown that traditional cardiovascular risk factors, such as a smoking and ischemic heart disease, are the most important predictors of early graft failure and patient death following LGB surgery for PAA.

Trends in Testicular Cancer Survival: A Large Population-based Analysis.

PubMed

Sui, Wilson; Morrow, David C; Bermejo, Carlos E; Hellenthal, Nicholas J

2015-06-01

To determine whether discrepancies in testicular cancer outcomes between Caucasians and non-Caucasians are changing over time. Although testicular cancer is more common in Caucasians, studies have shown that other races have worse outcomes. Using the Surveillance, Epidemiology, and End Results registry, we identified 29,803 patients diagnosed with histologically confirmed testicular cancer between 1983 and 2011. Of these, 12,650 patients (42%) had 10-year follow-up data. We stratified the patients by age group, stage, race, and year of diagnosis and assessed 10-year overall and cancer-specific survival in each cohort. Cox proportional hazard models were used to determine the relative contributions of each stratum to cancer-specific survival. Predicted overall 10-year survival of Caucasian patients with testicular cancer increased slightly from 88% to 89% over the period studied, whereas predicted cancer-specific 10-year survival dropped slightly from 94% to 93%. In contrast, non-Caucasian men demonstrated larger changes in 10-year overall (84%-86%) and cancer-specific (88%-91%) survival. On univariate analysis, race was significantly associated with testicular cancer death, with non-Caucasian men being 1.69 times more likely to die of testicular cancer than Caucasians (hazard ratio, 1.33-2.16; 95% confidence interval, <.001). Historically, non-Caucasian race has been associated with poorer outcomes from testicular cancer. These data show a convergence in cancer-specific survival between racial groups over time, suggesting that diagnostic and treatment discrepancies may be improving for non-Caucasians. Copyright © 2015 Elsevier Inc. All rights reserved.

Early prognostic value of an Algorithm based on spectral Variables of Ventricular fibrillAtion from the EKG of patients with suddEn cardiac death: A multicentre observational study (AWAKE).

PubMed

Palacios-Rubio, Julián; Marina-Breysse, Manuel; Quintanilla, Jorge G; Gil-Perdomo, José Miguel; Juárez-Fernández, Miriam; Garcia-Gonzalez, Inés; Rial-Bastón, Verónica; Corcobado, María Carmen; Espinosa, María Carmen; Ruiz, Francisco; Gómez-Mascaraque Pérez, Francisco; Bringas-Bollada, María; Lillo-Castellano, José María; Pérez-Castellano, Nicasio; Martínez-Sellés, Manuel; López de Sá, Esteban; Martín-Benítez, Juan Carlos; Perez-Villacastín, Julián; Filgueiras-Rama, David

2018-06-06

Ventricular fibrillation (VF)-related sudden cardiac death (SCD) is a leading cause of mortality and morbidity. Current biological and imaging parameters show significant limitations on predicting cerebral performance at hospital admission. The AWAKE study (NCT03248557) is a multicentre observational study to validate a model based on spectral ECG analysis to early predict cerebral performance and survival in resuscitated comatose survivors. Data from VF ECG tracings of patients resuscitated from SCD will be collected using an electronic Case Report Form. Patients can be either comatose (Glasgow Coma Scale - GCS - ≤8) survivors undergoing temperature control after return of spontaneous circulation (RoSC), or those who regain consciousness (GCS=15) after RoSC; all admitted to Intensive Cardiac Care Units in 4 major university hospitals. VF tracings prior to the first direct current shock will be digitized and analyzed to derive spectral data and feed a predictive model to estimate favorable neurological performance (FNP). The results of the model will be compared to the actual prognosis. The primary clinical outcome is FNP during hospitalization. Patients will be categorized into 4 subsets of neurological prognosis according to the risk score obtained from the predictive model. The secondary clinical outcomes are survival to hospital discharge, and FNP and survival after 6 months of follow-up. The model-derived categorisation will be also compared with clinical variables to assess model sensitivity, specificity, and accuracy. A model based on spectral analysis of VF tracings is a promising tool to obtain early prognostic data after SCD. Copyright © 2018 Instituto Nacional de Cardiología Ignacio Chávez. Publicado por Masson Doyma México S.A. All rights reserved.

Prognostic factors in multiple myeloma: definition of risk groups in 410 previously untreated patients: a Grupo Argentino de Tratamiento de la Leucemia Aguda study.

PubMed

Corrado, C; Santarelli, M T; Pavlovsky, S; Pizzolato, M

1989-12-01

Four hundred ten previously untreated multiple myeloma patients entered onto two consecutive Grupo Argentino de Tratamiento de la Leucemia Aguda (GATLA) protocols were analyzed to identify significant prognostic factors influencing survival. The univariate analysis selected the following variables: performance status, renal function, percentage of bone marrow plasma cells at diagnosis, hemoglobin, and age. A multivariate analysis showed that performance status, renal function, percentage of bone marrow plasma cells, hemoglobin, and age were the best predictive variables for survival. A score was assigned to each patient according to these variables, which led to their classification in three groups: good, intermediate, and poor risk, with a probability of survival of 26% and 10% at 96 months, and 5% at 56 months, and median survival of 60, 37, and 14 months, respectively (P = .0000). In our patient population, this model proved to be superior to the Durie-Salmon staging system in defining prognostic risk groups, and separating patients with significantly different risks within each Durie-Salmon stage.

RAS mutations predict radiologic and pathologic response in patients treated with chemotherapy before resection of colorectal liver metastases.

PubMed

Zimmitti, Giuseppe; Shindoh, Junichi; Mise, Yoshihiro; Kopetz, Scott; Loyer, Evelyne M; Andreou, Andreas; Cooper, Amanda B; Kaur, Harmeet; Aloia, Thomas A; Maru, Dipen M; Vauthey, Jean-Nicolas

2015-03-01

RAS mutations have been reported to be a potential prognostic factor in patients with colorectal liver metastases (CLM). However, the impact of RAS mutations on response to chemotherapy remains unclear. The purpose of this study was to investigate the correlation between RAS mutations and response to preoperative chemotherapy and their impact on survival in patients undergoing curative resection of CLM. RAS mutational status was assessed and its relation to morphologic response and pathologic response was investigated in 184 patients meeting inclusion criteria. Predictors of survival were assessed. The prognostic impact of RAS mutational status was then analyzed using two different multivariate models, including either radiologic morphologic response (model 1) or pathologic response (model 2). Optimal morphologic response and major pathologic response were more common in patients with wild-type RAS (32.9 and 58.9%, respectively) than in patients with RAS mutations (10.5 and 36.8%; P = 0.006 and 0.015, respectively). Multivariate analysis confirmed that wild-type RAS was a strong predictor of optimal morphologic response [odds ratio (OR), 4.38; 95% CI 1.45-13.15] and major pathologic response (OR, 2.61; 95% CI 1.17-5.80). RAS mutations were independently correlated with both overall survival and recurrence free-survival (hazard ratios, 3.57 and 2.30, respectively, in model 1, and 3.19 and 2.09, respectively, in model 2). Subanalysis revealed that RAS mutational status clearly stratified survival in patients with inadequate response to preoperative chemotherapy. RAS mutational status can be used to complement the current prognostic indicators for patients undergoing curative resection of CLM after preoperative modern chemotherapy.

Evaluating the predictive accuracy and the clinical benefit of a nomogram aimed to predict survival in node-positive prostate cancer patients: External validation on a multi-institutional database.

PubMed

Bianchi, Lorenzo; Schiavina, Riccardo; Borghesi, Marco; Bianchi, Federico Mineo; Briganti, Alberto; Carini, Marco; Terrone, Carlo; Mottrie, Alex; Gacci, Mauro; Gontero, Paolo; Imbimbo, Ciro; Marchioro, Giansilvio; Milanese, Giulio; Mirone, Vincenzo; Montorsi, Francesco; Morgia, Giuseppe; Novara, Giacomo; Porreca, Angelo; Volpe, Alessandro; Brunocilla, Eugenio

2018-04-06

To assess the predictive accuracy and the clinical value of a recent nomogram predicting cancer-specific mortality-free survival after surgery in pN1 prostate cancer patients through an external validation. We evaluated 518 prostate cancer patients treated with radical prostatectomy and pelvic lymph node dissection with evidence of nodal metastases at final pathology, at 10 tertiary centers. External validation was carried out using regression coefficients of the previously published nomogram. The performance characteristics of the model were assessed by quantifying predictive accuracy, according to the area under the curve in the receiver operating characteristic curve and model calibration. Furthermore, we systematically analyzed the specificity, sensitivity, positive predictive value and negative predictive value for each nomogram-derived probability cut-off. Finally, we implemented decision curve analysis, in order to quantify the nomogram's clinical value in routine practice. External validation showed inferior predictive accuracy as referred to in the internal validation (65.8% vs 83.3%, respectively). The discrimination (area under the curve) of the multivariable model was 66.7% (95% CI 60.1-73.0%) by testing with receiver operating characteristic curve analysis. The calibration plot showed an overestimation throughout the range of predicted cancer-specific mortality-free survival rates probabilities. However, in decision curve analysis, the nomogram's use showed a net benefit when compared with the scenarios of treating all patients or none. In an external setting, the nomogram showed inferior predictive accuracy and suboptimal calibration characteristics as compared to that reported in the original population. However, decision curve analysis showed a clinical net benefit, suggesting a clinical implication to correctly manage pN1 prostate cancer patients after surgery. © 2018 The Japanese Urological Association.

Association of family history and survival in patients with colorectal cancer: a pooled analysis of eight epidemiologic studies.

PubMed

Chong, Dawn Q; Banbury, Barbara L; Phipps, Amanda I; Hua, Xinwei; Kocarnik, Jonathan; Peters, Ulrike; Berndt, Sonja I; Huang, Wen-Yi; Potter, John D; Slattery, Martha L; White, Emily; Campbell, Peter T; Harrison, Tabitha; Newcomb, Polly A; Chan, Andrew T

2018-05-01

A family history of colorectal cancer (CRC) in first-degree relatives (FDRs) increases the risk of CRC. However, the influence of family history on survival among CRC patients remains unclear. We conducted a pooled analysis of survival in 5010 incident CRC cases. Cox proportional hazards models were used to estimate the association of family history with overall survival (OS) and CRC-specific survival (CSS). We also assessed the impact of the number of affected FDRs and age at CRC diagnosis in the affected FDRs on survival. Among CRC cases, 819 (16%) patients reported a family history of CRC. There were 1580 total deaths over a median follow-up of 4.6 years, of which 1046 (66%) deaths were due to CRC. Having a family history of CRC was not associated with OS [hazard ratio (HR), 1.03; 95% confidence interval (CI), 0.89-1.19] or CSS (HR, 1.13; 95% CI, 0.95-1.36)]. There were no associations between the number of affected relatives or age at CRC diagnosis of the affected relative with survival (all P trend  > 0.05). However, a family history of CRC did confer worse CSS in patients diagnosed with distal colon cancer (HR, 1.45, 95% CI, 1.03-2.04). A family history of CRC was generally not associated with survival after CRC diagnosis. However, having a family history of CRC was associated with worse CRC prognosis in individuals with distal colon cancer, suggesting a possible genetic predisposition with distinct pathogenic mechanism that may lead to worse survival in this group. © 2018 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Characterize Human Forward Contamination Project

NASA Technical Reports Server (NTRS)

Rucker, Michelle

2015-01-01

Let's face it: wherever we go, we will inevitably carry along the little critters that live in and on us. Conventional wisdom has long held that it's unlikely those critters could survive the space environment, but in 2007 microscopic animals called Tardigrades survived exposure to space and in 2008 Cyanobacteria lived for 548 days outside the International Space Station (ISS). But what about the organisms we might reasonably expect a crewed spacecraft to leak or vent? Do we even know what they are? How long might our tiny hitch-hikers survive in close proximity to a warm spacecraft that periodically leaks/vents water or oxygen-and how might they mutate with long-duration exposure? Unlike the Mars rovers that we cleaned once and sent on their way, crew members will provide a constantly regenerating contaminant source. Are we prepared to certify that we can meet forward contamination protocols as we search for life at new destinations? This project has four technical objectives: 1. TEST: Develop a test plan to leverage existing equipment (i.e. ISS) to characterize the kinds of organisms we can reasonably expect pressurized, crewed volumes to vent or leak overboard; 2. ANALYSIS: Develop an analysis plan to study those organisms in relevant destination environments, including spacecraft-induced conditions; 3. MODEL: Develop a modeling plan to model organism transport mechanisms in relevant destination environments; 4. SHARE: Develop a plan to disseminate findings and integrate recommendations into exploration requirements & ops. In short, we propose a system engineering approach to roadmap the necessary experiments, analysis, and modeling up front--rather than try to knit together disparate chunks of data into a sensible conclusion after the fact.

[Essential thrombocythemia: baseline characteristics and risk factors for survival and thrombosis in a series of 214 patients].

PubMed

Angona, Anna; Alvarez-Larrán, Alberto; Bellosillo, Beatriz; Martínez-Avilés, Luz; Garcia-Pallarols, Francesc; Longarón, Raquel; Ancochea, Àgueda; Besses, Carles

2015-03-15

Two prognostic models to predict overall survival and thrombosis-free survival have been proposed: International Prognostic Score for Essential Thrombocythemia (IPSET) and IPSET-Thrombosis, respectively, based on age, leukocytes count, history of previous thrombosis, the presence of cardiovascular risk factors and the JAK2 mutational status. The aim of the present study was to assess the clinical and biological characteristics at diagnosis and during evolution in essential thrombocythemia (ET) patients as well as the factors associated with survival and thrombosis and the usefulness of these new prognostic models. We have evaluated the clinical data and the mutation status of JAK2, MPL and calreticulin of 214 ET patients diagnosed in a single center between 1985 and 2012, classified according to classical risk stratification, IPSET and IPSET-Thrombosis. With a median follow-up of 6.9 years, overall survival was not associated with any variable by multivariate analysis. Thrombotic history and leukocytes>10×10(9)/l were associated with thrombosis-free survival (TFS). In our series, IPSET prognostic systems of survival and thrombosis did not provide more clinically relevant information regarding the classic risk of thrombosis stratification. Thrombotic history and leukocytosis>10×10(9)/l were significantly associated with lower TFS, while the prognostic IPSET-Thrombosis system did not provide more information than classical thrombotic risk assessment. Copyright © 2014 Elsevier España, S.L.U. All rights reserved.

Ambulation and survival following surgery in elderly patients with metastatic epidural spinal cord compression.

PubMed

Itshayek, Eyal; Candanedo, Carlos; Fraifeld, Shifra; Hasharoni, Amir; Kaplan, Leon; Schroeder, Josh E; Cohen, José E

2018-07-01

Metastatic epidural spinal cord compression (MESCC) is a disabling consequence of disease progression. Surgery can restore or preserve physical function, improving access to treatments that increase duration of survival; however, advanced patient age may deter oncologists and surgeons from considering surgical management. Evaluate the duration of ambulation and survival in elderly patients following surgical decompression of MESCC. Retrospective file review of a prospective database, under institutional review board (IRB) waiver of informed consent, of consecutive patients treated in an academic tertiary care medical center from August 2008 to March 2015. Patients ≥65 years presenting neurological and/or radiological signs of cord compression because of metastatic disease, who underwent surgical decompression. Duration of ambulation and survival. Patients underwent urgent multidisciplinary evaluation and surgery. Ambulation and survival were compared with age, pre-, and postoperative neurological (American Spinal Injury Association [ASIA] Impairment Scale [AIS]) and performance status (Karnofsky Performance Status [KPS]), and Tokuhashi Score using Kruskal-Wallis and Wilcoxon signed rank tests, Pearson correlation coefficient, Cox regression model, log-rank analysis, and Kaplan-Meier analysis. Forty patients were included (21 male, 54%; mean age 74 years, range 65-87). Surgery was performed a mean 3.8 days after onset of motor symptoms. Mean duration of ambulation and survival were 474 (range 0-1662) and 525 days (range 11-1662), respectively; 53% of patients (21 of 40) survived and 43% (17 of 40) retained ambulation for ≥1 year. There was no significant relationship between survival and ambulation for patients aged 65-69, 70-79, or 80-89 years, although Kaplan-Meier analysis suggested stratification. There was a significant relationship between duration of ambulation and pre- and postoperative AIS (p=.0342, p=.0358, respectively) and postoperative KPS (p=.0221). Tokuhashi score was not significantly related to duration of survival or ambulation, and greatly underestimated life expectancy in 22 of 37 (59%) patients with scores 0-11. Decompressive surgery led to marked improvement in neurological function and performance status. More than 50% of patients survived for >1 year, some for 3 years or more after surgery. Copyright © 2018 Elsevier Inc. All rights reserved.

Simple prognostic model for patients with advanced cancer based on performance status.

PubMed

Jang, Raymond W; Caraiscos, Valerie B; Swami, Nadia; Banerjee, Subrata; Mak, Ernie; Kaya, Ebru; Rodin, Gary; Bryson, John; Ridley, Julia Z; Le, Lisa W; Zimmermann, Camilla

2014-09-01

Providing survival estimates is important for decision making in oncology care. The purpose of this study was to provide survival estimates for outpatients with advanced cancer, using the Eastern Cooperative Oncology Group (ECOG), Palliative Performance Scale (PPS), and Karnofsky Performance Status (KPS) scales, and to compare their ability to predict survival. ECOG, PPS, and KPS were completed by physicians for each new patient attending the Princess Margaret Cancer Centre outpatient Oncology Palliative Care Clinic (OPCC) from April 2007 to February 2010. Survival analysis was performed using the Kaplan-Meier method. The log-rank test for trend was employed to test for differences in survival curves for each level of performance status (PS), and the concordance index (C-statistic) was used to test the predictive discriminatory ability of each PS measure. Measures were completed for 1,655 patients. PS delineated survival well for all three scales according to the log-rank test for trend (P < .001). Survival was approximately halved for each worsening performance level. Median survival times, in days, for each ECOG level were: EGOG 0, 293; ECOG 1, 197; ECOG 2, 104; ECOG 3, 55; and ECOG 4, 25.5. Median survival times, in days, for PPS (and KPS) were: PPS/KPS 80-100, 221 (215); PPS/KPS 60 to 70, 115 (119); PPS/KPS 40 to 50, 51 (49); PPS/KPS 10 to 30, 22 (29). The C-statistic was similar for all three scales and ranged from 0.63 to 0.64. We present a simple tool that uses PS alone to prognosticate in advanced cancer, and has similar discriminatory ability to more complex models. Copyright © 2014 by American Society of Clinical Oncology.

Comparison of clinical and survival characteristics between prostate cancer patients of PSA-based screening and clinical diagnosis in China.

PubMed

Xu, Libo; Wang, Jinguo; Guo, Baofeng; Zhang, Haixia; Wang, Kaichen; Wang, Ding; Dai, Chang; Zhang, Ling; Zhao, Xuejian

2018-01-02

Prostate-specific antigen (PSA)-based mass screening remains the most controversial topic in prostate cancer. PSA-based mass screening has not been widely used in China yet. The aim of our study was to evaluate the effect of the PSA-based screening in China. The cohort consisted of 1,012 prostate cancer patients. Data were retrospectively collected and clinical characteristics of the cohorts were investigated. Survival was analyzed for prostatic carcinoma of both PSA screened and clinically diagnosed patients according to clinical characteristics and the National Comprehensive Cancer Network (NCCN) risk classification. Cox Proportional Hazards Model analysis was done for risk predictor identification. The median age was 71 years old. Five-year overall and prostate-cancer-specific survival in prostatic adenocarcinoma patients were 77.52% and 79.65%; 10-year survivals were 62.57% and 68.60%, respectively. Survival was significantly poorer in patients with metastases and non-curative management. T staging and Gleason score by NCCN classification effectively stratified prostatic adenocarcinoma patients into different risk groups. T staging was a significant predictor of survival by COX Proportional Hazard Model. PSA screened patients had a significantly higher percentage diagnosed in early stage. PSA screened prostatic adenocarcinoma patients had a better prognosis in both overall and prostate cancer-specific survivals. This Chinese cohort had a lower overall and prostate cancer survival rate than it is reported in western countries. The incidence of early-stage prostate cancer found in PSA-based mass screening was high and there were significant differences in both overall and prostate cancer-specific survival between the PSA-screened and clinically diagnosed patients.

Comparison of clinical and survival characteristics between prostate cancer patients of PSA-based screening and clinical diagnosis in China

PubMed Central

Xu, Libo; Wang, Jinguo; Guo, Baofeng; Zhang, Haixia; Wang, Kaichen; Wang, Ding; Dai, Chang; Zhang, Ling; Zhao, Xuejian

2018-01-01

Prostate-specific antigen (PSA)-based mass screening remains the most controversial topic in prostate cancer. PSA-based mass screening has not been widely used in China yet. The aim of our study was to evaluate the effect of the PSA-based screening in China. The cohort consisted of 1,012 prostate cancer patients. Data were retrospectively collected and clinical characteristics of the cohorts were investigated. Survival was analyzed for prostatic carcinoma of both PSA screened and clinically diagnosed patients according to clinical characteristics and the National Comprehensive Cancer Network (NCCN) risk classification. Cox Proportional Hazards Model analysis was done for risk predictor identification. The median age was 71 years old. Five-year overall and prostate-cancer-specific survival in prostatic adenocarcinoma patients were 77.52% and 79.65%; 10-year survivals were 62.57% and 68.60%, respectively. Survival was significantly poorer in patients with metastases and non-curative management. T staging and Gleason score by NCCN classification effectively stratified prostatic adenocarcinoma patients into different risk groups. T staging was a significant predictor of survival by COX Proportional Hazard Model. PSA screened patients had a significantly higher percentage diagnosed in early stage. PSA screened prostatic adenocarcinoma patients had a better prognosis in both overall and prostate cancer-specific survivals. This Chinese cohort had a lower overall and prostate cancer survival rate than it is reported in western countries. The incidence of early-stage prostate cancer found in PSA-based mass screening was high and there were significant differences in both overall and prostate cancer-specific survival between the PSA-screened and clinically diagnosed patients. PMID:29416625

Ethnicity and health care in cervical cancer survival: comparisons between a Filipino resident population, Filipino-Americans, and Caucasians.

PubMed

Redaniel, Maria Theresa; Laudico, Adriano; Mirasol-Lumague, Maria Rica; Gondos, Adam; Uy, Gemma Leonora; Toral, Jean Ann; Benavides, Doris; Brenner, Hermann

2009-08-01

Few studies have assessed and compared cervical cancer survival between developed and developing countries, or between ethnic groups within a country. Fewer still have addressed how much of the international or interracial survival differences can be attributed to ethnicity or health care. To determine the role of ethnicity and health care, 5-year survival of patients with cervical cancer was compared between patients in the Philippines and Filipino-Americans, who have the same ethnicity, and between Filipino-Americans and Caucasians, who have the same health care system. Cervical cancer databases from the Manila and Rizal Cancer Registries and Surveillance, Epidemiology, and End Results 13 were used. Age-adjusted 5-year survival estimates were computed and compared between the three patient groups. Using Cox proportional hazards modeling, potential determinants of survival differences were examined. Overall 5-year relative survival was similar in Filipino-Americans (68.8%) and Caucasians (66.6%), but was lower for Philippine residents (42.9%). Although late stage at diagnosis explained a large proportion of the survival differences between Philippine residents and Filipino-Americans, excess mortality prevailed after adjustment for stage, age, and morphology in multivariate analysis [relative risk (RR), 2.07; 95% confidence interval (CI), 1.68-2.55]. Excess mortality decreased, but persisted, when treatments were included in the multivariate models (RR, 1.78; 95% CI, 1.41-2.23). A moderate, marginally significant excess mortality was found among Caucasians compared with Filipino-Americans (adjusted RR, 1.22; 95% CI, 1.01-1.47). The differences in cervical cancer survival between patients in the Philippines and in the United States highlight the importance of enhanced health care and access to diagnostic and treatment facilities in the Philippines.

Clinical Outcomes of Specific Immunotherapy in Advanced Pancreatic Cancer: A Systematic Review and Meta-Analysis

PubMed Central

Qi, Xing-Shun

2017-01-01

Specific immunotherapies, including vaccines with autologous tumor cells and tumor antigen-specific monoclonal antibodies, are important treatments for PC patients. To evaluate the clinical outcomes of PC-specific immunotherapy, we performed a systematic review and meta-analysis of the relevant published clinical trials. The effects of specific immunotherapy were compared with those of nonspecific immunotherapy and the meta-analysis was executed with results regarding the overall survival (OS), immune responses data, and serum cancer markers data. The pooled analysis was performed by using the random-effects model. We found that significantly improved OS was noted for PC patients utilizing specific immunotherapy and an improved immune response was also observed. In conclusion, specific immunotherapy was superior in prolonging the survival time and enhancing immunological responses in PC patients. PMID:28265583

Survival analysis and prognostic indicators of systemic lupus erythematosus in Pakistani patients.

PubMed

Rabbani, Malik Anas; Habib, H B; Islam, M; Ahmad, B; Majid, S; Saeed, W; Shah, S M A; Ahmad, A

2009-08-01

To aim of this study is to analyse the survival rate and prognostic indicators of systemic lupus erythematosus (SLE) in Pakistani population. A total of 198 patients with SLE diagnosed between 1992 and 2005 were reviewed retrospectively. Clinical features at presentation, subsequent evolving features, autoantibody profile, damage scores and mortality data were obtained. Prognostic factors for survival were studied by statistical analysis. Of 198 SLE patients studied, 174 were women and 24 were men. The women to men ratio was 7.2:1. Mean age at presentation was 31 years (range 14-76). Mean duration of symptoms before diagnosis was 2.8 years. Mean duration of follow-up was 34.21 months (+/-33.69). Mean disease duration was 15.6 years. At diagnosis, arthritis, malar rash, oral ulcers and alopecia were the commonest features. During the follow-up, the prevalence of nephritis, arthritis, neurological and hematological disease increased significantly. About 76% (n = 151) of the patients had organ damage at the time of data analysis, and renal disease was the commonest cause. Univariate analysis revealed that renal disease (P = 0.000), seizures (P = 0.048), pleural involvement (P = 0.019), alopecia (P = 0.000) and discoid lesions (P = 0.005) were predictors for damage. Multivariate model, however, revealed that only renal disease was independent risk factor for damage (P = 0.002). During the study period, 47 patients (24%) died (five due to disease-related complications and rest as a result of infections). The 3-, 5-, 10-, 15- and 20-year survival rates of our cohort were 99, 80, 77, 75 and 75%, respectively. Cox regression analysis revealed that renal involvement (P = 0.002) and infections (P = 0.004) were independent risk factors for mortality. The survival of our Pakistani SLE patients was significantly lower compared to that of the Caucasian series reported in last decade. Nephritis not only contributes to organ damage but also acts a major determinant for survival. Infection remains the commonest cause of death. Renal involvement and infections are independent risk factors for mortality. Judicious use of immunosuppressive agents is necessary to improve the short-term survival of lupus patients.

Influence of habitat and intrinsic characteristics on survival of neonatal pronghorn

USGS Publications Warehouse

Jacques, Christopher N.; Jenks, Jonathan A.; Grovenburg, Troy W.; Klaver, Robert W.

2015-01-01

Increased understanding of the influence of habitat (e.g., composition, patch size) and intrinsic (e.g., age, birth mass) factors on survival of neonatal pronghorn (Antilocapra americana) is a prerequisite to successful management programs, particularly as they relate to population dynamics and the role of population models in adaptive species management. Nevertheless, few studies have presented empirical data quantifying the influence of habitat variables on survival of neonatal pronghorn. During 2002–2005, we captured and radiocollared 116 neonates across two sites in western South Dakota. We documented 31 deaths during our study, of which coyote (Canis latrans) predation (n = 15) was the leading cause of mortality. We used known fate analysis in Program MARK to investigate the influence of intrinsic and habitat variables on neonatal survival. We generated a priori models that we grouped into habitat and intrinsic effects. The highest-ranking model indicated that neonate mortality was best explained by site, percent grassland, and open water habitat; 90-day survival (0.80; 90% CI = 0.71–0.88) declined 23% when grassland and water increased from 80.1 to 92.3% and 0.36 to 0.40%, respectively, across 50% natal home ranges. Further, our results indicated that grassland patch size and shrub density were important predictors of neonate survival; neonate survival declined 17% when shrub density declined from 5.0 to 2.5 patches per 100 ha. Excluding the site covariates, intrinsic factors (i.e., sex, age, birth mass, year, parturition date) were not important predictors of survival of neonatal pronghorns. Further, neonatal survival may depend on available land cover and interspersion of habitats. We have demonstrated that maintaining minimum and maximum thresholds for habitat factors (e.g., percentages of grassland and open water patches, density of shrub patches) throughout natal home ranges will in turn, ensure relatively high (>0.50) neonatal survival rates, especially as they relate to coyote predation. Thus, landscape level variables (particularly percentages of open water, grassland habitats, and shrub density) should be incorporated into the development or implementation of pronghorn management plans across sagebrush steppe communities of the western Dakotas, and potentially elsewhere within the geographic range of pronghorn.

Sensory cortex hyperexcitability predicts short survival in amyotrophic lateral sclerosis.

PubMed

Shimizu, Toshio; Bokuda, Kota; Kimura, Hideki; Kamiyama, Tsutomu; Nakayama, Yuki; Kawata, Akihiro; Isozaki, Eiji; Ugawa, Yoshikazu

2018-05-01

To investigate somatosensory cortex excitability and its relationship to survival prognosis in patients with amyotrophic lateral sclerosis (ALS). A total of 145 patients with sporadic ALS and 73 healthy control participants were studied. We recorded compound muscle action potential and sensory nerve action potential of the median nerve and the median nerve somatosensory evoked potential (SEP), and we measured parameters, including onset-to-peak amplitude of N13 and N20 and peak-to-peak amplitude between N20 and P25 (N20p-P25p). Clinical prognostic factors, including ALS Functional Rating Scale-Revised, were evaluated. We followed up patients until the endpoints (death or tracheostomy) and analyzed factors associated with survival using multivariate analysis in the Cox proportional hazard model. Compared to controls, patients with ALS showed a larger amplitude of N20p-P25p in the median nerve SEP. Median survival time after examination was shorter in patients with N20p-P25p ≥8 μV (0.82 years) than in those with N20p-P25p <8 μV (1.68 years, p = 0.0002, log-rank test). Multivariate analysis identified a larger N20p-P25p amplitude as a factor that was independently associated with shorter survival ( p = 0.002). Sensory cortex hyperexcitability predicts short survival in patients with ALS. © 2018 American Academy of Neurology.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bilbao, Cristina, E-mail: cbilbao@dbbf.ulpgc.e; Department of Radiation Oncology, Hospital Universitario de Gran Canaria Dr. Negrin, Las Palmas de Gran Canaria, Canary Islands; Lara, Pedro Carlos

Purpose: To elucidate whether microsatellite instability (MSI) predicts clinical outcome in radiation-treated endometrioid endometrial cancer (EEC). Methods and Materials: A consecutive series of 93 patients with EEC treated with extrafascial hysterectomy and postoperative radiotherapy was studied. The median clinical follow-up of patients was 138 months, with a maximum of 232 months. Five quasimonomorphic mononucleotide markers (BAT-25, BAT-26, NR21, NR24, and NR27) were used for MSI classification. Results: Twenty-five patients (22%) were classified as MSI. Both in the whole series and in early stages (I and II), univariate analysis showed a significant association between MSI and poorer 10-year local disease-free survival,more » disease-free survival, and cancer-specific survival. In multivariate analysis, MSI was excluded from the final regression model in the whole series, but in early stages MSI provided additional significant predictive information independent of traditional prognostic and predictive factors (age, stage, grade, and vascular invasion) for disease-free survival (hazard ratio [HR] 3.25, 95% confidence interval [CI] 1.01-10.49; p = 0.048) and cancer-specific survival (HR 4.20, 95% CI 1.23-14.35; p = 0.022) and was marginally significant for local disease-free survival (HR 3.54, 95% CI 0.93-13.46; p = 0.064). Conclusions: These results suggest that MSI may predict radiotherapy response in early-stage EEC.« less

Multiplexed methylation profiles of tumor suppressor genes and clinical outcome in oligodendroglial tumors.

PubMed

Kuo, Lu-Ting; Lu, Hsueh-Yi; Lee, Chien-Chang; Tsai, Jui-Chang; Lai, Hong-Shiee; Tseng, Ham-Min; Kuo, Meng-Fai; Tu, Yong-Kwang

2016-08-01

Aberrant methylation has been associated with transcriptional inactivation of tumor-related genes in a wide spectrum of human neoplasms. The influence of DNA methylation in oligodendroglial tumors is not fully understood. Genomic DNA was isolated from 61 oligodendroglial tumors for analysis of methylation using methylation-specific multiplex ligation-dependent probe amplification assay (MS-MLPA). We correlated methylation status with clinicopathological findings and outcome. The genes found to be most frequently methylated in oligodendroglial tumors were RASSF1A (80.3%), CASP8 (70.5%), and CDKN2A (52.5%). Kaplan-Meier survival curve analysis demonstrated longer duration of progression-free survival in patients with 19q loss, aged less than 38 years, and with a proliferative index of less than 5%. Methylation of the ESR1 promoter is significantly associated with shorter duration of overall survival and progression-free survival, and that methylation of IGSF4 and RASSF1A is significantly associated with shorter duration of progression-free survival. However, none of the methylation status of ESR1, IGSF4, and RASSF1A was of prognostic value for survival in a multivariate Cox model. A number of novel and interesting epigenetic alterations were identified in this study. The findings highlight the importance of methylation profiles in oligodendroglial tumors and their possible involvement in tumorigenesis. © 2016 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

A cost-utility analysis of NRG Oncology/Gynecologic Oncology Group Protocol 218: incorporating prospectively collected quality-of-life scores in an economic model of treatment of ovarian cancer.

PubMed

Cohn, David E; Barnett, Jason C; Wenzel, Lari; Monk, Bradley J; Burger, Robert A; Straughn, J Michael; Myers, Evan R; Havrilesky, Laura J

2015-02-01

To estimate quality-of-life (QOL)-adjusted cost-utility with addition of bevacizumab (B) to intravenous paclitaxel/carboplatin (PC) for primary treatment of advanced-stage epithelial ovarian cancer. A modified Markov state transition model of 3 regimens evaluated in GOG 218 (PC, PC+concurrent B [PCB], and PCB+maintenance B [PCB+B]) was populated by prospectively collected survival, adverse event, and QOL data from GOG 218. Progression-free survival (PFS) and overall survival (OS) were modeled using primary event data. Costs of grade 4 hypertension, grade 3-5 bowel events, and growth factor support were incorporated. QOL scores were converted to utilities and incorporated into the model. Monte Carlo probabilistic sensitivity analysis was performed to account for uncertainty in estimates. PC was the least expensive ($4044) and least effective (mean 1.1 quality-adjusted progression-free years [QA-PFY]) regimen. PCB ($43,703 and 1.13 QA-PFY) was dominated by a combination of PC and PCB+B. PCB+B ($122,700 and 1.25 QA-PFY) was the most expensive regimen with an incremental cost-effectiveness ratio of $792,380/QA-PFY compared to PC. In a model not incorporating QOL, the incremental cost-effectiveness ratio (ICER) of PCB+B was $632,571/PFY compared to PC. In this cost-utility model, incorporation of QOL into an analysis of GOG 218 led to less favorable ICER (by >$150,000/QA-PFY) in regimens containing B compared with those that do not include B. Continued investigation of populations with ovarian cancer in whom the efficacy of treatment with bevacizumab is expected to be increased (or in whom QOL is expected to increase with use) is critical. Copyright © 2014 Elsevier Inc. All rights reserved.

Cancer survival in Eastern and Western Germany after the fall of the iron curtain.

PubMed

Jansen, Lina; Gondos, Adam; Eberle, Andrea; Emrich, Katharina; Holleczek, Bernd; Katalinic, Alexander; Brenner, Hermann

2012-09-01

Prior to the German reunification, cancer survival was much lower in East than in West Germany. We compare cancer survival between Eastern and Western Germany in the early twenty-first century, i.e. the second decade after the German reunification. Using data from 11 population-based cancer registries covering a population of 33 million people, 5-year age-standardized relative survival for the time period 2002-2006 was estimated for the 25 most common cancers using model-based period analysis. In 2002-2006, 5-year relative survival was very similar for most cancers, with differences below 3% units for 20 of 25 cancer sites. Larger, statistically significant survival advantages were seen for oral cavity, oesophagus, and gallbladder cancer and skin melanoma in the West and for leukemia in the East. Our study shows that within two decades after the assimilation of political and health care systems, the former major survival gap of cancer patients in Eastern Germany has been essentially overcome. This result is encouraging as it suggests that, even though economic conditions have remained difficult in Eastern Germany, comparable health care provision may nevertheless enable comparable levels of cancer survival within a relatively short period of time.

Incorporating diverse data and realistic complexity into demographic estimation procedures for sea otters

USGS Publications Warehouse

Tinker, M. Timothy; Doak, Daniel F.; Estes, James A.; Hatfield, Brian B.; Staedler, Michelle M.; Gross, Arthur

2006-01-01

Reliable information on historical and current population dynamics is central to understanding patterns of growth and decline in animal populations. We developed a maximum likelihood-based analysis to estimate spatial and temporal trends in age/sex-specific survival rates for the threatened southern sea otter (Enhydra lutris nereis), using annual population censuses and the age structure of salvaged carcass collections. We evaluated a wide range of possible spatial and temporal effects and used model averaging to incorporate model uncertainty into the resulting estimates of key vital rates and their variances. We compared these results to current demographic parameters estimated in a telemetry-based study conducted between 2001 and 2004. These results show that survival has decreased substantially from the early 1990s to the present and is generally lowest in the north-central portion of the population's range. The greatest temporal decrease in survival was for adult females, and variation in the survival of this age/sex class is primarily responsible for regulating population growth and driving population trends. Our results can be used to focus future research on southern sea otters by highlighting the life history stages and mortality factors most relevant to conservation. More broadly, we have illustrated how the powerful and relatively straightforward tools of information-theoretic-based model fitting can be used to sort through and parameterize quite complex demographic modeling frameworks. ?? 2006 by the Ecological Society of America.

A simulation model to estimate the cost and effectiveness of alternative dialysis initiation strategies.

PubMed

Lee, Chris P; Chertow, Glenn M; Zenios, Stefanos A

2006-01-01

Patients with end-stage renal disease (ESRD) require dialysis to maintain survival. The optimal timing of dialysis initiation in terms of cost-effectiveness has not been established. We developed a simulation model of individuals progressing towards ESRD and requiring dialysis. It can be used to analyze dialysis strategies and scenarios. It was embedded in an optimization frame worked to derive improved strategies. Actual (historical) and simulated survival curves and hospitalization rates were virtually indistinguishable. The model overestimated transplantation costs (10%) but it was related to confounding by Medicare coverage. To assess the model's robustness, we examined several dialysis strategies while input parameters were perturbed. Under all 38 scenarios, relative rankings remained unchanged. An improved policy for a hypothetical patient was derived using an optimization algorithm. The model produces reliable results and is robust. It enables the cost-effectiveness analysis of dialysis strategies.

Proteobionics: biomimetics in proteomics.

PubMed

Sommer, Andrei P; Gheorghiu, Eleonora

2006-03-01

Proteomics was established 10 years ago by the analysis of microbial genomes via their protein complement or proteome. Bionics is an ancient art, which converts structures optimized by nature into advanced technical products. Previously, we analyzed survival modalities in nanobacteria and converted the interplay between survival-oriented protein functions and nanoscale mineral shells into models for advanced drug delivery. Exploiting protein functions observed in nature to design biomedical products and therapies could be named proteobionics. Here, we present examples for this new branch of nanoproteomics.

Cost-effectiveness of gemcitabine in stage IV non-small cell lung cancer: an estimate using the Population Health Model lung cancer module.

PubMed

Evans, W K

1997-04-01

Statistics Canada (Ottawa, Ontario, Canada) is in the process of developing the Population Health Model to simulate the health and common illnesses of Canadians. The Population Health Model incorporates a lung cancer module that is based on contemporary Canadian practice. This microsimulation model can be used to estimate the total direct care costs of treating all lung cancer cases diagnosed in Canada and to evaluate the cost and cost-effectiveness of new therapeutic interventions as they are introduced into practice. Gemcitabine, a new nucleoside analogue with a broad spectrum of antitumor activity, is about to be introduced on the Canadian market. The Population Health Model has been used to estimate the cost-effectiveness of gemcitabine in the management of lung cancer over a range of drug doses per treatment cycle starting at 1,000 mg/m2 weekly x 3, as well as potential survival benefits. The survival of stage IV non-small cell lung cancer (NSCLC) patients treated on an international trial of gemcitabine (EO-18) was used to estimate the potential survival gain relative to the survival of stage IV NSCLC patients managed with best supportive care on a randomized trial conducted by the National Cancer Institute of Canada (BR 5). Sensitivity analyses were performed assuming that the survival gain was 25% or 50% less than that reported in the EO-18 trial. The perspective of the economic analysis is that of the government as payer in a universal health care system, and all costs are expressed in 1993 Canadian dollars. Based on the apparent survival advantage of the EO-18 trial in comparison to best supportive care, the cost per life-year gained ranged from $632 to $9,285, depending on the dose per treatment cycle. At the highest dose per cycle (2,000 mg/m2) and with survival reduced by 50% as compared with the EO-18 result, the cost per life-year gained was estimated to be $17,390. From these estimates of direct care costs in the Canadian health care system, gemcitabine appears to be a cost-effective intervention for advanced NSCLC.

Comparative effectiveness of upfront esophagectomy versus induction chemoradiation in clinical stage T2N0 esophageal cancer: A decision analysis.

PubMed

Semenkovich, Tara R; Panni, Roheena Z; Hudson, Jessica L; Thomas, Theodore; Elmore, Leisha C; Chang, Su-Hsin; Meyers, Bryan F; Kozower, Benjamin D; Puri, Varun

2018-05-01

We compared the effectiveness of upfront esophagectomy versus induction chemoradiation followed by esophagectomy for overall survival in patients with clinical T2N0 (cT2N0) esophageal cancer. We also assessed the influence of the diagnostic uncertainty of endoscopic ultrasound on the expected benefit of chemoradiation. We created a decision analysis model representing 2 treatment strategies for cT2N0 esophageal cancer: upfront esophagectomy that may be followed by adjuvant therapy for upstaged patients and induction chemoradiation for all patients with cT2N0 esophageal cancer followed by esophagectomy. Parameter values within the model were obtained from published data, and median survival for pathologic subgroups was derived from the National Cancer Database. In sensitivity analyses, staging uncertainty of endoscopic ultrasound was introduced by varying the probability of pathologic upstaging. The baseline model showed comparable median survival for both strategies: 48.3 months for upfront esophagectomy versus 45.9 months for induction chemoradiation and surgery. The sensitivity analysis demonstrated induction chemoradiation was beneficial, with probability of upstaging > 48.1%, which is within the published range of 32% to 65% probability of pathologic upstaging after cT2N0 diagnosis. The presence of any of 3 key variables (size larger than 3 cm, high grade, or lymphovascular invasion) was associated with > 48.1% risk of upstaging, thus conferring a survival advantage to induction chemoradiation. The optimal treatment strategy for cT2N0 esophageal cancer depends on the accuracy of endoscopic ultrasound staging. High-risk features that confer increased probability of upstaging can inform clinical decision making to recommend induction chemoradiation for select cT2N0 patients. Copyright © 2018 The American Association for Thoracic Surgery. All rights reserved.

A comparison of individualized treatment guided by VeriStrat with standard of care treatment strategies in patients receiving second-line treatment for advanced non-small cell lung cancer: A cost-utility analysis.

PubMed

Nelson, Richard E; Stenehjem, David; Akerley, Wallace

2013-12-01

Two therapies are appropriate as 2nd-line treatment of non-small cell lung cancer (NSCLC) patients: chemotherapy and epidermal growth factor receptor (EGFR) inhibitor therapy. VeriStrat, a serum proteomic test, can be used to guide treatment decisions for NSCLC patients. The test classifies patients as likely to benefit from either of these two treatment options. The objective of this research was to model the anticipated survival and cost-effectiveness of four different treatment strategies: chemotherapy for all patients (C-all), EGFR inhibitor for all (E-all), a performance status guided selection strategy (PS-guided), and a strategy guided by VeriStrat test results (V-guided). We developed a Markov model with the perspective of the U.S. health care system. Model inputs were taken from published literature for the base-case analysis. One-way and probabilistic sensitivity analyses were performed. The C-all treatment strategy showed the best overall survival outcome (10.1 months), followed by V-guided (9.6 months), PS-guided (9.2 months), and E-all (8.2 months) strategies. The incremental cost-effectiveness ratio (ICER) of a V-guided treatment strategy was $91,111 (vs. E-all) and $8462 (vs. PS-guided) per quality-adjusted life year (QALY). The ICER for C-all compared to V-guided was $105,616. This cost-utility analysis indicates that a treatment strategy guided by the VeriStrat test in patients receiving second-line therapy for NSCLC may experience an overall survival benefit at an incremental cost-effectiveness ratio that is reasonable when compared with other practices, including cancer treatments, generally covered in the U.S. health care system. However, treating all patients with chemotherapy yielded the greatest expected survival. Copyright © 2013. Published by Elsevier Ireland Ltd.

Cisplatin and Etoposide Versus Carboplatin and Paclitaxel With Concurrent Radiotherapy for Stage III Non–Small-Cell Lung Cancer: An Analysis of Veterans Health Administration Data

PubMed Central

Santana-Davila, Rafael; Devisetty, Kiran; Szabo, Aniko; Sparapani, Rodney; Arce-Lara, Carlos; Gore, Elizabeth M.; Moran, Amy; Williams, Christina D.; Kelley, Michael J.; Whittle, Jeffrey

2015-01-01

Purpose The optimal chemotherapy regimen to use with radiotherapy in stage III non–small-cell lung cancer is unknown. Here, we compare the outcome of patents treated within the Veterans Health Administration with either etoposide-cisplatin (EP) or carboplatin-paclitaxel (CP). Methods We identified patients treated with EP and CP with concurrent radiotherapy from 2001 to 2010. Survival rates were compared using Cox proportional hazards regression models with adjustments for confounding provided by propensity score methods and an instrumental variables analysis. Comorbidities and treatment complications were identified through administrative data. Results A total of 1,842 patients were included; EP was used in 27% (n = 499). Treatment with EP was not associated with a survival advantage in a Cox proportional hazards model (hazard ratio [HR], 0.97; 95% CI, 0.85 to 1.10), a propensity score matched cohort (HR, 1.07; 95% CI, 0.91 to 1.24), or a propensity score adjusted model (HR, 0.97; 95% CI, 0.85 to 1.10). In an instrumental variables analysis, there was no survival advantage for patients treated in centers where EP was used more than 50% of the time as compared with centers where EP was used in less than 10% of the patients (HR, 1.07; 95% CI, 0.90 to 1.26). Patients treated with EP, compared with patients treated with CP, had more hospitalizations (2.4 v 1.7 hospitalizations, respectively; P < .001), outpatient visits (17.6 v 12.6 visits, respectively; P < .001), infectious complications (47.3% v 39.4%, respectively; P = .0022), acute kidney disease/dehydration (30.5% v 21.2%, respectively; P < .001), and mucositis/esophagitis (18.6% v 14.4%, respectively; P = .0246). Conclusion After accounting for prognostic variables, patients treated with EP versus CP had similar overall survival, but EP was associated with increased morbidity. PMID:25422491

Living Donation Versus Donation After Circulatory Death Liver Transplantation for Low MELD Recipients.

PubMed

Kling, Catherine E; Perkins, James D; Reyes, Jorge D; Montenovo, Martin I

2018-04-10

Background In this era of organ scarcity, living donor liver transplant (LDLT) is an alternative to using deceased donors and in Western countries is more often used in low model for end-stage liver disease (MELD) recipients. We sought to compare the patient survival and graft survival between recipients of liver transplantation from living donors and donation after circulatory death (DCD) donors in patients with low MELD scores. Methods Retrospective cohort analysis of adult liver transplant recipients with a laboratory MELD <= 20 who underwent transplantation between 01/01/2003 and 03/31/2016. Recipients were categorized by donor graft type (DCD or LDLT) and recipient and donor characteristics were compared. Ten-year patient and graft survival curves were calculated using Kaplan-Meier analyses and a mixed-effects model was performed to determine the contributions of recipient, donor and center variables on patient and graft survival. Results 36,705 liver transplants were performed - 2,166 (5.9%) were from DCD donors and 2,284 (6.2%) from living donors. In the mixed-effects model, DCD status was associated with a higher risk of graft failure (RR 1.27, 95% CI 1.16-1.38) but not worse patient survival (RR 1.27, 95% CI: 0.96-1.67). Lower DCD center experience was associated with a 1.21 higher risk of patient death (95% CI: 1.17-1.25) and 1.13 higher risk of graft failure (95% CI: 1.12-1.15). LDLT center experience was also predictive of patient survival (RR 1.03, 95% CI: 1.02-1.03) and graft failure (RR 1.05, 95% CI: 1.05-1.06). Conclusion For liver transplant recipients with low laboratory MELD, LDLT offers better graft survival and a tendency to better patient survival than DCD donors. This article is protected by copyright. All rights reserved. © 2018 by the American Association for the Study of Liver Diseases.

NOAA Fisheries Toolbox - Welcome

Science.gov Websites

Fitting Model » Stock Synthesis Version 3 » Survival Estimation in Non-Equilibrium situations » Virtual -Sissenwine Analysis (CSA) 4.3 01/13/2014 Dual Zone Virtual Population Analysis (VPA-2BOX) 3.05 8/4/2004 /2013 Stock Synthesis Version 3 (SS3) 3.45f 10/18/2012 Virtual Population Analysis (VPA) 3.4.4 3/3/2014

Embolotherapy for Neuroendocrine Tumor Liver Metastases: Prognostic Factors for Hepatic Progression-Free Survival and Overall Survival

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, James X.; Rose, Steven; White, Sarah B.

PurposeThe purpose of the study was to evaluate prognostic factors for survival outcomes following embolotherapy for neuroendocrine tumor (NET) liver metastases.Materials and MethodsThis was a multicenter retrospective study of 155 patients (60 years mean age, 57 % male) with NET liver metastases from pancreas (n = 71), gut (n = 68), lung (n = 8), or other/unknown (n = 8) primary sites treated with conventional transarterial chemoembolization (TACE, n = 50), transarterial radioembolization (TARE, n = 64), or transarterial embolization (TAE, n = 41) between 2004 and 2015. Patient-, tumor-, and treatment-related factors were evaluated for prognostic effect on hepatic progression-free survival (HPFS) and overall survival (OS) using unadjusted and propensity score-weighted univariate and multivariate Coxmore » proportional hazards models.ResultsMedian HPFS and OS were 18.5 and 125.1 months for G1 (n = 75), 12.2 and 33.9 months for G2 (n = 60), and 4.9 and 9.3 months for G3 tumors (n = 20), respectively (p < 0.05). Tumor burden >50 % hepatic volume demonstrated 5.5- and 26.8-month shorter median HPFS and OS, respectively, versus burden ≤50 % (p < 0.05). There were no significant differences in HPFS or OS between gut or pancreas primaries. In multivariate HPFS analysis, there were no significant differences among embolotherapy modalities. In multivariate OS analysis, TARE had a higher hazard ratio than TACE (unadjusted Cox model: HR 2.1, p = 0.02; propensity score adjusted model: HR 1.8, p = 0.11), while TAE did not differ significantly from TACE.ConclusionHigher tumor grade and tumor burden prognosticated shorter HPFS and OS. TARE had a higher hazard ratio for OS than TACE. There were no significant differences in HPFS among embolotherapy modalities.« less

STRUCTURAL ESTIMATES OF TREATMENT EFFECTS ON OUTCOMES USING RETROSPECTIVE DATA: AN APPLICATION TO DUCTAL CARCINOMA IN SITU

PubMed Central

Gold, Heather Taffet; Sorbero, Melony E. S.; Griggs, Jennifer J.; Do, Huong T.; Dick, Andrew W.

2013-01-01

Analysis of observational cohort data is subject to bias from unobservable risk selection. We compared econometric models and treatment effectiveness estimates using the linked Surveillance, Epidemiology, and End Results (SEER)-Medicare claims data for women diagnosed with ductal carcinoma in situ. Treatment effectiveness estimates for mastectomy and breast conserving surgery (BCS) with or without radiotherapy were compared using three different models: simultaneous-equations model, discrete-time survival model with unobserved heterogeneity (frailty), and proportional hazards model. Overall trends in disease-free survival (DFS), or time to first subsequent breast event, by treatment are similar regardless of the model, with mastectomy yielding the highest DFS over 8 years of follow-up, followed by BCS with radiotherapy, and then BCS alone. Absolute rates and direction of bias varied substantially by treatment strategy. DFS was underestimated by single-equation and frailty models compared to the simultaneous-equations model and RCT results for BCS with RT and overestimated for BCS alone. PMID:21602195

TMA Navigator: network inference, patient stratification and survival analysis with tissue microarray data

PubMed Central

Lubbock, Alexander L. R.; Katz, Elad; Harrison, David J.; Overton, Ian M.

2013-01-01

Tissue microarrays (TMAs) allow multiplexed analysis of tissue samples and are frequently used to estimate biomarker protein expression in tumour biopsies. TMA Navigator (www.tmanavigator.org) is an open access web application for analysis of TMA data and related information, accommodating categorical, semi-continuous and continuous expression scores. Non-biological variation, or batch effects, can hinder data analysis and may be mitigated using the ComBat algorithm, which is incorporated with enhancements for automated application to TMA data. Unsupervised grouping of samples (patients) is provided according to Gaussian mixture modelling of marker scores, with cardinality selected by Bayesian information criterion regularization. Kaplan–Meier survival analysis is available, including comparison of groups identified by mixture modelling using the Mantel-Cox log-rank test. TMA Navigator also supports network inference approaches useful for TMA datasets, which often constitute comparatively few markers. Tissue and cell-type specific networks derived from TMA expression data offer insights into the molecular logic underlying pathophenotypes, towards more effective and personalized medicine. Output is interactive, and results may be exported for use with external programs. Private anonymous access is available, and user accounts may be generated for easier data management. PMID:23761446

The EPOS-CC Score: An Integration of Independent, Tumor- and Patient-Associated Risk Factors to Predict 5-years Overall Survival Following Colorectal Cancer Surgery.

PubMed

Haga, Yoshio; Ikejiri, Koji; Wada, Yasuo; Ikenaga, Masakazu; Koike, Shoichiro; Nakamura, Seiji; Koseki, Masato

2015-06-01

Surgical audit is an essential task for the estimation of postoperative outcome and comparison of quality of care. Previous studies on surgical audits focused on short-term outcomes, such as postoperative mortality. We propose a surgical audit evaluating long-term outcome following colorectal cancer surgery. The predictive model for this audit is designated as 'Estimation of Postoperative Overall Survival for Colorectal Cancer (EPOS-CC)'. Thirty-one tumor-related and physiological variables were prospectively collected in 889 patients undergoing elective resection for colorectal cancer between April 2005 and April 2007 in 16 Japanese hospitals. Postoperative overall survival was assessed over a 5-years period. The EPOS-CC score was established by selecting significant variables in a uni- and multivariate analysis and allocating a risk-adjusted multiplication factor to each variable using Cox regression analysis. For validation, the EPOS-CC score was compared to the predictive power of UICC stage. Inter-hospital variability of the observed-to-estimated 5-years survival was assessed to estimate quality of care. Among the 889 patients, 804 (90%) completed the 5-years follow-up. Univariate analysis displayed a significant correlation with 5-years survival for 14 physiological and nine tumor-related variables (p < 0.005). Highly significant p-values below 0.0001 were found for age, ASA score, severe pulmonary disease, respiratory history, performance status, hypoalbuminemia, alteration of hemoglobin, serum sodium level, and for all histological variables except tumor location. Age, TNM stage, lymphatic invasion, performance status, and serum sodium level were independent variables in the multivariate analysis and were entered the EPOS-CC model for the prediction of survival. Risk-adjusted multiplication factors between 1.5 (distant metastasis) and 0.16 (serum sodium level) were accorded to the different variables. The predictive power of EPOS-CC was superior to the one of UICC stage; area under the curve 0.87, 95% CI 0.85-0.90 for EPOS-CC, and 0.80, 0.76-0.83 for UICC stage, p < 0.001. Quality of care did not differ between hospitals. The EPOS-CC score including the independent variables age, performance status, serum sodium level, TNM stage, and lymphatic invasion is superior to the UICC stage in the prediction of 5-years overall survival. This higher accuracy might be explained by the inclusion of physiological factors, thus also taking non-tumor-associated deaths into account. Furthermore, EPOS-CC score may compare quality of care among different institutions. Future studies are necessary to further evaluate this score and help improving the prediction of long-term survival following colorectal cancer surgery.

Predictors of overall and recurrence-free survival after neoadjuvant chemotherapy for gastroesophageal adenocarcinoma: Pooled analysis of individual patient data (IPD) from randomized controlled trials (RCTs).

PubMed

Ronellenfitsch, U; Schwarzbach, M; Hofheinz, R; Kienle, P; Nowak, K; Kieser, M; Slanger, T E; Burmeister, B; Kelsen, D; Niedzwiecki, D; Schuhmacher, C; Urba, S; van de Velde, C; Walsh, T N; Ychou, M; Jensen, K

2017-08-01

Neoadjuvant chemotherapy improves prognosis of patients with locally advanced gastroesophageal adenocarcinoma. The aim of this study was to identify predictors for postoperative survival following neoadjuvant therapy. These could be useful in deciding about postoperative continuation of chemotherapy. This meta-analysis used IPD from RCTs comparing neoadjuvant chemotherapy with surgery alone for gastroesophageal adenocarcinoma. Trials providing IPD on age, sex, performance status, pT/N stage, resection status, overall and recurrence-free survival were included. Survival was calculated in the entire study population and subgroups stratified by supposed predictors and compared using the log-rank test. Multivariable Cox models were used to identify independent survival predictors. Four RCTs providing IPD from 553 patients fulfilled the inclusion criteria. (y)pT and (y)pN stage and resection status strongly predicted postoperative survival both after neoadjuvant therapy and surgery alone. Patients with R1 resection after neoadjuvant therapy survived longer than those with R1 resection after surgery alone. Patients with stage pN0 after surgery alone had better prognosis than those with ypN0 after neoadjuvant therapy. Patients with stage ypT3/4 after neoadjuvant therapy survived longer than those with stage pT3/4 after surgery alone. Multivariable regression identified resection status and (y)pN stage as predictors of survival in both groups. (y)pT stage predicted survival only after surgery alone. After neoadjuvant therapy for gastroesophageal adenocarcinoma, survival is determined by the same factors as after surgery alone. However, ypT stage is not an independent predictor. These results can facilitate the decision about postoperative continuation of chemotherapy in pretreated patients. Copyright © 2017 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

Biological Effects of Protracted Exposure to Ionizing Radiation: Review, Analysis, and Model Development

DTIC Science & Technology

1991-11-01

dynamics, physiological changes, morphologi- cal changes, cell/tissue damage and recovery mechanisms, and existing radiobiological injury and recovery...humans and the ferret. The gut injury model (GIM) is a three-compartment hierarchial- type tissue model to simulate radiation-induced changes in the...Prodromal Symptoms Diarrhea Gastrointestinal Symptoms Dose Rate Cell Survival Intestinal Injury Fatigability Cell Damage Cell Repair Cell Proliferation

Aircraft Survivability: Aircraft Battle Damage and Repair, Summer 2007

DTIC Science & Technology

2007-01-01

Modeling and Analysis Program ( TMAP ) Missile Modeling System for Advanced Investigation of Countermeasures (MOSAIC) & Joint Surface-to-Air Missle... TMAP Threat System Models (TSM) into engagement simulations (MOSAIC [IR] and JSAMS [RF]). This 3-year project will integrate and fully test six...three per engagement simulation) JASC priority TMAP TSMs in official releases of MOSAIC and JSAMS. Project Engineers— Luke Borntrager (USAF, AFRL) and

A general model for the analysis of mark-resight, mark-recapture, and band-recovery data under tag loss

USGS Publications Warehouse

Conn, P.B.; Kendall, W.L.; Samuel, M.D.

2004-01-01

Estimates of waterfowl demographic parameters often come from resighting studies where birds fit with individually identifiable neck collars are resighted at a distance. Concerns have been raised about the effects of collar loss on parameter estimates, and the reliability of extrapolating from collared individuals to the population. Models previously proposed to account for collar loss do not allow survival or harvest parameters to depend on neck collar presence or absence. Also, few models have incorporated recent advances in mark-recapture theory that allow for multiple states or auxiliary encounters such as band recoveries. We propose a multistate model for tag loss in which the presence or absence of a collar is considered as a state variable. In this framework, demographic parameters are corrected for tag loss and questions related to collar effects on survival and recovery rates can be addressed. Encounters of individuals between closed sampling periods also can be incorporated in the analysis. We discuss data requirements for answering questions related to tag loss and sampling designs that lend themselves to this purpose. We illustrate the application of our model using a study of lesser snow geese (Chen caerulescens caerulescens).

A new semi-supervised learning model combined with Cox and SP-AFT models in cancer survival analysis.

PubMed

Chai, Hua; Li, Zi-Na; Meng, De-Yu; Xia, Liang-Yong; Liang, Yong

2017-10-12

Gene selection is an attractive and important task in cancer survival analysis. Most existing supervised learning methods can only use the labeled biological data, while the censored data (weakly labeled data) far more than the labeled data are ignored in model building. Trying to utilize such information in the censored data, a semi-supervised learning framework (Cox-AFT model) combined with Cox proportional hazard (Cox) and accelerated failure time (AFT) model was used in cancer research, which has better performance than the single Cox or AFT model. This method, however, is easily affected by noise. To alleviate this problem, in this paper we combine the Cox-AFT model with self-paced learning (SPL) method to more effectively employ the information in the censored data in a self-learning way. SPL is a kind of reliable and stable learning mechanism, which is recently proposed for simulating the human learning process to help the AFT model automatically identify and include samples of high confidence into training, minimizing interference from high noise. Utilizing the SPL method produces two direct advantages: (1) The utilization of censored data is further promoted; (2) the noise delivered to the model is greatly decreased. The experimental results demonstrate the effectiveness of the proposed model compared to the traditional Cox-AFT model.

Causal Mediation Analysis for the Cox Proportional Hazards Model with a Smooth Baseline Hazard Estimator.

PubMed

Wang, Wei; Albert, Jeffrey M

2017-08-01

An important problem within the social, behavioral, and health sciences is how to partition an exposure effect (e.g. treatment or risk factor) among specific pathway effects and to quantify the importance of each pathway. Mediation analysis based on the potential outcomes framework is an important tool to address this problem and we consider the estimation of mediation effects for the proportional hazards model in this paper. We give precise definitions of the total effect, natural indirect effect, and natural direct effect in terms of the survival probability, hazard function, and restricted mean survival time within the standard two-stage mediation framework. To estimate the mediation effects on different scales, we propose a mediation formula approach in which simple parametric models (fractional polynomials or restricted cubic splines) are utilized to approximate the baseline log cumulative hazard function. Simulation study results demonstrate low bias of the mediation effect estimators and close-to-nominal coverage probability of the confidence intervals for a wide range of complex hazard shapes. We apply this method to the Jackson Heart Study data and conduct sensitivity analysis to assess the impact on the mediation effects inference when the no unmeasured mediator-outcome confounding assumption is violated.

Implementing a novel movement-based approach to inferring parturition and neonate caribou calf survival.

PubMed

Bonar, Maegwin; Ellington, E Hance; Lewis, Keith P; Vander Wal, Eric

2018-01-01

In ungulates, parturition is correlated with a reduction in movement rate. With advances in movement-based technologies comes an opportunity to develop new techniques to assess reproduction in wild ungulates that are less invasive and reduce biases. DeMars et al. (2013, Ecology and Evolution 3:4149-4160) proposed two promising new methods (individual- and population-based; the DeMars model) that use GPS inter-fix step length of adult female caribou (Rangifer tarandus caribou) to infer parturition and neonate survival. Our objective was to apply the DeMars model to caribou populations that may violate model assumptions for retrospective analysis of parturition and calf survival. We extended the use of the DeMars model after assigning parturition and calf mortality status by examining herd-wide distributions of parturition date, calf mortality date, and survival. We used the DeMars model to estimate parturition and calf mortality events and compared them with the known parturition and calf mortality events from collared adult females (n = 19). We also used the DeMars model to estimate parturition and calf mortality events for collared female caribou with unknown parturition and calf mortality events (n = 43) and instead derived herd-wide estimates of calf survival as well as distributions of parturition and calf mortality dates and compared them to herd-wide estimates generated from calves fitted with VHF collars (n = 134). For our data, the individual-based method was effective at predicting calf mortality, but was not effective at predicting parturition. The population-based method was more effective at predicting parturition but was not effective at predicting calf mortality. At the herd-level, the predicted distributions of parturition date from both methods differed from each other and from the distribution derived from the parturition dates of VHF-collared calves (log-ranked test: χ2 = 40.5, df = 2, p < 0.01). The predicted distributions of calf mortality dates from both methods were similar to the observed distribution derived from VHF-collared calves. Both methods underestimated herd-wide calf survival based on VHF-collared calves, however, a combination of the individual- and population-based methods produced herd-wide survival estimates similar to estimates generated from collared calves. The limitations we experienced when applying the DeMars model could result from the shortcomings in our data violating model assumptions. However despite the differences in our caribou systems, with proper validation techniques the framework in the DeMars model is sufficient to make inferences on parturition and calf mortality.

Implementing a novel movement-based approach to inferring parturition and neonate caribou calf survival

PubMed Central

Ellington, E. Hance; Lewis, Keith P.; Vander Wal, Eric

2018-01-01

In ungulates, parturition is correlated with a reduction in movement rate. With advances in movement-based technologies comes an opportunity to develop new techniques to assess reproduction in wild ungulates that are less invasive and reduce biases. DeMars et al. (2013, Ecology and Evolution 3:4149–4160) proposed two promising new methods (individual- and population-based; the DeMars model) that use GPS inter-fix step length of adult female caribou (Rangifer tarandus caribou) to infer parturition and neonate survival. Our objective was to apply the DeMars model to caribou populations that may violate model assumptions for retrospective analysis of parturition and calf survival. We extended the use of the DeMars model after assigning parturition and calf mortality status by examining herd-wide distributions of parturition date, calf mortality date, and survival. We used the DeMars model to estimate parturition and calf mortality events and compared them with the known parturition and calf mortality events from collared adult females (n = 19). We also used the DeMars model to estimate parturition and calf mortality events for collared female caribou with unknown parturition and calf mortality events (n = 43) and instead derived herd-wide estimates of calf survival as well as distributions of parturition and calf mortality dates and compared them to herd-wide estimates generated from calves fitted with VHF collars (n = 134). For our data, the individual-based method was effective at predicting calf mortality, but was not effective at predicting parturition. The population-based method was more effective at predicting parturition but was not effective at predicting calf mortality. At the herd-level, the predicted distributions of parturition date from both methods differed from each other and from the distribution derived from the parturition dates of VHF-collared calves (log-ranked test: χ2 = 40.5, df = 2, p < 0.01). The predicted distributions of calf mortality dates from both methods were similar to the observed distribution derived from VHF-collared calves. Both methods underestimated herd-wide calf survival based on VHF-collared calves, however, a combination of the individual- and population-based methods produced herd-wide survival estimates similar to estimates generated from collared calves. The limitations we experienced when applying the DeMars model could result from the shortcomings in our data violating model assumptions. However despite the differences in our caribou systems, with proper validation techniques the framework in the DeMars model is sufficient to make inferences on parturition and calf mortality. PMID:29466451

Economic Analysis of Neoadjuvant Chemotherapy Versus Primary Debulking Surgery for Advanced Epithelial Ovarian Cancer Using an Aggressive Surgical Paradigm.

PubMed

Cole, Ashley L; Barber, Emma L; Gogate, Anagha; Tran, Arthur-Quan; Wheeler, Stephanie B

2018-04-21

Neoadjuvant chemotherapy (NACT) versus primary debulking surgery (PDS) for advanced epithelial ovarian cancer (AEOC) remains controversial in the United States. Generalizability of existing trial results has been criticized because of less aggressive debulking procedures than commonly used in the United States. As a result, economic evaluations using input data from these trials may not accurately reflect costs and outcomes associated with more aggressive primary surgery. Using data from an ongoing trial performing aggressive debulking, we investigated the cost-effectiveness and cost-utility of NACT versus PDS for AEOC. A decision tree model was constructed to estimate differences in short-term outcomes and costs for a hypothetical cohort of 15,000 AEOC patients (US annual incidence of AEOC) treated with NACT versus PDS over a 1-year time horizon from a Medicare payer perspective. Outcomes included costs per cancer-related death averted, life-years and quality-adjusted life-years (QALYs) gained. Base-case probabilities, costs, and utilities were based on the Surgical Complications Related to Primary or Interval Debulking in Ovarian Neoplasms trial. Base-case analyses assumed equivalent survival; threshold analysis estimated the maximum survival difference that would result in NACT being cost-effective at $50,000/QALY and $100,000/QALY willingness-to-pay thresholds. Probabilistic sensitivity analysis was used to characterize model uncertainty. Compared with PDS, NACT was associated with $142 million in cost savings, 1098 fewer cancer-related deaths, and 1355 life-years and 1715 QALYs gained, making it the dominant treatment strategy for all outcomes. In sensitivity analysis, NACT remained dominant in 99.3% of simulations. Neoadjuvant chemotherapy remained cost-effective at $50,000/QALY and $100,000/QALY willingness-to-pay thresholds if survival differences were less than 2.7 and 1.4 months, respectively. In the short term, NACT is cost-saving with improved outcomes. However, if PDS provides a longer-term survival advantage, it may be cost-effective. Research is needed on the role of patient preferences in tradeoffs between survival and quality of life.

The value of survival analyses for evidence-based rural medical workforce planning.

PubMed

Russell, Deborah J; Humphreys, John S; McGrail, Matthew R; Cameron, W Ian; Williams, Peter J

2013-12-11

Globally, abundant opportunities exist for policymakers to improve the accessibility of rural and remote populations to primary health care through improving workforce retention. This paper aims to identify and quantify the most important factors associated with rural and remote Australian family physician turnover, and to demonstrate how evidence generated by survival analysis of health workforce data can inform rural workforce policy making. A secondary analysis of longitudinal data collected by the New South Wales (NSW) Rural Doctors Network for all family physicians working in rural or remote NSW between January 1(st) 2003 and December 31(st) 2012 was performed. The Prentice, Williams and Peterson statistical model for survival analysis was used to identify and quantify risk factors for rural NSW family physician turnover. Multivariate modelling revealed a higher (2.65-fold) risk of family physician turnover in small, remote locations compared to that in small closely settled locations. Family physicians who graduated from countries other than Australia, United Kingdom, United States of America, New Zealand, Ireland, and Canada also had a higher (1.45-fold) risk of turnover compared to Australian trained family physicians. This was after adjusting for the effects of conditional registration. Procedural skills and public hospital admitting rights were associated with a lower risk of turnover. These risks translate to a predicted median survival of 11 years for Australian-trained family physician non-proceduralists with hospital admitting rights working in small coastal closely settled locations compared to 3 years for family physicians in remote locations. This study provides rigorous empirical evidence of the strong association between population size and geographical location and the retention of family physicians in rural and remote NSW. This has important policy ramifications since retention grants for rural and remote family physicians in Australia are currently based on a geographical 'remoteness' classification rather than population size. In addition, this study demonstrates how survival analysis assists health workforce planning, such as through generating evidence to assist in benchmarking 'reasonable' lengths of practice in different geographic settings that might guide service obligation requirements.

The value of survival analyses for evidence-based rural medical workforce planning

PubMed Central

2013-01-01

Background Globally, abundant opportunities exist for policymakers to improve the accessibility of rural and remote populations to primary health care through improving workforce retention. This paper aims to identify and quantify the most important factors associated with rural and remote Australian family physician turnover, and to demonstrate how evidence generated by survival analysis of health workforce data can inform rural workforce policy making. Methods A secondary analysis of longitudinal data collected by the New South Wales (NSW) Rural Doctors Network for all family physicians working in rural or remote NSW between January 1st 2003 and December 31st 2012 was performed. The Prentice, Williams and Peterson statistical model for survival analysis was used to identify and quantify risk factors for rural NSW family physician turnover. Results Multivariate modelling revealed a higher (2.65-fold) risk of family physician turnover in small, remote locations compared to that in small closely settled locations. Family physicians who graduated from countries other than Australia, United Kingdom, United States of America, New Zealand, Ireland, and Canada also had a higher (1.45-fold) risk of turnover compared to Australian trained family physicians. This was after adjusting for the effects of conditional registration. Procedural skills and public hospital admitting rights were associated with a lower risk of turnover. These risks translate to a predicted median survival of 11 years for Australian-trained family physician non-proceduralists with hospital admitting rights working in small coastal closely settled locations compared to 3 years for family physicians in remote locations. Conclusions This study provides rigorous empirical evidence of the strong association between population size and geographical location and the retention of family physicians in rural and remote NSW. This has important policy ramifications since retention grants for rural and remote family physicians in Australia are currently based on a geographical ‘remoteness’ classification rather than population size. In addition, this study demonstrates how survival analysis assists health workforce planning, such as through generating evidence to assist in benchmarking ‘reasonable’ lengths of practice in different geographic settings that might guide service obligation requirements. PMID:24330603

Impact of copula directional specification on multi-trial evaluation of surrogate endpoints

PubMed Central

Renfro, Lindsay A.; Shang, Hongwei; Sargent, Daniel J.

2014-01-01

Evaluation of surrogate endpoints using patient-level data from multiple trials is the gold standard, where multi-trial copula models are used to quantify both patient-level and trial-level surrogacy. While limited consideration has been given in the literature to copula choice (e.g., Clayton), no prior consideration has been given to direction of implementation (via survival versus distribution functions). We demonstrate that evenwith the “correct” copula family, directional misspecification leads to biased estimates of patient-level and trial-level surrogacy. We illustrate with a simulation study and a re-analysis of disease-free survival as a surrogate for overall survival in early stage colon cancer. PMID:24905465