Survival analysis and Cox regression.
Benítez-Parejo, N; Rodríguez del Águila, M M; Pérez-Vicente, S
2011-01-01
The data provided by clinical trials are often expressed in terms of survival. The analysis of survival comprises a series of statistical analytical techniques in which the measurements analysed represent the time elapsed between a given exposure and the outcome of a certain event. Despite the name of these techniques, the outcome in question does not necessarily have to be either survival or death, and may be healing versus no healing, relief versus pain, complication versus no complication, relapse versus no relapse, etc. The present article describes the analysis of survival from both a descriptive perspective, based on the Kaplan-Meier estimation method, and in terms of bivariate comparisons using the log-rank statistic. Likewise, a description is provided of the Cox regression models for the study of risk factors or covariables associated to the probability of survival. These models are defined in both simple and multiple forms, and a description is provided of how they are calculated and how the postulates for application are checked - accompanied by illustrating examples with the shareware application R.
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.
High-dimensional, massive sample-size Cox proportional hazards regression for survival analysis.
Mittal, Sushil; Madigan, David; Burd, Randall S; Suchard, Marc A
2014-04-01
Survival analysis endures as an old, yet active research field with applications that spread across many domains. Continuing improvements in data acquisition techniques pose constant challenges in applying existing survival analysis methods to these emerging data sets. In this paper, we present tools for fitting regularized Cox survival analysis models on high-dimensional, massive sample-size (HDMSS) data using a variant of the cyclic coordinate descent optimization technique tailored for the sparsity that HDMSS data often present. Experiments on two real data examples demonstrate that efficient analyses of HDMSS data using these tools result in improved predictive performance and calibration.
Cox Regression Model Analysis of Infection in Renal Transplants After Operation.
Junchen, Z; Houjing, Z; Yun, F
2016-10-01
The objective of this study was to explore the factors that affect infections after renal transplant, establishing the Cox model to forecast infection for patients of renal transplant. Data were collected from patients who had renal transplantation in Nanking Jinlin Hospital from January 2011 to April 2015 (n = 305 transplants). There were 296 individual data that could be used after deleting the people who were lacking some data, changing the main immunosuppressants during the first year, losing follow-up, and data writing that was not fully 1 year after the operation; 296 individuals were divided by 3:7. The 206 data of patients (7/10 of the total individuals) were used to analyze and build a model, and the rest of the data were used to verify the model, analyzing the 206 data with Cox regression, discovering the factors that affect the infection after renal transplant independently, building the model, and verification. Cox regression showed that there are three independent factors that affect infections after renal transplant: X3, the donor type (relative risk [RR] = 1.929, P = .037); X9, dialysis time (RR = 1.017, P = .032); and X13, human leukocyte antigen (HLA) match (RR = 0.257, P = .013). The model is: PI = 0.657X3 + 0.017X9 - 1.359X13. All PI for the 206 individuals were calculated and then divided into three groups: the low-risk group, the median-risk group, and the high-risk group. The model was verified by calculating the PI for all 90 people. The log-rank test showed that the survival rates among these groups were significantly different (P < .001). Donor type, dialysis time, and HLA match are all factors that affect infection after renal transplant. Donor type and dialysis time were the dangerous factors for infection, but HLA match was the protecting factor. The model depends on these three factors and could forecast infection after renal transplant. Copyright © 2016. Published by Elsevier Inc.
Objective Bayesian model selection for Cox regression.
Held, Leonhard; Gravestock, Isaac; Sabanés Bové, Daniel
2016-12-20
There is now a large literature on objective Bayesian model selection in the linear model based on the g-prior. The methodology has been recently extended to generalized linear models using test-based Bayes factors. In this paper, we show that test-based Bayes factors can also be applied to the Cox proportional hazards model. If the goal is to select a single model, then both the maximum a posteriori and the median probability model can be calculated. For clinical prediction of survival, we shrink the model-specific log hazard ratio estimates with subsequent calculation of the Breslow estimate of the cumulative baseline hazard function. A Bayesian model average can also be employed. We illustrate the proposed methodology with the analysis of survival data on primary biliary cirrhosis patients and the development of a clinical prediction model for future cardiovascular events based on data from the Second Manifestations of ARTerial disease (SMART) cohort study. Cross-validation is applied to compare the predictive performance with alternative model selection approaches based on Harrell's c-Index, the calibration slope and the integrated Brier score. Finally, a novel application of Bayesian variable selection to optimal conditional prediction via landmarking is described. Copyright © 2016 John Wiley & Sons, Ltd.
van Houwelingen, Hans C; Putter, Hein
2015-04-01
By far the most popular model to obtain survival predictions for individual patients is the Cox model. The Cox model does not make any assumptions on the underlying hazard, but it relies heavily on the proportional hazards assumption. The most common ways to circumvent this robustness problem are 1) to categorize patients based on their prognostic risk score and to base predictions on Kaplan-Meier curves for the risk categories, or 2) to include interactions with the covariates and suitable functions of time. Robust estimators of the t(0)-year survival probabilities can also be obtained from a "stopped Cox" regression model, in which all observations are administratively censored at t(0). Other recent approaches to solve this robustness problem, originally proposed in the context of competing risks, are pseudo-values and direct binomial regression, based on unbiased estimating equations. In this paper stopped Cox regression is compared with these direct approaches. This is done by means of a simulation study to assess the biases of the different approaches and an analysis of breast cancer data to get some feeling for the performance in practice. The tentative conclusion is that stopped Cox and direct models agree well if the follow-up is not too long. There are larger differences for long-term follow-up data. There stopped Cox might be more efficient, but less robust.
Extended cox regression model: The choice of timefunction
NASA Astrophysics Data System (ADS)
Isik, Hatice; Tutkun, Nihal Ata; Karasoy, Durdu
2017-07-01
Cox regression model (CRM), which takes into account the effect of censored observations, is one the most applicative and usedmodels in survival analysis to evaluate the effects of covariates. Proportional hazard (PH), requires a constant hazard ratio over time, is the assumptionofCRM. Using extended CRM provides the test of including a time dependent covariate to assess the PH assumption or an alternative model in case of nonproportional hazards. In this study, the different types of real data sets are used to choose the time function and the differences between time functions are analyzed and discussed.
Simultaneous confidence bands for Cox regression from semiparametric random censorship.
Mondal, Shoubhik; Subramanian, Sundarraman
2016-01-01
Cox regression is combined with semiparametric random censorship models to construct simultaneous confidence bands (SCBs) for subject-specific survival curves. Simulation results are presented to compare the performance of the proposed SCBs with the SCBs that are based only on standard Cox. The new SCBs provide correct empirical coverage and are more informative. The proposed SCBs are illustrated with two real examples. An extension to handle missing censoring indicators is also outlined.
Adjusted variable plots for Cox's proportional hazards regression model.
Hall, C B; Zeger, S L; Bandeen-Roche, K J
1996-01-01
Adjusted variable plots are useful in linear regression for outlier detection and for qualitative evaluation of the fit of a model. In this paper, we extend adjusted variable plots to Cox's proportional hazards model for possibly censored survival data. We propose three different plots: a risk level adjusted variable (RLAV) plot in which each observation in each risk set appears, a subject level adjusted variable (SLAV) plot in which each subject is represented by one point, and an event level adjusted variable (ELAV) plot in which the entire risk set at each failure event is represented by a single point. The latter two plots are derived from the RLAV by combining multiple points. In each point, the regression coefficient and standard error from a Cox proportional hazards regression is obtained by a simple linear regression through the origin fit to the coordinates of the pictured points. The plots are illustrated with a reanalysis of a dataset of 65 patients with multiple myeloma.
Xu, Qiang; Paik, Myunghee Cho; Rundek, Tatjana; Elkind, Mitchell S. V.; Sacco, Ralph L.
2015-01-01
Incomplete covariates often obscure analysis results from a Cox regression. In an analysis of the Northern Manhattan Study (NOMAS) to determine the influence of insulin resistance on the incidence of stroke in non-diabetic individuals, insulin level is unknown for 34.1% of the subjects. The available data suggest that the missingness mechanism depends on outcome variables, which may generate biases in estimating the parameters of interest if only using the complete observations. This article aimed to introduce practical strategies to analyze the NOMAS data and present sensitivity analyses by using the reweighting method in standard statistical packages. When the data set structure is in counting process style, the reweighting estimates can be obtained by built-in procedures with variance estimated by the jackknife method. Simulation results indicate that the jackknife variance estimate provides reasonable coverage probability in moderate sample sizes. We subsequently conducted sensitivity analyses for the NOMAS data, showing that the risk estimates are robust to a variety of missingness mechanisms. At the end of this article, we present the core SAS and R programs used in the analysis. PMID:21965165
Gui, Jiang; Li, Hongzhe
2005-07-01
An important application of microarray technology is to relate gene expression profiles to various clinical phenotypes of patients. Success has been demonstrated in molecular classification of cancer in which the gene expression data serve as predictors and different types of cancer serve as a categorical outcome variable. However, there has been less research in linking gene expression profiles to the censored survival data such as patients' overall survival time or time to cancer relapse. It would be desirable to have models with good prediction accuracy and parsimony property. We propose to use the L(1) penalized estimation for the Cox model to select genes that are relevant to patients' survival and to build a predictive model for future prediction. The computational difficulty associated with the estimation in the high-dimensional and low-sample size settings can be efficiently solved by using the recently developed least-angle regression (LARS) method. Our simulation studies and application to real datasets on predicting survival after chemotherapy for patients with diffuse large B-cell lymphoma demonstrate that the proposed procedure, which we call the LARS-Cox procedure, can be used for identifying important genes that are related to time to death due to cancer and for building a parsimonious model for predicting the survival of future patients. The LARS-Cox regression gives better predictive performance than the L(2) penalized regression and a few other dimension-reduction based methods. We conclude that the proposed LARS-Cox procedure can be very useful in identifying genes relevant to survival phenotypes and in building a parsimonious predictive model that can be used for classifying future patients into clinically relevant high- and low-risk groups based on the gene expression profile and survival times of previous patients.
Inverse probability weighted Cox regression for doubly truncated data.
Mandel, Micha; de Uña-Álvarez, Jacobo; Simon, David K; Betensky, Rebecca A
2017-09-08
Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software. The approach is used to study the association between candidate single nucleotide polymorphisms and age of onset of Parkinson's disease. © 2017, The International Biometric Society.
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.
Yoshihama, Mieko; Horrocks, Julie
2003-08-01
This study uses Cox regression with time-varying covariates to examine the relationship between intimate partner violence and posttraumatic stress disorder (PTSD) in a random sample of Japanese American women and immigrant women from Japan (N = 211). Because applications of survival analysis in trauma research are scarce, this paper presents the utility of this analytical approach by contrasting it with other common methods of analysis (chi-square tests and Cox regression with covariates that do not change over time).
ERIC Educational Resources Information Center
Chen, Chau-Kuang
2005-01-01
Logistic and Cox regression methods are practical tools used to model the relationships between certain student learning outcomes and their relevant explanatory variables. The logistic regression model fits an S-shaped curve into a binary outcome with data points of zero and one. The Cox regression model allows investigators to study the duration…
[Application of spline-based Cox regression on analyzing data from follow-up studies].
Dong, Ying; Yu, Jin-ming; Hu, Da-yi
2012-09-01
With R, this study involved the application of the spline-based Cox regression to analyze data related to follow-up studies when the two basic assumptions of Cox proportional hazards regression were not satisfactory. Results showed that most of the continuous covariates contributed nonlinearly to mortality risk while the effects of three covariates were time-dependent. After considering multiple covariates in spline-based Cox regression, when the ankle brachial index (ABI) decreased by 0.1, the hazard ratio (HR) for all-cause death was 1.071. The spline-based Cox regression method could be applied to analyze the data related to follow-up studies when the assumptions of Cox proportional hazards regression were violated.
Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso
Kong, Shengchun; Nan, Bin
2013-01-01
We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival data, however, are neither iid nor Lipschitz.We first approximate the negative log partial likelihood function by a sum of iid non-Lipschitz terms, then derive the non-asymptotic oracle inequalities for the lasso penalized Cox regression using pointwise arguments to tackle the difficulties caused by lacking iid Lipschitz losses. PMID:24516328
Non-Asymptotic Oracle Inequalities for the High-Dimensional Cox Regression via Lasso.
Kong, Shengchun; Nan, Bin
2014-01-01
We consider finite sample properties of the regularized high-dimensional Cox regression via lasso. Existing literature focuses on linear models or generalized linear models with Lipschitz loss functions, where the empirical risk functions are the summations of independent and identically distributed (iid) losses. The summands in the negative log partial likelihood function for censored survival data, however, are neither iid nor Lipschitz.We first approximate the negative log partial likelihood function by a sum of iid non-Lipschitz terms, then derive the non-asymptotic oracle inequalities for the lasso penalized Cox regression using pointwise arguments to tackle the difficulties caused by lacking iid Lipschitz losses.
biospear: an R package for biomarker selection in penalized Cox regression.
Ternès, Nils; Rotolo, Federico; Michiels, Stefan
2017-09-12
The R package biospear allows selecting the biomarkers with the strongest impact on survival and on the treatment effect in high-dimensional Cox models, and estimating expected survival probabilities. Most of the implemented approaches are based on penalized regression techniques. The package is available on the CRAN. ( https://CRAN.R-project.org/package=biospear ). stefan.michiels@gustaveroussy.fr.
2010-01-01
Background Various perinatal factors influencing neuromotor development are known from cross sectional studies. Factors influencing the age at which distinct abilities are acquired are uncertain. We hypothesized that the Cox regression model might identify these factors. Methods Neonates treated at Aachen University Hospital in 2000/2001 were identified retrospectively (n = 796). Outcome data, based on a structured interview, were available from 466 children, as were perinatal data. Factors possibly related to outcome were identified by bootstrap selection and then included into a multivariate Cox regression model. To evaluate if the parental assessment might change with the time elapsed since birth we studied five age cohorts of 163 normally developed children. Results Birth weight, gestational age, congenital cardiac disease and periventricular leukomalacia were related to outcome in the multivariate analysis (p < 0.05). Analysis of the control cohorts revealed that the parents' assessment of the ability of bladder control is modified by the time elapsed since birth. Conclusions Combined application of the bootstrap resampling procedure and multivariate Cox regression analysis effectively identifies perinatal factors influencing the age at which distinct abilities are acquired. These were similar as known from previous cross sectional studies. Retrospective data acquistion may lead to a bias because the parental memories change with time. This recommends applying this statistical approach in larger prospective trials. PMID:20205739
Lee, Eunjee; Zhu, Hongtu; Kong, Dehan; Wang, Yalin; Giovanello, Kelly Sullivan; Ibrahim, Joseph G
2015-12-01
The aim of this paper is to develop a Bayesian functional linear Cox regression model (BFLCRM) with both functional and scalar covariates. This new development is motivated by establishing the likelihood of conversion to Alzheimer's disease (AD) in 346 patients with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) and the early markers of conversion. These 346 MCI patients were followed over 48 months, with 161 MCI participants progressing to AD at 48 months. The functional linear Cox regression model was used to establish that functional covariates including hippocampus surface morphology and scalar covariates including brain MRI volumes, cognitive performance (ADAS-Cog), and APOE status can accurately predict time to onset of AD. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFLCRM.
Modern Regression Discontinuity Analysis
ERIC Educational Resources Information Center
Bloom, Howard S.
2012-01-01
This article provides a detailed discussion of the theory and practice of modern regression discontinuity (RD) analysis for estimating the effects of interventions or treatments. Part 1 briefly chronicles the history of RD analysis and summarizes its past applications. Part 2 explains how in theory an RD analysis can identify an average effect of…
Modern Regression Discontinuity Analysis
ERIC Educational Resources Information Center
Bloom, Howard S.
2012-01-01
This article provides a detailed discussion of the theory and practice of modern regression discontinuity (RD) analysis for estimating the effects of interventions or treatments. Part 1 briefly chronicles the history of RD analysis and summarizes its past applications. Part 2 explains how in theory an RD analysis can identify an average effect of…
Xiao, Yongling; Abrahamowicz, Michal
2010-03-30
We propose two bootstrap-based methods to correct the standard errors (SEs) from Cox's model for within-cluster correlation of right-censored event times. The cluster-bootstrap method resamples, with replacement, only the clusters, whereas the two-step bootstrap method resamples (i) the clusters, and (ii) individuals within each selected cluster, with replacement. In simulations, we evaluate both methods and compare them with the existing robust variance estimator and the shared gamma frailty model, which are available in statistical software packages. We simulate clustered event time data, with latent cluster-level random effects, which are ignored in the conventional Cox's model. For cluster-level covariates, both proposed bootstrap methods yield accurate SEs, and type I error rates, and acceptable coverage rates, regardless of the true random effects distribution, and avoid serious variance under-estimation by conventional Cox-based standard errors. However, the two-step bootstrap method over-estimates the variance for individual-level covariates. We also apply the proposed bootstrap methods to obtain confidence bands around flexible estimates of time-dependent effects in a real-life analysis of cluster event times.
Zhu, Lucheng; Luo, Wenhua; Su, Meng; Wei, Hangping; Wei, Juan; Zhang, Xuebang; Zou, Changlin
2013-09-01
The aim of this study was to determine the prognostic factors and their significance in gastric cancer (GC) patients, using the artificial neural network (ANN) and Cox regression hazard (CPH) models. A retrospective analysis was undertaken, including 289 patients with GC who had undergone gastrectomy between 2006 and 2007. According to the CPH analysis, disease stage, peritoneal dissemination, radical surgery and body mass index (BMI) were selected as the significant variables. According to the ANN model, disease stage, radical surgery, serum CA19-9 levels, peritoneal dissemination and BMI were selected as the significant variables. The true prediction of the ANN was 85.3% and of the CPH model 81.9%. In conclusion, the present study demonstrated that the ANN model is a more powerful tool in determining the significant prognostic variables for GC patients, compared to the CPH model. Therefore, this model is recommended for determining the risk factors of such patients.
Dynamics of HPV vaccination initiation in Flanders (Belgium) 2007-2009: a Cox regression model
2011-01-01
Background We investigated dynamic patterns and predictors of HPV vaccination initiation in Flanders (Belgium) by girls aged 12 to 18, between 2007 and 2009, the period immediately after the introduction of the HPV vaccines on the Belgian market. During this period the initiative for vaccination was taken by the girl, her family or the general practitioner/pediatrician/gynecologist. Methods We used a Cox regression model with time constant and time varying predictors to model hazard rates of HPV vaccination initiation. The sample existed of 117,151 female members of the National Alliance of Christian Mutualities, the largest sickness fund in Flanders. Results The study showed that the hazard of HPV vaccination initiation was higher (1) for older girls, (2) for girls with a more favorable socio-economic background, (3) under more generous reimbursement regimes (with this effect being more pronounced for girls with weak socioeconomic backgrounds), (4) for girls that were informed personally about the reimbursement rules. Conclusions When the initiative for HPV vaccination lies with the girls, their families or the physicians (no organized setting) the uptake of the vaccines is affected by both individual and organizational factors. PMID:21672202
Rahman, Mostafizur; Shariff, Asma Ahmad; Shafie, Aziz; Saaid, Rahmah; Tahir, Rohayatimah Md
2015-07-31
Caesarean delivery (C-section) rates have been increasing dramatically in the past decades around the world. This increase has been attributed to multiple factors such as maternal, socio-demographic and institutional factors and is a burning issue of global aspect like in many developed and developing countries. Therefore, this study examines the relationship between mode of delivery and time to event with provider characteristics (i.e., covariates) respectively. The study is based on a total of 1142 delivery cases from four private and four public hospitals maternity wards. Logistic regression and Cox proportional hazard models were the statistical tools of the present study. The logistic regression of multivariate analysis indicated that the risk of having a previous C-section, prolonged labour, higher educational level, mother age 25 years and above, lower order of birth, length of baby more than 45 cm and irregular intake of balanced diet were significantly predict for C-section. With regard to survival time, using the Cox model, fetal distress, previous C-section, mother's age, age at marriage and order of birth were also the most independent risk factors for C-section. By the forward stepwise selection, the study reveals that the most common factors were previous C-section, mother's age and order of birth in both analysis. As shown in the above results, the study suggests that these factors may influence the health-seeking behaviour of women. Findings suggest that program and policies need to address the increase rate of caesarean delivery in Northern region of Bangladesh. Also, for determinant of risk factors, the result of Akaike Information Criterion (AIC) indicated that logistic model is an efficient model.
Multiple linear regression analysis
NASA Technical Reports Server (NTRS)
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Multiple linear regression analysis
NASA Technical Reports Server (NTRS)
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Staley, James R; Jones, Edmund; Kaptoge, Stephen; Butterworth, Adam S; Sweeting, Michael J; Wood, Angela M; Howson, Joanna M M
2017-06-01
Logistic regression is often used instead of Cox regression to analyse genome-wide association studies (GWAS) of single-nucleotide polymorphisms (SNPs) and disease outcomes with cohort and case-cohort designs, as it is less computationally expensive. Although Cox and logistic regression models have been compared previously in cohort studies, this work does not completely cover the GWAS setting nor extend to the case-cohort study design. Here, we evaluated Cox and logistic regression applied to cohort and case-cohort genetic association studies using simulated data and genetic data from the EPIC-CVD study. In the cohort setting, there was a modest improvement in power to detect SNP-disease associations using Cox regression compared with logistic regression, which increased as the disease incidence increased. In contrast, logistic regression had more power than (Prentice weighted) Cox regression in the case-cohort setting. Logistic regression yielded inflated effect estimates (assuming the hazard ratio is the underlying measure of association) for both study designs, especially for SNPs with greater effect on disease. Given logistic regression is substantially more computationally efficient than Cox regression in both settings, we propose a two-step approach to GWAS in cohort and case-cohort studies. First to analyse all SNPs with logistic regression to identify associated variants below a pre-defined P-value threshold, and second to fit Cox regression (appropriately weighted in case-cohort studies) to those identified SNPs to ensure accurate estimation of association with disease.
NASA Technical Reports Server (NTRS)
Kattan, Michael W.; Hess, Kenneth R.; Kattan, Michael W.
1998-01-01
New computationally intensive tools for medical survival analyses include recursive partitioning (also called CART) and artificial neural networks. A challenge that remains is to better understand the behavior of these techniques in effort to know when they will be effective tools. Theoretically they may overcome limitations of the traditional multivariable survival technique, the Cox proportional hazards regression model. Experiments were designed to test whether the new tools would, in practice, overcome these limitations. Two datasets in which theory suggests CART and the neural network should outperform the Cox model were selected. The first was a published leukemia dataset manipulated to have a strong interaction that CART should detect. The second was a published cirrhosis dataset with pronounced nonlinear effects that a neural network should fit. Repeated sampling of 50 training and testing subsets was applied to each technique. The concordance index C was calculated as a measure of predictive accuracy by each technique on the testing dataset. In the interaction dataset, CART outperformed Cox (P less than 0.05) with a C improvement of 0.1 (95% Cl, 0.08 to 0.12). In the nonlinear dataset, the neural network outperformed the Cox model (P less than 0.05), but by a very slight amount (0.015). As predicted by theory, CART and the neural network were able to overcome limitations of the Cox model. Experiments like these are important to increase our understanding of when one of these new techniques will outperform the standard Cox model. Further research is necessary to predict which technique will do best a priori and to assess the magnitude of superiority.
Maximum Likelihood Inference for the Cox Regression Model with Applications to Missing Covariates.
Chen, Ming-Hui; Ibrahim, Joseph G; Shao, Qi-Man
2009-10-01
In this paper, we carry out an in-depth theoretical investigation for existence of maximum likelihood estimates for the Cox model (Cox, 1972, 1975) both in the full data setting as well as in the presence of missing covariate data. The main motivation for this work arises from missing data problems, where models can easily become difficult to estimate with certain missing data configurations or large missing data fractions. We establish necessary and sufficient conditions for existence of the maximum partial likelihood estimate (MPLE) for completely observed data (i.e., no missing data) settings as well as sufficient conditions for existence of the maximum likelihood estimate (MLE) for survival data with missing covariates via a profile likelihood method. Several theorems are given to establish these conditions. A real dataset from a cancer clinical trial is presented to further illustrate the proposed methodology.
Maximum Likelihood Inference for the Cox Regression Model with Applications to Missing Covariates
Chen, Ming-Hui; Ibrahim, Joseph G.; Shao, Qi-Man
2009-01-01
In this paper, we carry out an in-depth theoretical investigation for existence of maximum likelihood estimates for the Cox model (Cox, 1972, 1975) both in the full data setting as well as in the presence of missing covariate data. The main motivation for this work arises from missing data problems, where models can easily become difficult to estimate with certain missing data configurations or large missing data fractions. We establish necessary and sufficient conditions for existence of the maximum partial likelihood estimate (MPLE) for completely observed data (i.e., no missing data) settings as well as sufficient conditions for existence of the maximum likelihood estimate (MLE) for survival data with missing covariates via a profile likelihood method. Several theorems are given to establish these conditions. A real dataset from a cancer clinical trial is presented to further illustrate the proposed methodology. PMID:19802375
Solano, J C Conte; Gracia, A I Domínguez; Felipe, A I García; Calvo, E Rubio; Prados, A Pérez
2010-01-01
Works on labour-related hearing loss have traditionally been centred on the study of noise as the principal cause. The presence of physical and chemical pollutants is very common in the metalworking branch. This article analyses both, together with certain personal habits, with the aim of determining their joint influence on labour-related hearing loss. A sample of 558 workers was analysed using Cox regression with an explicative aim. The character of the cause-effect relations existing between the variables considered is defined with respect to three situations: healthy/altered; recoverable/non-recoverable; with falls in conversational abilities/without falls in conversational abilities. The analysis reflects the fact that metalworking fluids, in the presence of noise, delay the acquisition of different degrees of auditory alteration; an effect contrary to that produced by welding fumes, which accelerate such states. The habit of smoking is recognised as having an influence on the acquisition of an initial acoustic trauma; exposure to noise outside the workplace influences the acquisition of an advanced acoustic trauma; and, on the other hand, the auditory protective equipment provides protection against noise. The antagonistic effect of metalworking fluids and the synergic effect of welding fumes in the face of noise are made evident in relation to these environments, explaining the temporal variation in the evolution of auditory alteration; the influence of tobacco and noise outside the workplace in the acquisition of acoustic trauma are confirmed.
Kössler, Wolfgang; Fiebeler, Anette; Willms, Arnulf; ElAidi, Tina; Klosterhalfen, Bernd; Klinge, Uwe
2011-07-27
Personalised cancer therapy, such as that used for bronchial carcinoma (BC), requires treatment to be adjusted to the patient's status. Individual risk for progression is estimated from clinical and molecular-biological data using translational score systems. Additional molecular information can improve outcome prediction depending on the marker used and the applied algorithm. Two models, one based on regressions and the other on correlations, were used to investigate the effect of combining various items of prognostic information to produce a comprehensive score. This was carried out using correlation coefficients, with options concerning a more plausible selection of variables for modelling, and this is considered better than classical regression analysis. Clinical data concerning 63 BC patients were used to investigate the expression pattern of five tumour-associated proteins. Significant impact on survival was determined using log-rank tests. Significant variables were integrated into a Cox regression model and a new variable called integrative score of individual risk (ISIR), based on Spearman's correlations, was obtained. High tumour stage (TNM) was predictive for poor survival, while CD68 and Gas6 protein expression correlated with a favourable outcome. Cox regression model analysis predicted outcome more accurately than using each variable in isolation, and correctly classified 84% of patients as having a clear risk status. Calculation of the integrated score for an individual risk (ISIR), considering tumour size (T), lymph node status (N), metastasis (M), Gas6 and CD68 identified 82% of patients as having a clear risk status. Combining protein expression analysis of CD68 and GAS6 with T, N and M, using Cox regression or ISIR, improves prediction. Considering the increasing number of molecular markers, subsequent studies will be required to validate translational algorithms for the prognostic potential to select variables with a high prognostic power; the
van Dijk, M R; Steyerberg, E W; Stenning, S P; Dusseldorp, E; Habbema, J D F
2004-03-22
The International Germ Cell Consensus (IGCC) classification identifies good, intermediate and poor prognosis groups among patients with metastatic nonseminomatous germ cell tumours (NSGCT). It uses the risk factors primary site, presence of nonpulmonary visceral metastases and tumour markers alpha-fetoprotein (AFP), human chorionic gonadotrophin (HCG) and lactic dehydrogenase (LDH). The IGCC classification is easy to use and remember, but lacks flexibility. We aimed to examine the extent of any loss in discrimination within the IGCC classification in comparison with alternative modelling by formal weighing of the risk factors. We analysed survival of 3048 NSGCT patients with Cox regression and recursive partitioning for alternative classifications. Good, intermediate and poor prognosis groups were based on predicted 5-year survival. Classifications were further refined by subgrouping within the poor prognosis group. Performance was measured primarily by a bootstrap corrected c-statistic to indicate discriminative ability for future patients. The weights of the risk factors in the alternative classifications differed slightly from the implicit weights in the IGCC classification. Discriminative ability, however, did not increase clearly (IGCC classification, c=0.732; Cox classification, c=0.730; Recursive partitioning classification, c=0.709). Three subgroups could be identified within the poor prognosis groups, resulting in classifications with five prognostic groups and slightly better discriminative ability (c=0.740). In conclusion, the IGCC classification in three prognostic groups is largely supported by Cox regression and recursive partitioning. Cox regression was the most promising tool to define a more refined classification. British Journal of Cancer (2004) 90, 1176-1183. doi:10.1038/sj.bjc.6601665 www.bjcancer.com Published online 24 February 2004
Precision Efficacy Analysis for Regression.
ERIC Educational Resources Information Center
Brooks, Gordon P.
When multiple linear regression is used to develop a prediction model, sample size must be large enough to ensure stable coefficients. If the derivation sample size is inadequate, the model may not predict well for future subjects. The precision efficacy analysis for regression (PEAR) method uses a cross- validity approach to select sample sizes…
Relative risk regression analysis of epidemiologic data.
Prentice, R L
1985-11-01
Relative risk regression methods are described. These methods provide a unified approach to a range of data analysis problems in environmental risk assessment and in the study of disease risk factors more generally. Relative risk regression methods are most readily viewed as an outgrowth of Cox's regression and life model. They can also be viewed as a regression generalization of more classical epidemiologic procedures, such as that due to Mantel and Haenszel. In the context of an epidemiologic cohort study, relative risk regression methods extend conventional survival data methods and binary response (e.g., logistic) regression models by taking explicit account of the time to disease occurrence while allowing arbitrary baseline disease rates, general censorship, and time-varying risk factors. This latter feature is particularly relevant to many environmental risk assessment problems wherein one wishes to relate disease rates at a particular point in time to aspects of a preceding risk factor history. Relative risk regression methods also adapt readily to time-matched case-control studies and to certain less standard designs. The uses of relative risk regression methods are illustrated and the state of development of these procedures is discussed. It is argued that asymptotic partial likelihood estimation techniques are now well developed in the important special case in which the disease rates of interest have interpretations as counting process intensity functions. Estimation of relative risks processes corresponding to disease rates falling outside this class has, however, received limited attention. The general area of relative risk regression model criticism has, as yet, not been thoroughly studied, though a number of statistical groups are studying such features as tests of fit, residuals, diagnostics and graphical procedures. Most such studies have been restricted to exponential form relative risks as have simulation studies of relative risk estimation
Covariate analysis of survival data: a small-sample study of Cox's model
Johnson, M.E.; Tolley, H.D.; Bryson, M.C.; Goldman, A.S.
1982-09-01
Cox's proportional-hazards model is frequently used to adjust for covariate effects in survival-data analysis. The small-sample performances of the maximum partial likelihood estimators of the regression parameters in a two-covariate hazard function model are evaluated with respect to bias, variance, and power in hypothesis tests. Previous Monte Carlo work on the two-sample problem is reviewed.
Shintani, Ayumi K; Girard, Timothy D; Eden, Svetlana K; Arbogast, Patrick G; Moons, Karel G M; Ely, E Wesley
2009-11-01
To examine the bias introduced by using time-fixed methodology to analyze the effects of a time-varying exposure incurred in the intensive care unit. Prospective cohort and Monte Carlo simulation studies. Medical and coronary intensive care units in a university hospital. A total of 224 mechanically ventilated patients. Part I was a case study analyzing the association between delirium in the intensive care unit (exposure variable) and outcomes (intensive care unit length of stay and 6-mo mortality) in a prospective cohort study. Part II was a Monte Carlo simulation generating 16,000 data sets wherein the true associations between delirium and outcomes were known before analysis. In both parts, we assessed associations between delirium in the intensive care unit and outcomes (intensive care unit length of stay and mortality), using time-fixed vs. time-varying Cox regression methodology. In the case study, delirium analyzed as a time-fixed variable was associated with a delayed intensive care unit discharge (adjusted hazard ratio = 1.9, 95% confidence interval, 1.3-2.7, p < .001), but no association was noted using a time-varying method (adjusted hazard ratio = 1.1, 95% confidence interval = 0.7-1.6, p = .70). Alternatively, delirium analyzed as a time-fixed variable was not associated with 6-mo mortality (adjusted hazard ratio = 2.9, 95% confidence interval, 0.9-5.0, p = .09), whereas delirium analyzed as a time-varying variable was associated with increased mortality (adjusted hazard ratio = 3.2, 95% confidence interval, 1.4-7.7, p = .008). In the simulation study, time-fixed methods produced erroneous results in 97.1% of the data sets with no true association; time-varying methods produced erroneous results in only 3.7%. Similarly, time-fixed methods produced biased results when a true association was present, whereas time-varying methods produced accurate results. Studies using a time-fixed analytic approach to understand relationships between exposures and
Zemmour, Christophe; Bertucci, François; Finetti, Pascal; Chetrit, Bernard; Birnbaum, Daniel; Filleron, Thomas; Boher, Jean-Marie
2015-01-01
BACKGROUND DNA microarray studies identified gene expression signatures predictive of metastatic relapse in early breast cancer. Standard feature selection procedures applied to reduce the set of predictive genes did not take into account the correlation between genes. In this paper, we studied the performances of three high-dimensional regression methods – CoxBoost, LASSO (Least Absolute Shrinkage and Selection Operator), and Elastic net – to identify prognostic signatures in patients with early breast cancer. METHODS We analyzed three public retrospective datasets, including a total of 384 patients with axillary lymph node-negative breast cancer. The Amsterdam van’t Veer’s training set of 78 patients was used to determine the optimal gene sets and classifiers using sensitivity thresholds resulting in mis-classification of no more than 10% of the poor-prognosis group. To ensure the comparability between different methods, an automatic selection procedure was used to determine the number of genes included in each model. The van de Vijver’s and Desmedt’s datasets were used as validation sets to evaluate separately the prognostic performances of our classifiers. The results were compared to the original Amsterdam 70-gene classifier. RESULTS The automatic selection procedure reduced the number of predictive genes up to a minimum of six genes. In the two validation sets, the three models (Elastic net, LASSO, and CoxBoost) led to the definition of genomic classifiers predicting the 5-year metastatic status with similar performances, with respective 59, 56, and 54% accuracy, 83, 75, and 83% sensitivity, and 53, 52, and 48% specificity in the Desmedt’s dataset. In comparison, the Amsterdam 70-gene signature showed 45% accuracy, 97% sensitivity, and 34% specificity. The gene overlap and the classification concordance between the three classifiers were high. All the classifiers added significant prognostic information to that provided by the traditional
Accounting for covariate measurement error in a Cox model analysis of recurrence of depression.
Liu, K; Mazumdar, S; Stone, R A; Dew, M A; Houck, P R; Reynolds, C F
2001-01-01
When a covariate measured with error is used as a predictor in a survival analysis using the Cox model, the parameter estimate is usually biased. In clinical research, covariates measured without error such as treatment procedure or sex are often used in conjunction with a covariate measured with error. In a randomized clinical trial of two types of treatments, we account for the measurement error in the covariate, log-transformed total rapid eye movement (REM) activity counts, in a Cox model analysis of the time to recurrence of major depression in an elderly population. Regression calibration and two variants of a likelihood-based approach are used to account for measurement error. The likelihood-based approach is extended to account for the correlation between replicate measures of the covariate. Using the replicate data decreases the standard error of the parameter estimate for log(total REM) counts while maintaining the bias reduction of the estimate. We conclude that covariate measurement error and the correlation between replicates can affect results in a Cox model analysis and should be accounted for. In the depression data, these methods render comparable results that have less bias than the results when measurement error is ignored.
Ali, M Sanni; Groenwold, Rolf H H; Belitser, Svetlana V; Souverein, Patrick C; Martín, Elisa; Gatto, Nicolle M; Huerta, Consuelo; Gardarsdottir, Helga; Roes, Kit C B; Hoes, Arno W; de Boer, Antonius; Klungel, Olaf H
2016-03-01
Observational studies including time-varying treatments are prone to confounding. We compared time-varying Cox regression analysis, propensity score (PS) methods, and marginal structural models (MSMs) in a study of antidepressant [selective serotonin reuptake inhibitors (SSRIs)] use and the risk of hip fracture. A cohort of patients with a first prescription for antidepressants (SSRI or tricyclic antidepressants) was extracted from the Dutch Mondriaan and Spanish Base de datos para la Investigación Farmacoepidemiológica en Atención Primaria (BIFAP) general practice databases for the period 2001-2009. The net (total) effect of SSRI versus no SSRI on the risk of hip fracture was estimated using time-varying Cox regression, stratification and covariate adjustment using the PS, and MSM. In MSM, censoring was accounted for by inverse probability of censoring weights. The crude hazard ratio (HR) of SSRI use versus no SSRI use on hip fracture was 1.75 (95%CI: 1.12, 2.72) in Mondriaan and 2.09 (1.89, 2.32) in BIFAP. After confounding adjustment using time-varying Cox regression, stratification, and covariate adjustment using the PS, HRs increased in Mondriaan [2.59 (1.63, 4.12), 2.64 (1.63, 4.25), and 2.82 (1.63, 4.25), respectively] and decreased in BIFAP [1.56 (1.40, 1.73), 1.54 (1.39, 1.71), and 1.61 (1.45, 1.78), respectively]. MSMs with stabilized weights yielded HR 2.15 (1.30, 3.55) in Mondriaan and 1.63 (1.28, 2.07) in BIFAP when accounting for censoring and 2.13 (1.32, 3.45) in Mondriaan and 1.66 (1.30, 2.12) in BIFAP without accounting for censoring. In this empirical study, differences between the different methods to control for time-dependent confounding were small. The observed differences in treatment effect estimates between the databases are likely attributable to different confounding information in the datasets, illustrating that adequate information on (time-varying) confounding is crucial to prevent bias. Copyright © 2016 John Wiley & Sons, Ltd.
Analysis of COX2 mutants reveals cytochrome oxidase subassemblies in yeast
2005-01-01
Cytochrome oxidase catalyses the reduction of oxygen to water. The mitochondrial enzyme contains up to 13 subunits, 11 in yeast, of which three, Cox1p, Cox2p and Cox3p, are mitochondrially encoded. The assembly pathway of this complex is still poorly understood. Its study in yeast has been so far impeded by the rapid turnover of unassembled subunits of the enzyme. In the present study, immunoblot analysis of blue native gels of yeast wild-type and Cox2p mutants revealed five cytochrome oxidase complexes or subcomplexes: a, b, c, d and f; a is likely to be the fully assembled enzyme; b lacks Cox6ap; d contains Cox7p and/or Cox7ap; f represents unassembled Cox1p; and c, observed only in the Cox2p mutants, contains Cox1p, Cox3p, Cox5p and Cox6p and lacks the other subunits. The identification of these novel cytochrome oxidase subcomplexes should encourage the reexamination of other yeast mutants. PMID:15921494
Common pitfalls in statistical analysis: Logistic regression.
Ranganathan, Priya; Pramesh, C S; Aggarwal, Rakesh
2017-01-01
Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary (dichotomous). In this article, we discuss logistic regression analysis and the limitations of this technique.
Principal component regression analysis with SPSS.
Liu, R X; Kuang, J; Gong, Q; Hou, X L
2003-06-01
The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.
Regression Analysis by Example. 5th Edition
ERIC Educational Resources Information Center
Chatterjee, Samprit; Hadi, Ali S.
2012-01-01
Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…
Regression Analysis by Example. 5th Edition
ERIC Educational Resources Information Center
Chatterjee, Samprit; Hadi, Ali S.
2012-01-01
Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. "Regression Analysis by Example, Fifth Edition" has been expanded and thoroughly…
Ternès, Nils; Rotolo, Federico; Michiels, Stefan
2016-07-10
Correct selection of prognostic biomarkers among multiple candidates is becoming increasingly challenging as the dimensionality of biological data becomes higher. Therefore, minimizing the false discovery rate (FDR) is of primary importance, while a low false negative rate (FNR) is a complementary measure. The lasso is a popular selection method in Cox regression, but its results depend heavily on the penalty parameter λ. Usually, λ is chosen using maximum cross-validated log-likelihood (max-cvl). However, this method has often a very high FDR. We review methods for a more conservative choice of λ. We propose an empirical extension of the cvl by adding a penalization term, which trades off between the goodness-of-fit and the parsimony of the model, leading to the selection of fewer biomarkers and, as we show, to the reduction of the FDR without large increase in FNR. We conducted a simulation study considering null and moderately sparse alternative scenarios and compared our approach with the standard lasso and 10 other competitors: Akaike information criterion (AIC), corrected AIC, Bayesian information criterion (BIC), extended BIC, Hannan and Quinn information criterion (HQIC), risk information criterion (RIC), one-standard-error rule, adaptive lasso, stability selection, and percentile lasso. Our extension achieved the best compromise across all the scenarios between a reduction of the FDR and a limited raise of the FNR, followed by the AIC, the RIC, and the adaptive lasso, which performed well in some settings. We illustrate the methods using gene expression data of 523 breast cancer patients. In conclusion, we propose to apply our extension to the lasso whenever a stringent FDR with a limited FNR is targeted. Copyright © 2016 John Wiley & Sons, Ltd.
Does Cox analysis of a randomized survival study yield a causal treatment effect?
Aalen, Odd O; Cook, Richard J; Røysland, Kjetil
2015-10-01
Statistical methods for survival analysis play a central role in the assessment of treatment effects in randomized clinical trials in cardiovascular disease, cancer, and many other fields. The most common approach to analysis involves fitting a Cox regression model including a treatment indicator, and basing inference on the large sample properties of the regression coefficient estimator. Despite the fact that treatment assignment is randomized, the hazard ratio is not a quantity which admits a causal interpretation in the case of unmodelled heterogeneity. This problem arises because the risk sets beyond the first event time are comprised of the subset of individuals who have not previously failed. The balance in the distribution of potential confounders between treatment arms is lost by this implicit conditioning, whether or not censoring is present. Thus while the Cox model may be used as a basis for valid tests of the null hypotheses of no treatment effect if robust variance estimates are used, modeling frameworks more compatible with causal reasoning may be preferrable in general for estimation.
Regression analysis of cytopathological data
Whittemore, A.S.; McLarty, J.W.; Fortson, N.; Anderson, K.
1982-12-01
Epithelial cells from the human body are frequently labelled according to one of several ordered levels of abnormality, ranging from normal to malignant. The label of the most abnormal cell in a specimen determines the score for the specimen. This paper presents a model for the regression of specimen scores against continuous and discrete variables, as in host exposure to carcinogens. Application to data and tests for adequacy of model fit are illustrated using sputum specimens obtained from a cohort of former asbestos workers.
Lee, Paul H.
2016-01-01
Healthy adults are advised to perform at least 150 min of moderate-intensity physical activity weekly, but this advice is based on studies using self-reports of questionable validity. This study examined the dose-response relationship of accelerometer-measured physical activity and sedentary behaviors on all-cause mortality using segmented Cox regression to empirically determine the break-points of the dose-response relationship. Data from 7006 adult participants aged 18 or above in the National Health and Nutrition Examination Survey waves 2003–2004 and 2005–2006 were included in the analysis and linked with death certificate data using a probabilistic matching approach in the National Death Index through December 31, 2011. Physical activity and sedentary behavior were measured using ActiGraph model 7164 accelerometer over the right hip for 7 consecutive days. Each minute with accelerometer count <100; 1952–5724; and ≥5725 were classified as sedentary, moderate-intensity physical activity, and vigorous-intensity physical activity, respectively. Segmented Cox regression was used to estimate the hazard ratio (HR) of time spent in sedentary behaviors, moderate-intensity physical activity, and vigorous-intensity physical activity and all-cause mortality, adjusted for demographic characteristics, health behaviors, and health conditions. Data were analyzed in 2016. During 47,119 person-year of follow-up, 608 deaths occurred. Each additional hour per day of sedentary behaviors was associated with a HR of 1.15 (95% CI 1.01, 1.31) among participants who spend at least 10.9 h per day on sedentary behaviors, and each additional minute per day spent on moderate-intensity physical activity was associated with a HR of 0.94 (95% CI 0.91, 0.96) among participants with daily moderate-intensity physical activity ≤14.1 min. Associations of moderate physical activity and sedentary behaviors on all-cause mortality were independent of each other. To conclude, evidence from
Cox models survival analysis based on breast cancer treatments.
Abadi, Alireza; Yavari, Parvin; Dehghani-Arani, Monireh; Alavi-Majd, Hamid; Ghasemi, Erfan; Amanpour, Farzaneh; Bajdik, Chris
2014-01-01
The aim of this study is to evaluate the association between different treatments and survival time of breast cancer patients using either standard Cox model or stratified Cox model. The study was conducted on 15830 women diagnosed with breast cancer in British Columbia, Canada. They were divided into eight groups according to patients' ages and stage of disease Either Cox's PH model or stratified Cox model was fitted to each group according to the PH assumption and tested using Schoenfeld residuals. The data show that in the group of patients under age 50 years old and over age 50 with stage I cancer, the highest hazard was related to radiotherapy (HR= 3.15, CI: 1.85-5.35) and chemotherapy (HR= 3, CI: 2.29- 3.93) respectively. For both groups of patients with stage II cancer, the highest risk was related to radiotherapy (HR=3.02, CI: 2.26-4.03) (HR=2.16, CI:1.85-2.52). For both groups of patients with stage III cancer, the highest risk was for surgery (HR=0.49, CI: 0.33-0.73), (HR=0.45, CI: 0.36-0.57). For patients of age 50 years or less with stage IV cancer, none of the treatments were statistically significant. In group of patients over age 50 years old with stage IV cancer, the highest hazard was related to surgery (HR=0.64, CI: 0.53-0.78). The results of this study show that for patients with stage I and II breast cancer, radiotherapy and chemotherapy had the highest hazard; for patients with stage III and IV breast cancer, the highest hazard was associated with treatment surgery.
Regression Analysis and the Sociological Imagination
ERIC Educational Resources Information Center
De Maio, Fernando
2014-01-01
Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.
Common pitfalls in statistical analysis: Logistic regression
Ranganathan, Priya; Pramesh, C. S.; Aggarwal, Rakesh
2017-01-01
Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary (dichotomous). In this article, we discuss logistic regression analysis and the limitations of this technique. PMID:28828311
Regression Analysis: Legal Applications in Institutional Research
ERIC Educational Resources Information Center
Frizell, Julie A.; Shippen, Benjamin S., Jr.; Luna, Andrew L.
2008-01-01
This article reviews multiple regression analysis, describes how its results should be interpreted, and instructs institutional researchers on how to conduct such analyses using an example focused on faculty pay equity between men and women. The use of multiple regression analysis will be presented as a method with which to compare salaries of…
Regression Analysis and the Sociological Imagination
ERIC Educational Resources Information Center
De Maio, Fernando
2014-01-01
Regression analysis is an important aspect of most introductory statistics courses in sociology but is often presented in contexts divorced from the central concerns that bring students into the discipline. Consequently, we present five lesson ideas that emerge from a regression analysis of income inequality and mortality in the USA and Canada.
Regression Analysis: Legal Applications in Institutional Research
ERIC Educational Resources Information Center
Frizell, Julie A.; Shippen, Benjamin S., Jr.; Luna, Andrew L.
2008-01-01
This article reviews multiple regression analysis, describes how its results should be interpreted, and instructs institutional researchers on how to conduct such analyses using an example focused on faculty pay equity between men and women. The use of multiple regression analysis will be presented as a method with which to compare salaries of…
Duchesne, Thierry; Abdous, Belkacem; Lowndes, Catherine M; Alary, Michel
2014-01-07
Large-scale public health interventions with rapid scale-up are increasingly being implemented worldwide. Such implementation allows for a large target population to be reached in a short period of time. But when the time comes to investigate the effectiveness of these interventions, the rapid scale-up creates several methodological challenges, such as the lack of baseline data and the absence of control groups. One example of such an intervention is Avahan, the India HIV/AIDS initiative of the Bill & Melinda Gates Foundation. One question of interest is the effect of Avahan on condom use by female sex workers with their clients. By retrospectively reconstructing condom use and sex work history from survey data, it is possible to estimate how condom use rates evolve over time. However formal inference about how this rate changes at a given point in calendar time remains challenging. We propose a new statistical procedure based on a mixture of binomial regression and Cox regression. We compare this new method to an existing approach based on generalized estimating equations through simulations and application to Indian data. Both methods are unbiased, but the proposed method is more powerful than the existing method, especially when initial condom use is high. When applied to the Indian data, the new method mostly agrees with the existing method, but seems to have corrected some implausible results of the latter in a few districts. We also show how the new method can be used to analyze the data of all districts combined. The use of both methods can be recommended for exploratory data analysis. However for formal statistical inference, the new method has better power.
Cox Models Survival Analysis Based on Breast Cancer Treatments
Abadi, Alireza; Yavari, Parvin; Dehghani-Arani, Monireh; Alavi-Majd, Hamid; Ghasemi, Erfan; Amanpour, Farzaneh; Bajdik, Chris
2014-01-01
Background The aim of this study is to evaluate the association between different treatments and survival time of breast cancer patients using either standard Cox model or stratified Cox model. Methods The study was conducted on 15830 women diagnosed with breast cancer in British Columbia, Canada. They were divided into eight groups according to patients’ ages and stage of disease Either Cox’s PH model or stratified Cox model was fitted to each group according to the PH assumption and tested using Schoenfeld residuals. Results The data show that in the group of patients under age 50 years old and over age 50 with stage I cancer, the highest hazard was related to radiotherapy (HR= 3.15, CI: 1.85-5.35) and chemotherapy (HR= 3, CI: 2.29- 3.93) respectively. For both groups of patients with stage II cancer, the highest risk was related to radiotherapy (HR=3.02, CI: 2.26-4.03) (HR=2.16, CI:1.85-2.52). For both groups of patients with stage III cancer, the highest risk was for surgery (HR=0.49, CI: 0.33-0.73), (HR=0.45, CI: 0.36-0.57). For patients of age 50 years or less with stage IV cancer, none of the treatments were statistically significant. In group of patients over age 50 years old with stage IV cancer, the highest hazard was related to surgery (HR=0.64, CI: 0.53-0.78). Conclusion The results of this study show that for patients with stage I and II breast cancer, radiotherapy and chemotherapy had the highest hazard; for patients with stage III and IV breast cancer, the highest hazard was associated with treatment surgery. PMID:25250162
Use of Multiple Correlation Analysis and Multiple Regression Analysis.
ERIC Educational Resources Information Center
Huberty, Carl J.; Petoskey, Martha D.
1999-01-01
Distinguishes between multiple correlation and multiple regression analysis. Illustrates suggested information reporting methods and reviews the use of regression methods when dealing with problems of missing data. (SK)
Box-Cox Mixed Logit Model for Travel Behavior Analysis
NASA Astrophysics Data System (ADS)
Orro, Alfonso; Novales, Margarita; Benitez, Francisco G.
2010-09-01
To represent the behavior of travelers when they are deciding how they are going to get to their destination, discrete choice models, based on the random utility theory, have become one of the most widely used tools. The field in which these models were developed was halfway between econometrics and transport engineering, although the latter now constitutes one of their principal areas of application. In the transport field, they have mainly been applied to mode choice, but also to the selection of destination, route, and other important decisions such as the vehicle ownership. In usual practice, the most frequently employed discrete choice models implement a fixed coefficient utility function that is linear in the parameters. The principal aim of this paper is to present the viability of specifying utility functions with random coefficients that are nonlinear in the parameters, in applications of discrete choice models to transport. Nonlinear specifications in the parameters were present in discrete choice theory at its outset, although they have seldom been used in practice until recently. The specification of random coefficients, however, began with the probit and the hedonic models in the 1970s, and, after a period of apparent little practical interest, has burgeoned into a field of intense activity in recent years with the new generation of mixed logit models. In this communication, we present a Box-Cox mixed logit model, original of the authors. It includes the estimation of the Box-Cox exponents in addition to the parameters of the random coefficients distribution. Probability of choose an alternative is an integral that will be calculated by simulation. The estimation of the model is carried out by maximizing the simulated log-likelihood of a sample of observed individual choices between alternatives. The differences between the predictions yielded by models that are inconsistent with real behavior have been studied with simulation experiments.
Understanding logistic regression analysis through example.
Ely, J W; Dawson, J D; Mehr, D R; Burns, T L
1996-02-01
Logistic regression is a valuable statistical tool that is often used in primary care research. When researchers explore the association between a possible risk factor and a disease, they attempt to control the effects of extraneous factors (confounders) that can obscure the true association. Using logistic regression, researchers can simultaneously control for the effects of multiple confounders. When investigators use logistic regression, they make subjective decisions about which factors to include in the analysis and in the final predictive model. Critical readers must understand basic concepts of logistic regression and potential problems with its use before they can accurately interpret study results. This article uses a familiar example to explain the principles of logistic regression to make it understandable to nonstatisticians.
Pedersen, L; Holck, S; Schiødt, T; Zedeler, K; Mouridsen, H T
1994-01-01
In this study of 136 breast cancers with medullary features (MC), registered in the Danish Breast Cancer Cooperative Group (DBCG) from 1982 to 1987, we confirmed the prognostic importance of a new definition of medullary carcinoma of the breast (MC newdef) which was recently proposed by us, deduced from a previous study of a corresponding tumour material (DBCG 77-82). However, the individual histological criteria did not have the same prognostic importance as in our previous study, although prognostic trends were the same. To further improve and validate the diagnostic criteria, we combined the two populations and performed a multivariate Cox regression analysis. In the final Cox model, four histological parameters retained positive prognostic importance: (1) predominantly syncytial growth pattern, (2) no tubular component, (3) diffuse stromal infiltration with mononuclear cells and (4) sparse necrosis. We propose that these criteria are emphasized in the histological diagnosis of medullary carcinoma of the breast.
Beretta, C; Garavaglia, G; Cavalli, M
2005-10-01
We report on the inhibitory activity of the NSAIDs meloxicam, carprofen, phenylbutazone and flunixin, on blood cyclooxygenases in the horse using in vitro enzyme-linked assays. As expected, comparison of IC50 indicated that meloxicam and carprofen are more selective inhibitors of COX-2 than phenylbutazone and flunixin; meloxicam was the most advantageous for horses of four NSAIDs examined. However at IC80, phenylbutazone (+134.4%) and flunixin (+29.7%) had greater COX-2 selectivity than at IC50, and meloxicam (-41.2%) and carprofen (-12.9%) had lower COX-2 selectivity than at IC50. We therefore propose that the selectivity of NSAIDs should be assessed at the 80% as well as 50% inhibition level.
Docking studies on NSAID/COX-2 isozyme complexes using Contact Statistics analysis
NASA Astrophysics Data System (ADS)
Ermondi, Giuseppe; Caron, Giulia; Lawrence, Raelene; Longo, Dario
2004-11-01
The selective inhibition of COX-2 isozymes should lead to a new generation of NSAIDs with significantly reduced side effects; e.g. celecoxib (Celebrex®) and rofecoxib (Vioxx®). To obtain inhibitors with higher selectivity it has become essential to gain additional insight into the details of the interactions between COX isozymes and NSAIDs. Although X-ray structures of COX-2 complexed with a small number of ligands are available, experimental data are missing for two well-known selective COX-2 inhibitors (rofecoxib and nimesulide) and docking results reported are controversial. We use a combination of a traditional docking procedure with a new computational tool (Contact Statistics analysis) that identifies the best orientation among a number of solutions to shed some light on this topic.
Liu, Ke; Chen, Kewei; Yao, Li; Guo, Xiaojuan
2017-01-01
Mild cognitive impairment (MCI) represents a transitional stage from normal aging to Alzheimer’s disease (AD) and corresponds to a higher risk of developing AD. Thus, it is necessary to explore and predict the onset of AD in MCI stage. In this study, we propose a combination of independent component analysis (ICA) and the multivariate Cox proportional hazards regression model to investigate promising risk factors associated with MCI conversion among 126 MCI converters and 108 MCI non-converters from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Using structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data, we extracted brain networks from AD and normal control groups via ICA and then constructed Cox models that included network-based neuroimaging factors for the MCI group. We carried out five separate Cox analyses and the two-modality neuroimaging Cox model identified three significant network-based risk factors with higher prediction performance (accuracy = 73.50%) than those in either single-modality model (accuracy = 68.80%). Additionally, the results of the comprehensive Cox model, including significant neuroimaging factors and clinical variables, demonstrated that MCI individuals with reduced gray matter volume in a temporal lobe-related network of structural MRI [hazard ratio (HR) = 8.29E-05 (95% confidence interval (CI), 5.10E- 07 ~ 0.013)], low glucose metabolism in the posterior default mode network based on FDG-PET [HR = 0.066 (95% CI, 4.63E-03 ~ 0.928)], positive apolipoprotein E ε4-status [HR = 1. 988 (95% CI, 1.531 ~ 2.581)], increased Alzheimer’s Disease Assessment Scale-Cognitive Subscale scores [HR = 1.100 (95% CI, 1.059 ~ 1.144)] and Sum of Boxes of Clinical Dementia Rating scores [HR = 1.622 (95% CI, 1.364 ~ 1.930)] were more likely to convert to AD within 36 months after baselines. These significant risk factors in such comprehensive Cox model had the best prediction
Association between COX-2 -1195G>A polymorphism and gastrointestinal cancer risk: A meta-analysis
Zhang, Xiao-Wei; Li, Jun; Jiang, Yu-Xing; Chen, Yu-Xiang
2017-01-01
AIM To perform a meta-analysis to investigate the association between cyclooxygenase-2 (COX-2) -1195G>A gene polymorphism and gastrointestinal cancers. METHODS Publications related to the COX-2 -1195G>A gene polymorphism and gastrointestinal cancers published before July 2016 were retrieved from PubMed, EMBASE, Web of Science, China Biological Medicine Database, China National Knowledge Infrastructure, and CQVIP Database. Meta-analysis was performed using Stata11.0 software. The strength of the association was evaluated by calculating the combined odds ratios (ORs) and the corresponding 95%CIs. The retrieved publications were excluded or included one by one for sensitivity analysis. In addition, the funnel plot, Begg’s rank correlation test, and Egger’s linear regression method were applied to analyse whether the included publications had publication bias. RESULTS A total of 24 publications related to the COX-2 -1195G>A gene polymorphism were included, including 28 studies involving 11043 cases and 18008 controls. The meta-analysis results showed that the COX-2 -1195G>A gene polymorphism significantly correlated with an increased risk of gastrointestinal cancers, particularly gastric cancer (A vs G: OR = 1.35; AA/AG vs GG: OR = 1.54; AA vs GG/AG: OR = 1.43; AA vs GG: OR = 1.80; AG vs GG: OR = 1.35). Compared to the Caucasian population in America and Europe, the COX-2 -1195G>A gene polymorphism in the Asian population (A vs G: OR = 1.30; AA/AG vs GG: OR = 1.50; AA vs GG/AG: OR = 1.35; AA vs GG: OR = 1.71; AG vs GG: OR = 1.37) significantly increased gastrointestinal cancer risk. The sensitivity analysis (P < 0.05) and the false positive report probability (P < 0.2) confirmed the reliability of the results. CONCLUSION The results showed that the COX-2 -1195G>A gene polymorphism might be a potential risk factor for gastrointestinal cancers. Further validation by a large homogeneous study is warranted. PMID:28405152
Xu, Feng; Li, Mengxin; Zhang, Chao; Cui, Jianxiu; Liu, Jun; Li, Jie; Jiang, Hongchuan
2017-01-01
The prognostic significance of COX-2 in patients with breast cancer remains controversial. The aims of our meta-analysis are to evaluate its association with clinicopathological characteristics and prognostic value in patients with breast cancer. PubMed, EMBASE, Web of Science, the Ovid Database and Grey literature were systematically searched up to May 2016. Twenty-one studies including 6739 patients with breast cancer were analyzed. The meta-analysis indicated that the incidence difference of COX-2 expression was significant when comparing the lymph node positive group to negative group (OR = 1.76, 95% CI [1.30, 2.39]) and the tumor size ≥ 2cm group to the tumor size < 2cm group (OR = 1.71, 95% CI [1.22, 2.39]). None of other clinicopathological parameters such as the ER status, PR status, HER2 status and the vascular invasion status were associated with COX-2 overexpression. The detection of COX-2 was significantly correlated with the disease-free survival (DFS) of patients (HR = 1.58, 95% CI [1.23, 2.03]) and the overall survival (OS) of patients (HR = 1.51, 95% CI [1.31, 1.72]). Our meta-analysis demonstrates that the presence of high levels of COX-2 is associated with poor prognosis for breast cancer patients and predicts bigger tumor size and lymph node metastasis. PMID:27999206
Robust Mediation Analysis Based on Median Regression
Yuan, Ying; MacKinnon, David P.
2014-01-01
Mediation analysis has many applications in psychology and the social sciences. The most prevalent methods typically assume that the error distribution is normal and homoscedastic. However, this assumption may rarely be met in practice, which can affect the validity of the mediation analysis. To address this problem, we propose robust mediation analysis based on median regression. Our approach is robust to various departures from the assumption of homoscedasticity and normality, including heavy-tailed, skewed, contaminated, and heteroscedastic distributions. Simulation studies show that under these circumstances, the proposed method is more efficient and powerful than standard mediation analysis. We further extend the proposed robust method to multilevel mediation analysis, and demonstrate through simulation studies that the new approach outperforms the standard multilevel mediation analysis. We illustrate the proposed method using data from a program designed to increase reemployment and enhance mental health of job seekers. PMID:24079925
Analysis of the cytochrome c oxidase subunit II (COX2) gene in giant panda, Ailuropoda melanoleuca.
Ling, S S; Zhu, Y; Lan, D; Li, D S; Pang, H Z; Wang, Y; Li, D Y; Wei, R P; Zhang, H M; Wang, C D; Hu, Y D
2017-01-23
The giant panda, Ailuropoda melanoleuca (Ursidae), has a unique bamboo-based diet; however, this low-energy intake has been sufficient to maintain the metabolic processes of this species since the fourth ice age. As mitochondria are the main sites for energy metabolism in animals, the protein-coding genes involved in mitochondrial respiratory chains, particularly cytochrome c oxidase subunit II (COX2), which is the rate-limiting enzyme in electron transfer, could play an important role in giant panda metabolism. Therefore, the present study aimed to isolate, sequence, and analyze the COX2 DNA from individuals kept at the Giant Panda Protection and Research Center, China, and compare these sequences with those of the other Ursidae family members. Multiple sequence alignment showed that the COX2 gene had three point mutations that defined three haplotypes, with 60% of the sequences corresponding to haplotype I. The neutrality tests revealed that the COX2 gene was conserved throughout evolution, and the maximum likelihood phylogenetic analysis, using homologous sequences from other Ursidae species, showed clustering of the COX2 sequences of giant pandas, suggesting that this gene evolved differently in them.
Han, Jeong A.; Kim, Jong-Il
2017-01-01
We have previously reported that NS-398, a cyclooxygenase-2 (COX-2)–selective inhibitor, inhibited replicative cellular senescence in human dermal fibroblasts and skin aging in hairless mice. In contrast, celecoxib, another COX-2–selective inhibitor, and aspirin, a non-selective COX inhibitor, accelerated the senescence and aging. To figure out causal factors for the senescence-modulating effect of the inhibitors, we here performed cDNA microarray experiment and subsequent Gene Set Enrichment Analysis. The data showed that several senescence-related gene sets were regulated by the inhibitor treatment. NS-398 up-regulated gene sets involved in the tumor necrosis factor β receptor pathway and the fructose and mannose metabolism, whereas it down-regulated a gene set involved in protein secretion. Celecoxib up-regulated gene sets involved in G2M checkpoint and E2F targets. Aspirin up-regulated the gene set involved in protein secretion, and down-regulated gene sets involved in RNA transcription. These results suggest that COX inhibitors modulate cellular senescence by different mechanisms and will provide useful information to understand senescence-modulating mechanisms of COX inhibitors. PMID:28638310
Han, Jeong A; Kim, Jong-Il
2017-06-01
We have previously reported that NS-398, a cyclooxygenase-2 (COX-2)-selective inhibitor, inhibited replicative cellular senescence in human dermal fibroblasts and skin aging in hairless mice. In contrast, celecoxib, another COX-2-selective inhibitor, and aspirin, a non-selective COX inhibitor, accelerated the senescence and aging. To figure out causal factors for the senescence-modulating effect of the inhibitors, we here performed cDNA microarray experiment and subsequent Gene Set Enrichment Analysis. The data showed that several senescence-related gene sets were regulated by the inhibitor treatment. NS-398 up-regulated gene sets involved in the tumor necrosis factor β receptor pathway and the fructose and mannose metabolism, whereas it down-regulated a gene set involved in protein secretion. Celecoxib up-regulated gene sets involved in G2M checkpoint and E2F targets. Aspirin up-regulated the gene set involved in protein secretion, and down-regulated gene sets involved in RNA transcription. These results suggest that COX inhibitors modulate cellular senescence by different mechanisms and will provide useful information to understand senescence-modulating mechanisms of COX inhibitors.
NASA Astrophysics Data System (ADS)
Mallakpour, Iman; Villarini, Gabriele; Jones, Michael P.; Smith, James A.
2017-08-01
The central United States is plagued by frequent catastrophic flooding, such as the flood events of 1993, 2008, 2011, 2013, 2014 and 2016. The goal of this study is to examine whether it is possible to describe the occurrence of flood and heavy precipitation events at the sub-seasonal scale in terms of variations in the climate system. Daily streamflow and precipitation time series over the central United States (defined here to include North Dakota, South Dakota, Nebraska, Kansas, Missouri, Iowa, Minnesota, Wisconsin, Illinois, West Virginia, Kentucky, Ohio, Indiana, and Michigan) are used in this study. We model the occurrence/non-occurrence of a flood and heavy precipitation event over time using regression models based on Cox processes, which can be viewed as a generalization of Poisson processes. Rather than assuming that an event (i.e., flooding or precipitation) occurs independently of the occurrence of the previous one (as in Poisson processes), Cox processes allow us to account for the potential presence of temporal clustering, which manifests itself in an alternation of quiet and active periods. Here we model the occurrence/non-occurrence of flood and heavy precipitation events using two climate indices as time-varying covariates: the Arctic Oscillation (AO) and the Pacific-North American pattern (PNA). We find that AO and/or PNA are important predictors in explaining the temporal clustering in flood occurrences in over 78% of the stream gages we considered. Similar results are obtained when working with heavy precipitation events. Analyses of the sensitivity of the results to different thresholds used to identify events lead to the same conclusions. The findings of this work highlight that variations in the climate system play a critical role in explaining the occurrence of flood and heavy precipitation events at the sub-seasonal scale over the central United States.
A method for nonlinear exponential regression analysis
NASA Technical Reports Server (NTRS)
Junkin, B. G.
1971-01-01
A computer-oriented technique is presented for performing a nonlinear exponential regression analysis on decay-type experimental data. The technique involves the least squares procedure wherein the nonlinear problem is linearized by expansion in a Taylor series. A linear curve fitting procedure for determining the initial nominal estimates for the unknown exponential model parameters is included as an integral part of the technique. A correction matrix was derived and then applied to the nominal estimate to produce an improved set of model parameters. The solution cycle is repeated until some predetermined criterion is satisfied.
Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin
2013-10-15
In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology.
Park, Sang Hyun; Eom, Keeseon S.; Park, Min Sun; Kwon, Oh Kyoung; Kim, Hyo Sun
2013-01-01
Diphyllobothrium nihonkaiense has been reported in Korea as Diphyllobothrium latum because of their close morphologic resemblance. We have identified a human case of D. nihonkaiense infection using the mitochondrial cytochrome c oxidase subunit I (cox1) gene sequence analysis. On 18 February 2012, a patient who had consumed raw fish a month earlier visited our outpatient clinic with a long tapeworm parasite excreted in the feces. The body of the segmented worm was 2 m long and divided into the scolex (head) and proglottids. It was morphologically close to D. nihonkaiense and D. latum. The cox1 gene analysis showed 99.4% (340/342 bp) homology with D. nihonkaiense but only 91.8% (314/342 bp) homology with D. latum. The present study suggested that the Diphyllobothrium spp. infection in Korea should be analyzed with specific DNA sequence for an accurate species identification. PMID:24039292
Canonical Analysis as a Generalized Regression Technique for Multivariate Analysis.
ERIC Educational Resources Information Center
Williams, John D.
The use of characteristic coding (dummy coding) is made in showing solutions to four multivariate problems using canonical analysis. The canonical variates can be themselves analyzed by the use of multiple linear regression. When the canonical variates are used as criteria in a multiple linear regression, the R2 values are equal to 0, where 0 is…
A rotor optimization using regression analysis
NASA Technical Reports Server (NTRS)
Giansante, N.
1984-01-01
The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.
A rotor optimization using regression analysis
NASA Technical Reports Server (NTRS)
Giansante, N.
1984-01-01
The design and development of helicopter rotors is subject to the many design variables and their interactions that effect rotor operation. Until recently, selection of rotor design variables to achieve specified rotor operational qualities has been a costly, time consuming, repetitive task. For the past several years, Kaman Aerospace Corporation has successfully applied multiple linear regression analysis, coupled with optimization and sensitivity procedures, in the analytical design of rotor systems. It is concluded that approximating equations can be developed rapidly for a multiplicity of objective and constraint functions and optimizations can be performed in a rapid and cost effective manner; the number and/or range of design variables can be increased by expanding the data base and developing approximating functions to reflect the expanded design space; the order of the approximating equations can be expanded easily to improve correlation between analyzer results and the approximating equations; gradients of the approximating equations can be calculated easily and these gradients are smooth functions reducing the risk of numerical problems in the optimization; the use of approximating functions allows the problem to be started easily and rapidly from various initial designs to enhance the probability of finding a global optimum; and the approximating equations are independent of the analysis or optimization codes used.
Common pitfalls in statistical analysis: Linear regression analysis.
Aggarwal, Rakesh; Ranganathan, Priya
2017-01-01
In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis.
Thompson, Audie K.; Smith, Daniel; Gray, Jimmy; Carr, Heather S.; Liu, Aimin; Winge, Dennis R.; Hosler, Jonathan P.
2010-01-01
The Cu(I) chaperone Cox11 is required for the insertion of CuB into cytochrome c oxidase (CcO) of mitochondria and many bacteria, including Rhodobacter sphaeroides. Exploration of the copper binding stoichiometry of R. sphaeroides Cox11 led to the finding that an apparent tetramer of both mitochondrial and bacterial Cox11 binds more copper than the sum of the dimers, providing another example of the flexibility of copper binding by Cu(I)-S clusters. Site-directed mutagenesis has been used to identify components of Cox11 that are not required for copper binding but are absolutely required for the assembly of CuB, including conserved Cys-35 and Lys-123. In contrast to earlier proposals, Cys-35 is not required for dimerization of Cox11 or for copper binding. These findings, plus the location of Cys-35 at the C terminus of the predicted transmembrane helix and thereby close to the surface of the membrane, allows a proposal that Cys-35 is involved in the transfer of copper from the Cu(I) cluster of Cox11 to the CuB ligands His-333/334 during the folding of CcO subunit I. Lys-123 is located near the Cu(I) cluster of Cox11, in an area otherwise devoid of charged residues. From the analysis of several Cox11 mutants, including K123E, L and R, we conclude that a previous proposal that Lys-123 provides charge balance for the stabilization of the Cu(I) cluster is unlikely to account for its absolute requirement for Cox11 function. Rather, consideration of the properties of Lys-123 and the apparent specificity of Cox11 suggests that Lys-123 plays a role in the interaction of Cox11 with its target. PMID:20524628
Weighted regression analysis and interval estimators
Donald W. Seegrist
1974-01-01
A method for deriving the weighted least squares estimators for the parameters of a multiple regression model. Confidence intervals for expected values, and prediction intervals for the means of future samples are given.
Giganti, Mark J.; Luz, Paula M.; Caro-Vega, Yanink; Cesar, Carina; Padgett, Denis; Koenig, Serena; Echevarria, Juan; McGowan, Catherine C.; Shepherd, Bryan E.
2015-01-01
Abstract Many studies of HIV/AIDS aggregate data from multiple cohorts to improve power and generalizability. There are several analysis approaches to account for cross-cohort heterogeneity; we assessed how different approaches can impact results from an HIV/AIDS study investigating predictors of mortality. Using data from 13,658 HIV-infected patients starting antiretroviral therapy from seven Latin American and Caribbean cohorts, we illustrate the assumptions of seven readily implementable approaches to account for across cohort heterogeneity with Cox proportional hazards models, and we compare hazard ratio estimates across approaches. As a sensitivity analysis, we modify cohort membership to generate specific heterogeneity conditions. Hazard ratio estimates varied slightly between the seven analysis approaches, but differences were not clinically meaningful. Adjusted hazard ratio estimates for the association between AIDS at treatment initiation and death varied from 2.00 to 2.20 across approaches that accounted for heterogeneity; the adjusted hazard ratio was estimated as 1.73 in analyses that ignored across cohort heterogeneity. In sensitivity analyses with more extreme heterogeneity, we noted a slightly greater distinction between approaches. Despite substantial heterogeneity between cohorts, the impact of the specific approach to account for heterogeneity was minimal in our case study. Our results suggest that it is important to account for across cohort heterogeneity in analyses, but that the specific technique for addressing heterogeneity may be less important. Because of their flexibility in accounting for cohort heterogeneity, we prefer stratification or meta-analysis methods, but we encourage investigators to consider their specific study conditions and objectives. PMID:25647087
Pipe performance analysis with nonparametric regression
NASA Astrophysics Data System (ADS)
Liu, Zheng; Hu, Yafei; Wu, Wei
2011-04-01
Asbestos cement (AC) water mains were installed extensively in North America, Europe, and Australia during 1920s-1980s and subject to a high breakage rate in recent years in some utilities. It is essential to understand how the influential factors contribute to the degradation and failure of AC pipes. The historical failure data collected from twenty utilities are used in this study to explore the correlation between pipe condition and its working environment. In this paper, we applied four nonparametric regression methods to model the relationship between pipe failure represented by average break rates and influential variables including pipe age and internal and external working environmental parameters. The nonparametric regression models do not take a predetermined form but it needs information derived from data. The feasibility of using a nonparametric regression model for the condition assessment of AC pipes is investigated and understood.
Using Dominance Analysis to Determine Predictor Importance in Logistic Regression
ERIC Educational Resources Information Center
Azen, Razia; Traxel, Nicole
2009-01-01
This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…
Using Dominance Analysis to Determine Predictor Importance in Logistic Regression
ERIC Educational Resources Information Center
Azen, Razia; Traxel, Nicole
2009-01-01
This article proposes an extension of dominance analysis that allows researchers to determine the relative importance of predictors in logistic regression models. Criteria for choosing logistic regression R[superscript 2] analogues were determined and measures were selected that can be used to perform dominance analysis in logistic regression. A…
Choi, In-Wook; Kim, Hwang-Yong; Quan, Juan-Hua; Ryu, Jae-Gee; Sun, Rubing; Lee, Young-Ha
2015-10-01
Fascioliasis, a food-borne trematode zoonosis, is a disease primarily in cattle and sheep and occasionally in humans. Water dropwort (Oenanthe javanica), an aquatic perennial herb, is a common second intermediate host of Fasciola, and the fresh stems and leaves are widely used as a seasoning in the Korean diet. However, no information regarding Fasciola species contamination in water dropwort is available. Here, we collected 500 samples of water dropwort in 3 areas in Korea during February and March 2015, and the water dropwort contamination of Fasciola species was monitored by DNA sequencing analysis of the Fasciola hepatica and Fasciola gigantica specific mitochondrial cytochrome c oxidase subunit 1 (cox1) and nuclear ribosomal internal transcribed spacer 2 (ITS-2). Among the 500 samples assessed, the presence of F. hepatica cox1 and 1TS-2 markers were detected in 2 samples, and F. hepatica contamination was confirmed by sequencing analysis. The nucleotide sequences of cox1 PCR products from the 2 F. hepatica-contaminated samples were 96.5% identical to the F. hepatica cox1 sequences in GenBank, whereas F. gigantica cox1 sequences were 46.8% similar with the sequence detected from the cox1 positive samples. However, F. gigantica cox1 and ITS-2 markers were not detected by PCR in the 500 samples of water dropwort. Collectively, in this survey of the water dropwort contamination with Fasciola species, very low prevalence of F. hepatica contamination was detected in the samples.
A random effects meta-analysis model with Box-Cox transformation.
Yamaguchi, Yusuke; Maruo, Kazushi; Partlett, Christopher; Riley, Richard D
2017-07-19
In a random effects meta-analysis model, true treatment effects for each study are routinely assumed to follow a normal distribution. However, normality is a restrictive assumption and the misspecification of the random effects distribution may result in a misleading estimate of overall mean for the treatment effect, an inappropriate quantification of heterogeneity across studies and a wrongly symmetric prediction interval. We focus on problems caused by an inappropriate normality assumption of the random effects distribution, and propose a novel random effects meta-analysis model where a Box-Cox transformation is applied to the observed treatment effect estimates. The proposed model aims to normalise an overall distribution of observed treatment effect estimates, which is sum of the within-study sampling distributions and the random effects distribution. When sampling distributions are approximately normal, non-normality in the overall distribution will be mainly due to the random effects distribution, especially when the between-study variation is large relative to the within-study variation. The Box-Cox transformation addresses this flexibly according to the observed departure from normality. We use a Bayesian approach for estimating parameters in the proposed model, and suggest summarising the meta-analysis results by an overall median, an interquartile range and a prediction interval. The model can be applied for any kind of variables once the treatment effect estimate is defined from the variable. A simulation study suggested that when the overall distribution of treatment effect estimates are skewed, the overall mean and conventional I (2) from the normal random effects model could be inappropriate summaries, and the proposed model helped reduce this issue. We illustrated the proposed model using two examples, which revealed some important differences on summary results, heterogeneity measures and prediction intervals from the normal random effects model
Park, Sang Hyun; Jeon, Hyeong Kyu; Kim, Jin Bong
2015-01-01
Most of the diphyllobothriid tapeworms isolated from human samples in the Republic of Korea (= Korea) have been identified as Diphyllobothrium nihonkaiense by genetic analysis. This paper reports confirmation of D. nihonkaiense infections in 4 additional human samples obtained between 1995 and 2014, which were analyzed at the Department of Parasitology, Hallym University College of Medicine, Korea. Analysis of the mitochondrial cytochrome c oxidase 1 (cox1) gene revealed a 98.5-99.5% similarity with a reference D. nihonkaiense sequence in GenBank. The present report adds 4 cases of D. nihonkaiense infections to the literature, indicating that the dominant diphyllobothriid tapeworm species in Korea is D. nihonkaiense but not D. latum. PMID:25748716
Anthropometric Data Reduction Using Factor Analysis and Stepwise Regression
1980-07-01
REFERENCES ........... ............................ 20 APPENDIX List of Anthropometric Variables ........ .................... .A-I TABLES I. Factor...Analysis and Regression Results for the Skinfold Group .... ........ 6 2. Factor Analysis and Regression Results for the Height Group ... ........ 7 3...organ. For instance, all skinfold measurement variables and all head measurement variables were separated from the reamining variables and were given
Strategies for Detecting Outliers in Regression Analysis: An Introductory Primer.
ERIC Educational Resources Information Center
Evans, Victoria P.
Outliers are extreme data points that have the potential to influence statistical analyses. Outlier identification is important to researchers using regression analysis because outliers can influence the model used to such an extent that they seriously distort the conclusions drawn from the data. The effects of outliers on regression analysis are…
Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro
2012-11-01
Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan.
Crager, Michael R; Tang, Gong
We propose a method for assessing an individual patient's risk of a future clinical event using clinical trial or cohort data and Cox proportional hazards regression, combining the information from several studies using meta-analysis techniques. The method combines patient-specific estimates of the log cumulative hazard across studies, weighting by the relative precision of the estimates, using either fixed- or random-effects meta-analysis calculations. Risk assessment can be done for any future patient using a few key summary statistics determined once and for all from each study. Generalizations of the method to logistic regression and linear models are immediate. We evaluate the methods using simulation studies and illustrate their application using real data.
Laubender, Ruediger P; Bender, Ralf
2014-02-28
Recently, Laubender and Bender (Stat. Med. 2010; 29: 851-859) applied the average risk difference (RD) approach to estimate adjusted RD and corresponding number needed to treat measures in the Cox proportional hazards model. We calculated standard errors and confidence intervals by using bootstrap techniques. In this paper, we develop asymptotic variance estimates of the adjusted RD measures and corresponding asymptotic confidence intervals within the counting process theory and evaluated them in a simulation study. We illustrate the use of the asymptotic confidence intervals by means of data of the Düsseldorf Obesity Mortality Study.
ERIC Educational Resources Information Center
Hecht, Jeffrey B.
The analysis of regression residuals and detection of outliers are discussed, with emphasis on determining how deviant an individual data point must be to be considered an outlier and the impact that multiple suspected outlier data points have on the process of outlier determination and treatment. Only bivariate (one dependent and one independent)…
ERIC Educational Resources Information Center
Hecht, Jeffrey B.
The analysis of regression residuals and detection of outliers are discussed, with emphasis on determining how deviant an individual data point must be to be considered an outlier and the impact that multiple suspected outlier data points have on the process of outlier determination and treatment. Only bivariate (one dependent and one independent)…
The Precision Efficacy Analysis for Regression Sample Size Method.
ERIC Educational Resources Information Center
Brooks, Gordon P.; Barcikowski, Robert S.
The general purpose of this study was to examine the efficiency of the Precision Efficacy Analysis for Regression (PEAR) method for choosing appropriate sample sizes in regression studies used for precision. The PEAR method, which is based on the algebraic manipulation of an accepted cross-validity formula, essentially uses an effect size to…
Regression Commonality Analysis: A Technique for Quantitative Theory Building
ERIC Educational Resources Information Center
Nimon, Kim; Reio, Thomas G., Jr.
2011-01-01
When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…
Regression Commonality Analysis: A Technique for Quantitative Theory Building
ERIC Educational Resources Information Center
Nimon, Kim; Reio, Thomas G., Jr.
2011-01-01
When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…
Moderation analysis using a two-level regression model.
Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott
2014-10-01
Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.
3D Regression Heat Map Analysis of Population Study Data.
Klemm, Paul; Lawonn, Kai; Glaßer, Sylvia; Niemann, Uli; Hegenscheid, Katrin; Völzke, Henry; Preim, Bernhard
2016-01-01
Epidemiological studies comprise heterogeneous data about a subject group to define disease-specific risk factors. These data contain information (features) about a subject's lifestyle, medical status as well as medical image data. Statistical regression analysis is used to evaluate these features and to identify feature combinations indicating a disease (the target feature). We propose an analysis approach of epidemiological data sets by incorporating all features in an exhaustive regression-based analysis. This approach combines all independent features w.r.t. a target feature. It provides a visualization that reveals insights into the data by highlighting relationships. The 3D Regression Heat Map, a novel 3D visual encoding, acts as an overview of the whole data set. It shows all combinations of two to three independent features with a specific target disease. Slicing through the 3D Regression Heat Map allows for the detailed analysis of the underlying relationships. Expert knowledge about disease-specific hypotheses can be included into the analysis by adjusting the regression model formulas. Furthermore, the influences of features can be assessed using a difference view comparing different calculation results. We applied our 3D Regression Heat Map method to a hepatic steatosis data set to reproduce results from a data mining-driven analysis. A qualitative analysis was conducted on a breast density data set. We were able to derive new hypotheses about relations between breast density and breast lesions with breast cancer. With the 3D Regression Heat Map, we present a visual overview of epidemiological data that allows for the first time an interactive regression-based analysis of large feature sets with respect to a disease.
Combined survival analysis of cardiac patients by a Cox PH model and a Markov chain.
Shauly, Michal; Rabinowitz, Gad; Gilutz, Harel; Parmet, Yisrael
2011-10-01
The control and treatment of dyslipidemia is a major public health challenge, particularly for patients with coronary heart diseases. In this paper we propose a framework for survival analysis of patients who had a major cardiac event, focusing on assessment of the effect of changing LDL-cholesterol level and statins consumption on survival. This framework includes a Cox PH model and a Markov chain, and combines their results into reinforced conclusions regarding the factors that affect survival time. We prospectively studied 2,277 cardiac patients, and the results show high congruence between the Markov model and the PH model; both evidence that diabetes, history of stroke, peripheral vascular disease and smoking significantly increase hazard rate and reduce survival time. On the other hand, statin consumption is correlated with a lower hazard rate and longer survival time in both models. The role of such a framework in understanding the therapeutic behavior of patients and implementing effective secondary and primary prevention of heart diseases is discussed here.
NASA Astrophysics Data System (ADS)
Ahn, Kuk-Hyun; Palmer, Richard
2016-09-01
Despite wide use of regression-based regional flood frequency analysis (RFFA) methods, the majority are based on either ordinary least squares (OLS) or generalized least squares (GLS). This paper proposes 'spatial proximity' based RFFA methods using the spatial lagged model (SLM) and spatial error model (SEM). The proposed methods are represented by two frameworks: the quantile regression technique (QRT) and parameter regression technique (PRT). The QRT develops prediction equations for flooding quantiles in average recurrence intervals (ARIs) of 2, 5, 10, 20, and 100 years whereas the PRT provides prediction of three parameters for the selected distribution. The proposed methods are tested using data incorporating 30 basin characteristics from 237 basins in Northeastern United States. Results show that generalized extreme value (GEV) distribution properly represents flood frequencies in the study gages. Also, basin area, stream network, and precipitation seasonality are found to be the most effective explanatory variables in prediction modeling by the QRT and PRT. 'Spatial proximity' based RFFA methods provide reliable flood quantile estimates compared to simpler methods. Compared to the QRT, the PRT may be recommended due to its accuracy and computational simplicity. The results presented in this paper may serve as one possible guidepost for hydrologists interested in flood analysis at ungaged sites.
Replica analysis of overfitting in regression models for time-to-event data
NASA Astrophysics Data System (ADS)
Coolen, A. C. C.; Barrett, J. E.; Paga, P.; Perez-Vicente, C. J.
2017-09-01
Overfitting, which happens when the number of parameters in a model is too large compared to the number of data points available for determining these parameters, is a serious and growing problem in survival analysis. While modern medicine presents us with data of unprecedented dimensionality, these data cannot yet be used effectively for clinical outcome prediction. Standard error measures in maximum likelihood regression, such as p-values and z-scores, are blind to overfitting, and even for Cox’s proportional hazards model (the main tool of medical statisticians), one finds in literature only rules of thumb on the number of samples required to avoid overfitting. In this paper we present a mathematical theory of overfitting in regression models for time-to-event data, which aims to increase our quantitative understanding of the problem and provide practical tools with which to correct regression outcomes for the impact of overfitting. It is based on the replica method, a statistical mechanical technique for the analysis of heterogeneous many-variable systems that has been used successfully for several decades in physics, biology, and computer science, but not yet in medical statistics. We develop the theory initially for arbitrary regression models for time-to-event data, and verify its predictions in detail for the popular Cox model.
Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.
Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A
2016-01-01
Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.
Wu, Chien-Ming; Wu, Shu-Chun; Chung, Wan-Jung; Lin, Hsien-Cheng; Chen, Kun-Tze; Chen, Yu-Chian; Hsu, Mei-Feng; Yang, Jwu-Maw; Wang, Jih-Pyang; Lin, Chun-Nan
2007-01-01
The known flavonoids ginkgetin (1), taiwanhomoflavone A (2), taiwanhomoflavone B (3), and taiwanhomoflavone C (4) and eight known lignans: justicidin B (9), justicidin C (10), justicidin D (11), chinensinaphthol methyl ether (12), procumphthalide A (13), procumbenoside A (15), and ciliatosides A (16) and B (17) were isolated from Cephalotaxus wilsoniana and Justicia species, respectively. The antiplatelet effects of the above constituents on human platelet-rich plasma (PRP) were evaluated. Of the compounds tested on human PRP, compounds 1, 4, 9, and 11 showed inhibition of secondary aggregation induced by adrenaline. Compound 1 had an inhibitory effect on cyclooxygenase-1 (COX-1). Molecular docking studies revealed that 1 and the related compounds apigenin (5), cycloheterophyllin (6), broussoflavone F (7), and quercetin (8) were docked near the gate of active site of COX-1. It indicated that the antiplatelet effect of 1, 4, 9, and 11 is partially owed to suppression of COX-1 activity and reduced thromboxane formation. Flavonoids, 1, 5, 6, 7, and 8 may block the gate of the active site of COX-1 and interfere the conversion of arachidonic acid to prostaglandin (PG) H2 in the COX-1 active site.
Linear regression analysis of survival data with missing censoring indicators
Wang, Qihua
2010-01-01
Linear regression analysis has been studied extensively in a random censorship setting, but typically all of the censoring indicators are assumed to be observed. In this paper, we develop synthetic data methods for estimating regression parameters in a linear model when some censoring indicators are missing. We define estimators based on regression calibration, imputation, and inverse probability weighting techniques, and we prove all three estimators are asymptotically normal. The finite-sample performance of each estimator is evaluated via simulation. We illustrate our methods by assessing the effects of sex and age on the time to non-ambulatory progression for patients in a brain cancer clinical trial. PMID:20559722
Joint regression analysis of correlated data using Gaussian copulas.
Song, Peter X-K; Li, Mingyao; Yuan, Ying
2009-03-01
This article concerns a new joint modeling approach for correlated data analysis. Utilizing Gaussian copulas, we present a unified and flexible machinery to integrate separate one-dimensional generalized linear models (GLMs) into a joint regression analysis of continuous, discrete, and mixed correlated outcomes. This essentially leads to a multivariate analogue of the univariate GLM theory and hence an efficiency gain in the estimation of regression coefficients. The availability of joint probability models enables us to develop a full maximum likelihood inference. Numerical illustrations are focused on regression models for discrete correlated data, including multidimensional logistic regression models and a joint model for mixed normal and binary outcomes. In the simulation studies, the proposed copula-based joint model is compared to the popular generalized estimating equations, which is a moment-based estimating equation method to join univariate GLMs. Two real-world data examples are used in the illustration.
Education, income inequality, and mortality: a multiple regression analysis.
Muller, Andreas
2002-01-05
To test whether the relation between income inequality and mortality found in US states is because of different levels of formal education. Cross sectional, multiple regression analysis. All US states and the District of Columbia (n=51). US census statistics and vital statistics for the years 1989 and 1990. Multiple regression analysis with age adjusted mortality from all causes as the dependent variable and 3 independent variables-the Gini coefficient, per capita income, and percentage of people aged >/=18 years without a high school diploma. The income inequality effect disappeared when percentage of people without a high school diploma was added to the regression models. The fit of the regression significantly improved when education was added to the model. Lack of high school education accounts for the income inequality effect and is a powerful predictor of mortality variation among US states.
Regression Model Optimization for the Analysis of Experimental Data
NASA Technical Reports Server (NTRS)
Ulbrich, N.
2009-01-01
A candidate math model search algorithm was developed at Ames Research Center that determines a recommended math model for the multivariate regression analysis of experimental data. The search algorithm is applicable to classical regression analysis problems as well as wind tunnel strain gage balance calibration analysis applications. The algorithm compares the predictive capability of different regression models using the standard deviation of the PRESS residuals of the responses as a search metric. This search metric is minimized during the search. Singular value decomposition is used during the search to reject math models that lead to a singular solution of the regression analysis problem. Two threshold dependent constraints are also applied. The first constraint rejects math models with insignificant terms. The second constraint rejects math models with near-linear dependencies between terms. The math term hierarchy rule may also be applied as an optional constraint during or after the candidate math model search. The final term selection of the recommended math model depends on the regressor and response values of the data set, the user s function class combination choice, the user s constraint selections, and the result of the search metric minimization. A frequently used regression analysis example from the literature is used to illustrate the application of the search algorithm to experimental data.
The Analysis of the Regression-Discontinuity Design in R
ERIC Educational Resources Information Center
Thoemmes, Felix; Liao, Wang; Jin, Ze
2017-01-01
This article describes the analysis of regression-discontinuity designs (RDDs) using the R packages rdd, rdrobust, and rddtools. We discuss similarities and differences between these packages and provide directions on how to use them effectively. We use real data from the Carolina Abecedarian Project to show how an analysis of an RDD can be…
Background stratified Poisson regression analysis of cohort data.
Richardson, David B; Langholz, Bryan
2012-03-01
Background stratified Poisson regression is an approach that has been used in the analysis of data derived from a variety of epidemiologically important studies of radiation-exposed populations, including uranium miners, nuclear industry workers, and atomic bomb survivors. We describe a novel approach to fit Poisson regression models that adjust for a set of covariates through background stratification while directly estimating the radiation-disease association of primary interest. The approach makes use of an expression for the Poisson likelihood that treats the coefficients for stratum-specific indicator variables as 'nuisance' variables and avoids the need to explicitly estimate the coefficients for these stratum-specific parameters. Log-linear models, as well as other general relative rate models, are accommodated. This approach is illustrated using data from the Life Span Study of Japanese atomic bomb survivors and data from a study of underground uranium miners. The point estimate and confidence interval obtained from this 'conditional' regression approach are identical to the values obtained using unconditional Poisson regression with model terms for each background stratum. Moreover, it is shown that the proposed approach allows estimation of background stratified Poisson regression models of non-standard form, such as models that parameterize latency effects, as well as regression models in which the number of strata is large, thereby overcoming the limitations of previously available statistical software for fitting background stratified Poisson regression models.
Joint regression analysis and AMMI model applied to oat improvement
NASA Astrophysics Data System (ADS)
Oliveira, A.; Oliveira, T. A.; Mejza, S.
2012-09-01
In our work we present an application of some biometrical methods useful in genotype stability evaluation, namely AMMI model, Joint Regression Analysis (JRA) and multiple comparison tests. A genotype stability analysis of oat (Avena Sativa L.) grain yield was carried out using data of the Portuguese Plant Breeding Board, sample of the 22 different genotypes during the years 2002, 2003 and 2004 in six locations. In Ferreira et al. (2006) the authors state the relevance of the regression models and of the Additive Main Effects and Multiplicative Interactions (AMMI) model, to study and to estimate phenotypic stability effects. As computational techniques we use the Zigzag algorithm to estimate the regression coefficients and the agricolae-package available in R software for AMMI model analysis.
Time series analysis using semiparametric regression on oil palm production
NASA Astrophysics Data System (ADS)
Yundari, Pasaribu, U. S.; Mukhaiyar, U.
2016-04-01
This paper presents semiparametric kernel regression method which has shown its flexibility and easiness in mathematical calculation, especially in estimating density and regression function. Kernel function is continuous and it produces a smooth estimation. The classical kernel density estimator is constructed by completely nonparametric analysis and it is well reasonable working for all form of function. Here, we discuss about parameter estimation in time series analysis. First, we consider the parameters are exist, then we use nonparametrical estimation which is called semiparametrical. The selection of optimum bandwidth is obtained by considering the approximation of Mean Integrated Square Root Error (MISE).
Analysis of Sting Balance Calibration Data Using Optimized Regression Models
NASA Technical Reports Server (NTRS)
Ulbrich, N.; Bader, Jon B.
2010-01-01
Calibration data of a wind tunnel sting balance was processed using a candidate math model search algorithm that recommends an optimized regression model for the data analysis. During the calibration the normal force and the moment at the balance moment center were selected as independent calibration variables. The sting balance itself had two moment gages. Therefore, after analyzing the connection between calibration loads and gage outputs, it was decided to choose the difference and the sum of the gage outputs as the two responses that best describe the behavior of the balance. The math model search algorithm was applied to these two responses. An optimized regression model was obtained for each response. Classical strain gage balance load transformations and the equations of the deflection of a cantilever beam under load are used to show that the search algorithm s two optimized regression models are supported by a theoretical analysis of the relationship between the applied calibration loads and the measured gage outputs. The analysis of the sting balance calibration data set is a rare example of a situation when terms of a regression model of a balance can directly be derived from first principles of physics. In addition, it is interesting to note that the search algorithm recommended the correct regression model term combinations using only a set of statistical quality metrics that were applied to the experimental data during the algorithm s term selection process.
Sparse Regression by Projection and Sparse Discriminant Analysis.
Qi, Xin; Luo, Ruiyan; Carroll, Raymond J; Zhao, Hongyu
2015-04-01
Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared to the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplemental materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.
Sparse Regression by Projection and Sparse Discriminant Analysis
Qi, Xin; Luo, Ruiyan; Carroll, Raymond J.; Zhao, Hongyu
2014-01-01
Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared to the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplemental materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided. PMID:26345204
Regression analysis for solving diagnosis problem of children's health
NASA Astrophysics Data System (ADS)
Cherkashina, Yu A.; Gerget, O. M.
2016-04-01
The paper includes results of scientific researches. These researches are devoted to the application of statistical techniques, namely, regression analysis, to assess the health status of children in the neonatal period based on medical data (hemostatic parameters, parameters of blood tests, the gestational age, vascular-endothelial growth factor) measured at 3-5 days of children's life. In this paper a detailed description of the studied medical data is given. A binary logistic regression procedure is discussed in the paper. Basic results of the research are presented. A classification table of predicted values and factual observed values is shown, the overall percentage of correct recognition is determined. Regression equation coefficients are calculated, the general regression equation is written based on them. Based on the results of logistic regression, ROC analysis was performed, sensitivity and specificity of the model are calculated and ROC curves are constructed. These mathematical techniques allow carrying out diagnostics of health of children providing a high quality of recognition. The results make a significant contribution to the development of evidence-based medicine and have a high practical importance in the professional activity of the author.
An adequate design for regression analysis of yield trials.
Gusmão, L
1985-12-01
Based on theoretical demonstrations and illustrated with a numerical example from triticale yield trials in Portugal, the Completely Randomized Design is proposed as the one suited for Regression Analysis. When trials are designed in Complete Randomized Blocks the regression of plot production on block mean instead of the regression of cultivar mean on the overall mean of the trial is proposed as the correct procedure for regression analysis. These proposed procedures, in addition to providing a better agreement with the assumptions for regression and the philosophy of the method, induce narrower confidence intervals and attenuation of the hyperbolic effect. The increase in precision is brought about by both a decrease in the t Student values by an increased number of degrees of freedom, and by a decrease in standard error by a non proportional increase of residual variance and non proportional increase of the sum of squares of the assumed independent variable. The new procedures seem to be promising for a better understanding of the mechanism of specific instability.
Regression Analysis with Dummy Variables: Use and Interpretation.
ERIC Educational Resources Information Center
Hinkle, Dennis E.; Oliver, J. Dale
1986-01-01
Multiple regression analysis (MRA) may be used when both continuous and categorical variables are included as independent research variables. The use of MRA with categorical variables involves dummy coding, that is, assigning zeros and ones to levels of categorical variables. Caution is urged in results interpretation. (Author/CH)
Regression Analysis: Instructional Resource for Cost/Managerial Accounting
ERIC Educational Resources Information Center
Stout, David E.
2015-01-01
This paper describes a classroom-tested instructional resource, grounded in principles of active learning and a constructivism, that embraces two primary objectives: "demystify" for accounting students technical material from statistics regarding ordinary least-squares (OLS) regression analysis--material that students may find obscure or…
Analysis of Sting Balance Calibration Data Using Optimized Regression Models
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert; Bader, Jon B.
2009-01-01
Calibration data of a wind tunnel sting balance was processed using a search algorithm that identifies an optimized regression model for the data analysis. The selected sting balance had two moment gages that were mounted forward and aft of the balance moment center. The difference and the sum of the two gage outputs were fitted in the least squares sense using the normal force and the pitching moment at the balance moment center as independent variables. The regression model search algorithm predicted that the difference of the gage outputs should be modeled using the intercept and the normal force. The sum of the two gage outputs, on the other hand, should be modeled using the intercept, the pitching moment, and the square of the pitching moment. Equations of the deflection of a cantilever beam are used to show that the search algorithm s two recommended math models can also be obtained after performing a rigorous theoretical analysis of the deflection of the sting balance under load. The analysis of the sting balance calibration data set is a rare example of a situation when regression models of balance calibration data can directly be derived from first principles of physics and engineering. In addition, it is interesting to see that the search algorithm recommended the same regression models for the data analysis using only a set of statistical quality metrics.
MULGRES: a computer program for stepwise multiple regression analysis
A. Jeff Martin
1971-01-01
MULGRES is a computer program source deck that is designed for multiple regression analysis employing the technique of stepwise deletion in the search for most significant variables. The features of the program, along with inputs and outputs, are briefly described, with a note on machine compatibility.
Regression Analysis: Instructional Resource for Cost/Managerial Accounting
ERIC Educational Resources Information Center
Stout, David E.
2015-01-01
This paper describes a classroom-tested instructional resource, grounded in principles of active learning and a constructivism, that embraces two primary objectives: "demystify" for accounting students technical material from statistics regarding ordinary least-squares (OLS) regression analysis--material that students may find obscure or…
Regression Analysis with Dummy Variables: Use and Interpretation.
ERIC Educational Resources Information Center
Hinkle, Dennis E.; Oliver, J. Dale
1986-01-01
Multiple regression analysis (MRA) may be used when both continuous and categorical variables are included as independent research variables. The use of MRA with categorical variables involves dummy coding, that is, assigning zeros and ones to levels of categorical variables. Caution is urged in results interpretation. (Author/CH)
Kolenda, Rafał; Ugorski, Maciej; Bednarski, Michał
2014-08-01
Sarcocysts from four Polish roe deer were collected and examined by light microscopy, small subunit ribosomal RNA (ssu rRNA), and the subunit I of cytochrome oxidase (cox1) sequence analysis. This resulted in identification of Sarcocystis gracilis, Sarcocystis oviformis, and Sarcocystis silva. However, we were unable to detect Sarcocystis capreolicanis, the fourth Sarcocystis species found previously in Norwegian roe deer. Polish sarcocysts isolated from various tissues differed in terms of their shape and size and were larger than the respective Norwegian isolates. Analysis of ssu rRNA gene revealed the lack of differences between Sarcocystis isolates belonging to one species and a very low degree of genetic diversity between Polish and Norwegian sarcocysts, ranging from 0.1% for Sarcocystis gracilis and Sarcocystis oviformis to 0.44% for Sarcocystis silva. Contrary to the results of the ssu rRNA analysis, small intraspecies differences in cox1 sequences were found among Polish Sarcocystis gracilis and Sarcocystis silva isolates. The comparison of Polish and Norwegian cox1 sequences representing the same Sarcocystis species revealed similar degree of sequence identity, namely 99.72% for Sarcocystis gracilis, 98.76% for Sarcocystis silva, and 99.85% for Sarcocystis oviformis. Phylogenetic reconstruction and genetic population analyses showed an unexpected high degree of identity between Polish and Norwegian isolates. Moreover, cox1 gene sequences turned out to be more accurate than ssu rRNA when used to reveal phylogenetic relationships among closely related species. The results of our study revealed that the same Sarcocystis species isolated from the same hosts living in different geographic regions show a very high level of genetic similarity.
COX-2-765G>C Polymorphism Increases the Risk of Cancer: A Meta-Analysis
Zhang, Xiao-wei; Hua, Rui-xi; Guo, Wei-jian
2013-01-01
Background Chronic inflammation has been regarded as an important mechanism in carcinogenesis. Inflammation-associated genetic variants have been highly associated with cancer risk. Polymorphisms in the gene cyclooxygenase-2 (COX-2), a pro-inflammation factor, have been suggested to alter the risk of multiple tumors, but the findings of various studies are not consistent. Methods A literature search through February 2013 was performed using PubMed, EMBASE, and CNKI databases. We used odds ratios (ORs) with confidence intervals (CIs) of 95% to assess the strength of the association between the COX-2-765G>C polymorphism and cancer risk in a random-effect model. We also assessed heterogeneity and publication bias. Results In total, 65 articles with 29,487 cancer cases and 39,212 non-cancer controls were included in this meta-analysis. The pooled OR (95% CIs) in the co-dominant model (GC vs. GG) was 1.11 (1.02–1.22), and in the dominant model ((CC+GC) vs. GG), the pooled OR was 1.12 (1.02–1.23). In the subgroup analysis, stratified by cancer type and race, significant associations were found between the-765 C allele and higher risk for gastric cancer, leukemia, pancreatic cancer, and cancer in the Asian population. Conclusion In summary, the COX-2-765 C allele was related to increased cancer susceptibility, especially gastric cancer and cancer in the Asian population. PMID:24023834
A framework for longitudinal data analysis via shape regression
NASA Astrophysics Data System (ADS)
Fishbaugh, James; Durrleman, Stanley; Piven, Joseph; Gerig, Guido
2012-02-01
Traditional longitudinal analysis begins by extracting desired clinical measurements, such as volume or head circumference, from discrete imaging data. Typically, the continuous evolution of a scalar measurement is estimated by choosing a 1D regression model, such as kernel regression or fitting a polynomial of fixed degree. This type of analysis not only leads to separate models for each measurement, but there is no clear anatomical or biological interpretation to aid in the selection of the appropriate paradigm. In this paper, we propose a consistent framework for the analysis of longitudinal data by estimating the continuous evolution of shape over time as twice differentiable flows of deformations. In contrast to 1D regression models, one model is chosen to realistically capture the growth of anatomical structures. From the continuous evolution of shape, we can simply extract any clinical measurements of interest. We demonstrate on real anatomical surfaces that volume extracted from a continuous shape evolution is consistent with a 1D regression performed on the discrete measurements. We further show how the visualization of shape progression can aid in the search for significant measurements. Finally, we present an example on a shape complex of the brain (left hemisphere, right hemisphere, cerebellum) that demonstrates a potential clinical application for our framework.
Luo, Wen; Wang, Lei; Luo, Mao-Hua; Huang, Yu-Zhu; Yang, Hua; Zhou, Yu; Jia, Hong-Tao; Wang, Xiu-Xin
2017-10-01
Papillary renal cell carcinoma(PRCC) is the second most common and aggressive renal cell carcinoma. Identification of novel microRNA biomarkers could be beneficial for the diagnosis and prognosis of PRCC patients. We aimed to screen differentially expressed miRNAs that can act as prognostic factors and to predict the survival of PRCC patients. High-throughput data of miRNAs of 274 PRCC samples were downloaded from TCGA (The Cancer Genome Atlas) dataset and interested miRNAs were identified. Hierarchical clustering and principal component analysis (PCA) were performed on these miRNAs. Critical genes that can act as prognostic factors were screened by LASSO. What's more, Kaplan-Meier survival analysis and ROC (Receiver Operating Characteristic) growth curve were used to testify the accuracy of the model. Biological processes of putative targets of miRNAs were analyzed by bioinformatics methods such as GO (Go Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis. A total of 105 differentially expressed miRNAs were screened out in PRCC samples compared with healthy controls. Two critical miRNAs, hsa-mir-3199-2, and hsa-mir-1293, were screened out by LASSO (Least Absolute Shrinkage and Selection Operator), including 197 and 189 target genes, respectively. Furthermore, its' accuracy was testified by ROC analysis with the AUC (Area under the curve) value of 0.7774968 and 0.6743466. These miRNAs were significantly enriched in pathways as platelet activating factor biosynthetic process, epithelial cell maturation, and IkappaB kinase complex. In conclusion, hsa-mir-3199-2 and hsa-mir-1293 that can act as prognostic biomarkers of PRCC were screened out, which can provide new insights for the clinical treatment of the disease. J. Cell. Biochem. 118: 3488-3494, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Tensor Regression with Applications in Neuroimaging Data Analysis
Zhou, Hua; Li, Lexin; Zhu, Hongtu
2013-01-01
Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of these high-throughput data due to their ultrahigh dimensionality as well as complex structure. In this article, we propose a new family of tensor regression models that efficiently exploit the special structure of tensor covariates. Under this framework, ultrahigh dimensionality is reduced to a manageable level, resulting in efficient estimation and prediction. A fast and highly scalable estimation algorithm is proposed for maximum likelihood estimation and its associated asymptotic properties are studied. Effectiveness of the new methods is demonstrated on both synthetic and real MRI imaging data. PMID:24791032
Islam, Abul B M M K; Dave, Mandar; Amin, Sonia; Jensen, Roderick V; Amin, Ashok R
2016-04-01
The constitutively-expressed cyclooxygenase 1 (COX-1) and the inducible COX-2 are both involved in the conversion of arachidonic acid (AA) to prostaglandins (PGs). However, the functional roles of COX-1 at the cellular level remain unclear. We hypothesized that by comparing differential gene expression and eicosanoid metabolism in lung fibroblasts from wild-type (WT) mice and COX-2(-/-) or COX-1(-/-) mice may help address the functional roles of COX-1 in inflammation and other cellular functions. Compared to WT, the number of specifically-induced transcripts were altered descendingly as follows: COX-2(-/-)>COX-1(-/-)>WT+IL-1β. COX-1(-/-) or COX-2(-/-) cells shared about 50% of the induced transcripts with WT cells treated with IL-1β, respectively. An interactive "anti-inflammatory, proinflammatory, and redox-activated" signature in the protein-protein interactome map was observed in COX-2(-/-) cells. The augmented COX-1 mRNA (in COX-2(-/-) cells) was associated with the upregulation of mRNAs for glutathione S-transferase (GST), superoxide dismutase (SOD), NAD(P)H dehydrogenase quinone 1 (NQO1), aryl hydrocarbon receptor (AhR), peroxiredoxin, phospholipase, prostacyclin synthase, and prostaglandin E synthase, resulting in a significant increase in the levels of PGE2, PGD2, leukotriene B4 (LTB4), PGF1α, thromboxane B2 (TXB2), and PGF2α. The COX-1 plays a dominant role in shifting AA toward the LTB4 pathway and anti-inflammatory activities. Compared to WT, the upregulated COX-1 mRNA in COX-2(-/-) cells generated an "eicosanoid storm". The genomic characteristics of COX-2(-/-) is similar to that of proinflammatory cells as observed in IL-1β induced WT cells. COX-1(-/-) and COX-2(-/-) cells exhibited compensation of various eicosanoids at the genomic and metabolic levels.
Four cases of Taenia saginata infection with an analysis of COX1 gene.
Cho, Jaeeun; Jung, Bong-Kwang; Lim, Hyemi; Kim, Min-Jae; Yooyen, Thanapon; Lee, Dongmin; Eom, Keeseon S; Shin, Eun-Hee; Chai, Jong-Yil
2014-02-01
Human taeniases had been not uncommon in the Republic of Korea (=Korea) until the 1980s. The prevalence decreased and a national survey in 2004 revealed no Taenia egg positive cases. However, a subsequent national survey in 2012 showed 0.04% (10 cases) prevalence of Taenia spp. eggs suggesting its resurgence in Korea. We recently encountered 4 cases of Taenia saginata infection who had symptoms of taeniasis that included discharge of proglottids. We obtained several proglottids from each case. Because the morphological features of T. saginata are almost indistinguishable from those of Taenia asiatica, molecular analyses using the PCR-RFLP and DNA sequencing of the cytochrome c oxidase subunit 1 (cox1) were performed to identify the species. The PCR-RFLP patterns of all of the 4 specimens were consistent with T. saginata, and the cox1 gene sequence showed 99.8-100% identity with that of T. saginata reported previously from Korea, Japan, China, and Cambodia. All of the 4 patients had the history of travel abroad but its relation with contracting taeniasis was unclear. Our findings may suggest resurgence of T. saginata infection among people in Korea.
A regression model analysis of longitudinal dental caries data.
Ringelberg, M L; Tonascia, J A
1976-03-01
Longitudinal data on caries experience were derived from the reexamination and interview of a cohort of 306 subjects with an average follow-up period of 33 years after the baseline examination. Analysis of the data was accomplished by the use of contingency tables utilizing enumeration statistics compared with a multiple regression analysis. The analyses indicated a strong association of caries experience at one point in time with the caries experience of that same person earlier in life. The regression model approach offers adjustment of any given independent variable for the effect of all other independent variables, providing a powerful means of bias reduction. The model is also useful in separating out the specific effect of an independent variable over and above the contribution of other variables. The model used explained 35% of the variability in the DMFS scores recorded. Similar models could be useful adjuncts in the analyses of dental epidemiologic data.
Principal regression analysis and the index leverage effect
NASA Astrophysics Data System (ADS)
Reigneron, Pierre-Alain; Allez, Romain; Bouchaud, Jean-Philippe
2011-09-01
We revisit the index leverage effect, that can be decomposed into a volatility effect and a correlation effect. We investigate the latter using a matrix regression analysis, that we call ‘Principal Regression Analysis' (PRA) and for which we provide some analytical (using Random Matrix Theory) and numerical benchmarks. We find that downward index trends increase the average correlation between stocks (as measured by the most negative eigenvalue of the conditional correlation matrix), and makes the market mode more uniform. Upward trends, on the other hand, also increase the average correlation between stocks but rotates the corresponding market mode away from uniformity. There are two time scales associated to these effects, a short one on the order of a month (20 trading days), and a longer time scale on the order of a year. We also find indications of a leverage effect for sectorial correlations as well, which reveals itself in the second and third mode of the PRA.
Robust regression applied to fractal/multifractal analysis.
NASA Astrophysics Data System (ADS)
Portilla, F.; Valencia, J. L.; Tarquis, A. M.; Saa-Requejo, A.
2012-04-01
Fractal and multifractal are concepts that have grown increasingly popular in recent years in the soil analysis, along with the development of fractal models. One of the common steps is to calculate the slope of a linear fit commonly using least squares method. This shouldn't be a special problem, however, in many situations using experimental data the researcher has to select the range of scales at which is going to work neglecting the rest of points to achieve the best linearity that in this type of analysis is necessary. Robust regression is a form of regression analysis designed to circumvent some limitations of traditional parametric and non-parametric methods. In this method we don't have to assume that the outlier point is simply an extreme observation drawn from the tail of a normal distribution not compromising the validity of the regression results. In this work we have evaluated the capacity of robust regression to select the points in the experimental data used trying to avoid subjective choices. Based on this analysis we have developed a new work methodology that implies two basic steps: • Evaluation of the improvement of linear fitting when consecutive points are eliminated based on R p-value. In this way we consider the implications of reducing the number of points. • Evaluation of the significance of slope difference between fitting with the two extremes points and fitted with the available points. We compare the results applying this methodology and the common used least squares one. The data selected for these comparisons are coming from experimental soil roughness transect and simulated based on middle point displacement method adding tendencies and noise. The results are discussed indicating the advantages and disadvantages of each methodology. Acknowledgements Funding provided by CEIGRAM (Research Centre for the Management of Agricultural and Environmental Risks) and by Spanish Ministerio de Ciencia e Innovación (MICINN) through project no
Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.
Zhang, Futao; Xie, Dan; Liang, Meimei; Xiong, Momiao
2016-04-01
To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI's Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.
Poisson Regression Analysis of Illness and Injury Surveillance Data
Frome E.L., Watkins J.P., Ellis E.D.
2012-12-12
The Department of Energy (DOE) uses illness and injury surveillance to monitor morbidity and assess the overall health of the work force. Data collected from each participating site include health events and a roster file with demographic information. The source data files are maintained in a relational data base, and are used to obtain stratified tables of health event counts and person time at risk that serve as the starting point for Poisson regression analysis. The explanatory variables that define these tables are age, gender, occupational group, and time. Typical response variables of interest are the number of absences due to illness or injury, i.e., the response variable is a count. Poisson regression methods are used to describe the effect of the explanatory variables on the health event rates using a log-linear main effects model. Results of fitting the main effects model are summarized in a tabular and graphical form and interpretation of model parameters is provided. An analysis of deviance table is used to evaluate the importance of each of the explanatory variables on the event rate of interest and to determine if interaction terms should be considered in the analysis. Although Poisson regression methods are widely used in the analysis of count data, there are situations in which over-dispersion occurs. This could be due to lack-of-fit of the regression model, extra-Poisson variation, or both. A score test statistic and regression diagnostics are used to identify over-dispersion. A quasi-likelihood method of moments procedure is used to evaluate and adjust for extra-Poisson variation when necessary. Two examples are presented using respiratory disease absence rates at two DOE sites to illustrate the methods and interpretation of the results. In the first example the Poisson main effects model is adequate. In the second example the score test indicates considerable over-dispersion and a more detailed analysis attributes the over-dispersion to extra
Spatial regression analysis on 32 years total column ozone data
NASA Astrophysics Data System (ADS)
Knibbe, J. S.; van der A, R. J.; de Laat, A. T. J.
2014-02-01
Multiple-regressions analysis have been performed on 32 years of total ozone column data that was spatially gridded with a 1° × 1.5° resolution. The total ozone data consists of the MSR (Multi Sensor Reanalysis; 1979-2008) and two years of assimilated SCIAMACHY ozone data (2009-2010). The two-dimensionality in this data-set allows us to perform the regressions locally and investigate spatial patterns of regression coefficients and their explanatory power. Seasonal dependencies of ozone on regressors are included in the analysis. A new physically oriented model is developed to parameterize stratospheric ozone. Ozone variations on non-seasonal timescales are parameterized by explanatory variables describing the solar cycle, stratospheric aerosols, the quasi-biennial oscillation (QBO), El Nino (ENSO) and stratospheric alternative halogens (EESC). For several explanatory variables, seasonally adjusted versions of these explanatory variables are constructed to account for the difference in their effect on ozone throughout the year. To account for seasonal variation in ozone, explanatory variables describing the polar vortex, geopotential height, potential vorticity and average day length are included. Results of this regression model are compared to that of similar analysis based on a more commonly applied statistically oriented model. The physically oriented model provides spatial patterns in the regression results for each explanatory variable. The EESC has a significant depleting effect on ozone at high and mid-latitudes, the solar cycle affects ozone positively mostly at the Southern Hemisphere, stratospheric aerosols affect ozone negatively at high Northern latitudes, the effect of QBO is positive and negative at the tropics and mid to high-latitudes respectively and ENSO affects ozone negatively between 30° N and 30° S, particularly at the Pacific. The contribution of explanatory variables describing seasonal ozone variation is generally large at mid to high
de Pedro, María; Baeza, Sara; Escudero, María-Teresa; Dierssen-Sotos, Trinidad; Gómez-Acebo, Inés; Pollán, Marina; Llorca, Javier
2015-01-01
Evidence on non-steroidal anti-inflammatory drugs (NSAID) use and breast cancer risk shows a slightly protective effect of these drugs, but previous studies lack randomized clinical trial results and present high heterogeneity in exposure measurement. This systematic review and meta-analysis widens the knowledge about NSAID use and breast cancer risk, updating the information from the last meta-analysis, focusing on evidence on specific effects of COX-2 inhibitors and differential expression patterns of hormonal receptors. A PubMed-database search was conducted to include all entries published with the keywords "BREAST CANCER NSAID ANTI-INFLAMMATORY" until 10/24/2013 providing original results from cohort studies, case-control studies, or randomized clinical trials with at least one reported relative risk (RR) or odds ratio (OR) on the association between any NSAID use and incidence of invasive breast cancer. This resulted in 49 publications, from which the information was retrieved about type of study, exposure characteristics, breast cancer characteristics, and breast cancer-NSAID association. Meta-analyses were performed separately for case-control and cohort studies and for different hormone-receptor status. NSAID use reduced invasive breast cancer risk by about 20 %. A similar effect was found for aspirin, acetaminophen, COX-2 inhibitors and, to a lesser extent, ibuprofen. The effect of aspirin was similar in preventing hormone-receptor-positive breast cancer. This meta-analysis suggests a slightly protective effect of NSAIDs-especially aspirin and COX-2 inhibitors- against breast cancer, which seems to be restricted to ER/PR+tumors.
Analysis of regression methods for solar activity forecasting
NASA Technical Reports Server (NTRS)
Lundquist, C. A.; Vaughan, W. W.
1979-01-01
The paper deals with the potential use of the most recent solar data to project trends in the next few years. Assuming that a mode of solar influence on weather can be identified, advantageous use of that knowledge presumably depends on estimating future solar activity. A frequently used technique for solar cycle predictions is a linear regression procedure along the lines formulated by McNish and Lincoln (1949). The paper presents a sensitivity analysis of the behavior of such regression methods relative to the following aspects: cycle minimum, time into cycle, composition of historical data base, and unnormalized vs. normalized solar cycle data. Comparative solar cycle forecasts for several past cycles are presented as to these aspects of the input data. Implications for the current cycle, No. 21, are also given.
Forecasting urban water demand: A meta-regression analysis.
Sebri, Maamar
2016-12-01
Water managers and planners require accurate water demand forecasts over the short-, medium- and long-term for many purposes. These range from assessing water supply needs over spatial and temporal patterns to optimizing future investments and planning future allocations across competing sectors. This study surveys the empirical literature on the urban water demand forecasting using the meta-analytical approach. Specifically, using more than 600 estimates, a meta-regression analysis is conducted to identify explanations of cross-studies variation in accuracy of urban water demand forecasting. Our study finds that accuracy depends significantly on study characteristics, including demand periodicity, modeling method, forecasting horizon, model specification and sample size. The meta-regression results remain robust to different estimators employed as well as to a series of sensitivity checks performed. The importance of these findings lies in the conclusions and implications drawn out for regulators and policymakers and for academics alike.
Analysis of regression methods for solar activity forecasting
NASA Technical Reports Server (NTRS)
Lundquist, C. A.; Vaughan, W. W.
1979-01-01
The paper deals with the potential use of the most recent solar data to project trends in the next few years. Assuming that a mode of solar influence on weather can be identified, advantageous use of that knowledge presumably depends on estimating future solar activity. A frequently used technique for solar cycle predictions is a linear regression procedure along the lines formulated by McNish and Lincoln (1949). The paper presents a sensitivity analysis of the behavior of such regression methods relative to the following aspects: cycle minimum, time into cycle, composition of historical data base, and unnormalized vs. normalized solar cycle data. Comparative solar cycle forecasts for several past cycles are presented as to these aspects of the input data. Implications for the current cycle, No. 21, are also given.
Dong, W; Zhao, W; Sun, L
1996-09-01
This paper reports a prospective survey of 173 patients with nonoperable lung cancer between January. 1, 1983 to March. 1, 1985. The follow-up rate was 97.7% over five years. Fourteen factors including sex, age, course of disease before treatment, clinical stage, performance status, size of mass, metastatic status, hemoglobin before treatment, short-term response to treatment and so on which might influence long term survival were studied by univariate analysis (Kruskal-Wallis test for Kaplan-Meier survival curve) and by multivariate analysis (Cox's proportional hazad model and audio-visual chart test for goodness of fit). Multivariate analysis using Cox's model revealed 6 significant prognostic factors: performance status, short-term response to treatment, clinical stage, hemoglobin before treatment, smoking index and method of treatment. The survival prediction equation was chi 2 = 72.14, nu = 6, P < 0.0001. The results indicate that the performance status and the CR rate of the initial treatment, among other things, is the major factors affecting prognosis.
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…
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…
Bar-Yaacov, Dan; Bouskila, Amos; Mishmar, Dan
2013-01-01
Recently, we found dramatic mitochondrial DNA divergence of Israeli Chamaeleo chamaeleon populations into two geographically distinct groups. We aimed to examine whether the same pattern of divergence could be found in nuclear genes. However, no genomic resource is available for any chameleon species. Here we present the first chameleon transcriptome, obtained using deep sequencing (SOLiD). Our analysis identified 164,000 sequence contigs of which 19,000 yielded unique BlastX hits. To test the efficacy of our sequencing effort, we examined whether the chameleon and other available reptilian transcriptomes harbored complete sets of genes comprising known biochemical pathways, focusing on the nDNA-encoded oxidative phosphorylation (OXPHOS) genes as a model. As a reference for the screen, we used the human 86 (including isoforms) known structural nDNA-encoded OXPHOS subunits. Analysis of 34 publicly available vertebrate transcriptomes revealed orthologs for most human OXPHOS genes. However, OXPHOS subunit COX8 (Cytochrome C oxidase subunit 8), including all its known isoforms, was consistently absent in transcriptomes of iguanian lizards, implying loss of this subunit during the radiation of this suborder. The lack of COX8 in the suborder Iguania is intriguing, since it is important for cellular respiration and ATP production. Our sequencing effort added a new resource for comparative genomic studies, and shed new light on the evolutionary dynamics of the OXPHOS system.
Bar-Yaacov, Dan; Bouskila, Amos; Mishmar, Dan
2013-01-01
Recently, we found dramatic mitochondrial DNA divergence of Israeli Chamaeleo chamaeleon populations into two geographically distinct groups. We aimed to examine whether the same pattern of divergence could be found in nuclear genes. However, no genomic resource is available for any chameleon species. Here we present the first chameleon transcriptome, obtained using deep sequencing (SOLiD). Our analysis identified 164,000 sequence contigs of which 19,000 yielded unique BlastX hits. To test the efficacy of our sequencing effort, we examined whether the chameleon and other available reptilian transcriptomes harbored complete sets of genes comprising known biochemical pathways, focusing on the nDNA-encoded oxidative phosphorylation (OXPHOS) genes as a model. As a reference for the screen, we used the human 86 (including isoforms) known structural nDNA-encoded OXPHOS subunits. Analysis of 34 publicly available vertebrate transcriptomes revealed orthologs for most human OXPHOS genes. However, OXPHOS subunit COX8 (Cytochrome C oxidase subunit 8), including all its known isoforms, was consistently absent in transcriptomes of iguanian lizards, implying loss of this subunit during the radiation of this suborder. The lack of COX8 in the suborder Iguania is intriguing, since it is important for cellular respiration and ATP production. Our sequencing effort added a new resource for comparative genomic studies, and shed new light on the evolutionary dynamics of the OXPHOS system. PMID:24009133
Durrieu, G; Olivier, P; Montastruc, J L
2005-09-01
To evaluate the main characteristics of case reports of arterial hypertension (AH) related to COX-2 inhibitor (coxib) use in real-life practice. This study was based on spontaneous reports of adverse drug reactions (ADRs) submitted to the French Pharmacovigilance system. Associations between AH and the different groups of those using non-steroidal anti-inflammatory drugs (NSAIDs: rofecoxib, celecoxib and non-selective NSAIDs) were compared using calculation of the odds ratio (OR) with 95% confidence intervals (CIs). In France, between 1 April 2000 and 30 November 2003, 34 AH cases related to coxibs were reported. Case reports include predominantly patients older than 65 years, with a previous story of essential AH. Most AH (60%) occurred during the first 15 days of treatment. The AH was reported significantly more frequently for rofecoxib than celecoxib. The OR for development of AH with rofecoxib versus celecoxib was 3.3 (1.6-6.9). The AH was also reported more frequently with coxib (2.8%) than with non-selective NSAID (0.5%) use, OR = 5.9 (3.8-9.0). This study shows that coxibs are associated with a risk of AH in real-life practice. More spontaneous reports of AH to the French Pharmacovigilance system concern rofecoxib than celecoxib (and coxibs than non-selective NSAIDs). This ADR is of special epidemiological importance due to both the risks of AH and the large use of coxibs.
Regression tree analysis of ecological momentary assessment data.
Richardson, Ben; Fuller-Tyszkiewicz, Matthew; O'Donnell, Renee; Ling, Mathew; Staiger, Petra K
2017-09-01
An increasingly popular form of data collection in health psychology research is Ecological Momentary Assessment (EMA); that is, using diaries or smartphones to collect intensive longitudinal data. This method is increasingly applied to the study of relationships between state-based aspects of individuals' functioning and health outcomes (e.g., binge eating, alcohol use). Analysis of such data is challenging and regression tree modelling (RTM) may be a useful alternative to multilevel modelling for investigating the association between a set of explanatory variables and a continuous outcome. Furthermore, RTM outputs 'decision trees' that could be used by health practitioners to guide assessment and tailor intervention. In contrast to regression, RTM is able to easily accommodate many complex, higher-order interactions between predictor variables (without the need to create explicit interaction terms). These benefits make the technique useful for those interested in monitoring and intervening upon health and psychological outcomes (e.g., mood, eating behaviour, risky alcohol use, and treatment adherence). Using real data, this paper demonstrates both the benefits and limitations of RTM and how to extend these models to accommodate analysis of nested data; that is, data that arise from EMA where repeated observations are nested within individuals.
Tree-augmented Cox proportional hazards models.
Su, Xiaogang; Tsai, Chih-Ling
2005-07-01
We study a hybrid model that combines Cox proportional hazards regression with tree-structured modeling. The main idea is to use step functions, provided by a tree structure, to 'augment' Cox (1972) proportional hazards models. The proposed model not only provides a natural assessment of the adequacy of the Cox proportional hazards model but also improves its model fitting without loss of interpretability. Both simulations and an empirical example are provided to illustrate the use of the proposed method.
Parra, Edwin Roger; Lin, Flavia; Martins, Vanessa; Rangel, Maristela Peres; Capelozzi, Vera Luiza
2013-01-01
OBJECTIVE: To study the expression of COX-1 and COX-2 in the remodeled lung in systemic sclerosis (SSc) and idiopathic pulmonary fibrosis (IPF) patients, correlating that expression with patient survival. METHODS: We examined open lung biopsy specimens from 24 SSc patients and 30 IPF patients, using normal lung tissue as a control. The histological patterns included fibrotic nonspecific interstitial pneumonia (NSIP) in SSc patients and usual interstitial pneumonia (UIP) in IPF patients. We used immunohistochemistry and histomorphometry to evaluate the expression of COX-1 and COX-2 in alveolar septa, vessels, and bronchioles. We then correlated that expression with pulmonary function test results and evaluated its impact on patient survival. RESULTS: The expression of COX-1 and COX-2 in alveolar septa was significantly higher in IPF-UIP and SSc-NSIP lung tissue than in the control tissue. No difference was found between IPF-UIP and SSc-NSIP tissue regarding COX-1 and COX-2 expression. Multivariate analysis based on the Cox regression model showed that the factors associated with a low risk of death were younger age, high DLCO/alveolar volume, IPF, and high COX-1 expression in alveolar septa, whereas those associated with a high risk of death were advanced age, low DLCO/alveolar volume, SSc (with NSIP), and low COX-1 expression in alveolar septa. CONCLUSIONS: Our findings suggest that strategies aimed at preventing low COX-1 synthesis will have a greater impact on SSc, whereas those aimed at preventing high COX-2 synthesis will have a greater impact on IPF. However, prospective randomized clinical trials are needed in order to confirm that. PMID:24473763
Parra, Edwin Roger; Lin, Flavia; Martins, Vanessa; Rangel, Maristela Peres; Capelozzi, Vera Luiza
2013-01-01
To study the expression of COX-1 and COX-2 in the remodeled lung in systemic sclerosis (SSc) and idiopathic pulmonary fibrosis (IPF) patients, correlating that expression with patient survival. We examined open lung biopsy specimens from 24 SSc patients and 30 IPF patients, using normal lung tissue as a control. The histological patterns included fibrotic nonspecific interstitial pneumonia (NSIP) in SSc patients and usual interstitial pneumonia (UIP) in IPF patients. We used immunohistochemistry and histomorphometry to evaluate the expression of COX-1 and COX-2 in alveolar septa, vessels, and bronchioles. We then correlated that expression with pulmonary function test results and evaluated its impact on patient survival. The expression of COX-1 and COX-2 in alveolar septa was significantly higher in IPF-UIP and SSc-NSIP lung tissue than in the control tissue. No difference was found between IPF-UIP and SSc-NSIP tissue regarding COX-1 and COX-2 expression. Multivariate analysis based on the Cox regression model showed that the factors associated with a low risk of death were younger age, high DLCO/alveolar volume, IPF, and high COX-1 expression in alveolar septa, whereas those associated with a high risk of death were advanced age, low DLCO/alveolar volume, SSc (with NSIP), and low COX-1 expression in alveolar septa. Our findings suggest that strategies aimed at preventing low COX-1 synthesis will have a greater impact on SSc, whereas those aimed at preventing high COX-2 synthesis will have a greater impact on IPF. However, prospective randomized clinical trials are needed in order to confirm that.
Regression analysis exploring teacher impact on student FCI post scores
NASA Astrophysics Data System (ADS)
Mahadeo, Jonathan V.; Manthey, Seth R.; Brewe, Eric
2013-01-01
High School Modeling Workshops are designed to improve high school physics teachers' understanding of physics and how to teach using the Modeling method. The basic assumption is that the teacher plays a critical role in their students' physics education. This study investigated teacher impacts on students' Force Concept Inventory scores, (FCI), with the hopes of identifying quantitative differences between teachers. This study examined student FCI scores from 18 teachers with at least a year of teaching high school physics. This data was then evaluated using a General Linear Model (GLM), which allowed for a regression equation to be fitted to the data. This regression equation was used to predict student post FCI scores, based on: teacher ID, student pre FCI score, gender, and representation. The results show 12 out of 18 teachers significantly impact their student post FCI scores. The GLM further revealed that of the 12 teachers only five have a positive impact on student post FCI scores. Given these differences among teachers it is our intention to extend our analysis to investigate pedagogical differences between them.
A Visual Analytics Approach for Correlation, Classification, and Regression Analysis
Steed, Chad A; SwanII, J. Edward; Fitzpatrick, Patrick J.; Jankun-Kelly, T.J.
2012-02-01
New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today's increasing complex, multivariate data sets. In this paper, a novel visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today's data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. The current work provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.
Cardiorespiratory fitness and laboratory stress: a meta-regression analysis.
Jackson, Erica M; Dishman, Rod K
2006-01-01
We performed a meta-regression analysis of 73 studies that examined whether cardiorespiratory fitness mitigates cardiovascular responses during and after acute laboratory stress in humans. The cumulative evidence indicates that fitness is related to slightly greater reactivity, but better recovery. However, effects varied according to several study features and were smallest in the better controlled studies. Fitness did not mitigate integrated stress responses such as heart rate and blood pressure, which were the focus of most of the studies we reviewed. Nonetheless, potentially important areas, particularly hemodynamic and vascular responses, have been understudied. Women, racial/ethnic groups, and cardiovascular patients were underrepresented. Randomized controlled trials, including naturalistic studies of real-life responses, are needed to clarify whether a change in fitness alters putative stress mechanisms linked with cardiovascular health.
Estimation of crown closure from AVIRIS data using regression analysis
NASA Technical Reports Server (NTRS)
Staenz, K.; Williams, D. J.; Truchon, M.; Fritz, R.
1993-01-01
Crown closure is one of the input parameters used for forest growth and yield modelling. Preliminary work by Staenz et al. indicates that imaging spectrometer data acquired with sensors such as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) have some potential for estimating crown closure on a stand level. The objectives of this paper are: (1) to establish a relationship between AVIRIS data and the crown closure derived from aerial photography of a forested test site within the Interior Douglas Fir biogeoclimatic zone in British Columbia, Canada; (2) to investigate the impact of atmospheric effects and the forest background on the correlation between AVIRIS data and crown closure estimates; and (3) to improve this relationship using multiple regression analysis.
A Visual Analytics Approach for Correlation, Classification, and Regression Analysis
Steed, Chad A; SwanII, J. Edward; Fitzpatrick, Patrick J.; Jankun-Kelly, T.J.
2013-01-01
New approaches that combine the strengths of humans and machines are necessary to equip analysts with the proper tools for exploring today s increasing complex, multivariate data sets. In this paper, a visual data mining framework, called the Multidimensional Data eXplorer (MDX), is described that addresses the challenges of today s data by combining automated statistical analytics with a highly interactive parallel coordinates based canvas. In addition to several intuitive interaction capabilities, this framework offers a rich set of graphical statistical indicators, interactive regression analysis, visual correlation mining, automated axis arrangements and filtering, and data classification techniques. This chapter provides a detailed description of the system as well as a discussion of key design aspects and critical feedback from domain experts.
Qiu, Xiaoming; Mei, Jixin; Yin, Jianjun; Wang, Hong; Wang, Jinqi; Xie, Ming
2017-09-01
This study investigated expression of proliferating cell nuclear antigen (PCNA), proliferation-associated nuclear antigen (Ki-67) and cyclooxygenase-2 (COX-2) in tissues of breast invasive ductal carcinoma, and analyzed the correlations between these indexes and X-ray features in mammography. A total of 90 patients who were admitted to Huangshi Central Hospital and diagnosed as breast invasive ductal carcinoma from January 2014 to January 2016 were selected. The expression of PCNA, Ki-67 and COX-2 in cancer tissues and cancer-adjacent normal tissues of patients were detected by immunohistochemical staining, and X-ray features in mammography of patients were observed. By using Spearman correlation analysis, the correlations between expression of PCNA, Ki-67 and COX-2 and X-ray features in mammography in breast cancer were investigated. As a result, the positive expression rates of PCNA, Ki-67 and COX-2 in cancer tissues of the patient groups were respectively 42.2, 45.6 and 51.1%, which were significantly higher than those in cancer-adjacent normal tissues of the control group (p<0.05). PCNA, Ki-67 and COX-2 expression in cancer tissues of the patient group was associated with clinical staging and lymphatic metastasis (p<0.05), but had no correlation with age and tumor size (p>0.05). PCNA, Ki-67 and COX-2 expression in cancer tissues of the patient group had no correlation with the existence of lumps and localized density-increased shadows (p>0.05), but were associated with manifestations of architectural distortion, calcification as well as skin and nipple depression (p<0.05). Spearman correlation analysis revealed that there was a significantly positive correlation between the expression of PCNA and COX-2 in cancer tissues of the patient group (r=0.676, p<0.05); there was a significantly positive correlation between the expression of Ki-67 and COX-2 (r=0.724, p<0.05); PCNA expression had no obvious correlation with the expression of Ki-67 (p>0.05). In conclusion
Spatial regression analysis of traffic crashes in Seoul.
Rhee, Kyoung-Ah; Kim, Joon-Ki; Lee, Young-ihn; Ulfarsson, Gudmundur F
2016-06-01
Traffic crashes can be spatially correlated events and the analysis of the distribution of traffic crash frequency requires evaluation of parameters that reflect spatial properties and correlation. Typically this spatial aspect of crash data is not used in everyday practice by planning agencies and this contributes to a gap between research and practice. A database of traffic crashes in Seoul, Korea, in 2010 was developed at the traffic analysis zone (TAZ) level with a number of GIS developed spatial variables. Practical spatial models using available software were estimated. The spatial error model was determined to be better than the spatial lag model and an ordinary least squares baseline regression. A geographically weighted regression model provided useful insights about localization of effects. The results found that an increased length of roads with speed limit below 30 km/h and a higher ratio of residents below age of 15 were correlated with lower traffic crash frequency, while a higher ratio of residents who moved to the TAZ, more vehicle-kilometers traveled, and a greater number of access points with speed limit difference between side roads and mainline above 30 km/h all increased the number of traffic crashes. This suggests, for example, that better control or design for merging lower speed roads with higher speed roads is important. A key result is that the length of bus-only center lanes had the largest effect on increasing traffic crashes. This is important as bus-only center lanes with bus stop islands have been increasingly used to improve transit times. Hence the potential negative safety impacts of such systems need to be studied further and mitigated through improved design of pedestrian access to center bus stop islands.
Lofstedt, Ragnar E
2007-01-01
The field of risk communication has its roots in the environmental, chemical, space, and nuclear arenas. As a number of these sectors have now vastly improved their communication strategies, attention is being placed on sectors that have been more problematic as of late. Examples of such sectors, include the food industries and the pharmaceutical/health sector. This article focuses on how large, multinational pharmaceutical companies can better communicate risks by analysis of one specific case, namely, that of the Cox-2 controversy.(1) For purposes of this article, risk communication is best described as "the flow of information and risk evaluations back and forth between academic experts, regulatory practitioners, interest groups and the general public," and "big pharma" refers to the more traditional R & D-based, innovative pharmaceutical companies.
Zhao, Qiu-jiong; Bai, Shao-cong; Cheng, Cheng; Tao, Ben-zhang; Wang, Le-kai; Liang, Shuang; Yin, Ling; Hang, Xing-yi; Shang, Ai-jia
2016-01-01
Copy number variations have been found in patients with neural tube abnormalities. In this study, we performed genome-wide screening using high-resolution array-based comparative genomic hybridization in three children with tethered spinal cord syndrome and two healthy parents. Of eight copy number variations, four were non-polymorphic. These non-polymorphic copy number variations were associated with Angelman and Prader-Willi syndromes, and microcephaly. Gene function enrichment analysis revealed that COX8C, a gene associated with metabolic disorders of the nervous system, was located in the copy number variation region of Patient 1. Our results indicate that array-based comparative genomic hybridization can be used to diagnose tethered spinal cord syndrome. Our results may help determine the pathogenesis of tethered spinal cord syndrome and prevent occurrence of this disease. PMID:27651783
Factors that determine false recall: a multiple regression analysis.
Roediger, H L; Watson, J M; McDermott, K B; Gallo, D A
2001-09-01
In the Deese-Roediger-McDermott (DRM) paradigm, subjects study lists of words that are designed to elicit the recall of an associatively related critical item. The 55 lists we have developed provide levels of false recall ranging from .01 to .65, and understanding this variability should provide a key to understanding this memory illusion. Using a simultaneous multiple regression analysis, we assessed the contribution of seven factors in creating false recall of critical items in the DRM paradigm. This analysis accounted for approximately 68% of the variance in false recall, with two main predictors: associative connections from the study words to the critical item (r = +.73; semipartial r = +.60) and recallability of the lists (r = -.43; semipartial r = -.34). Taken together, the variance in false recall captured by these predictors accounted for 84% of the variance that can be explained, given the reliability of the false recall measures (r = .90). Therefore, the results of this analysis strongly constrain theories of false memory in this paradigm, suggesting that at least two factors determine the propensity of DRM lists to elicit false recall. The results fit well within the theoretical framework postulating that both semantic activation of the critical item and strategic monitoring processes influence the probability of false recall and false recognition in this paradigm.
Determinants of orphan drugs prices in France: a regression analysis.
Korchagina, Daria; Millier, Aurelie; Vataire, Anne-Lise; Aballea, Samuel; Falissard, Bruno; Toumi, Mondher
2017-04-21
The introduction of the orphan drug legislation led to the increase in the number of available orphan drugs, but the access to them is often limited due to the high price. Social preferences regarding funding orphan drugs as well as the criteria taken into consideration while setting the price remain unclear. The study aimed at identifying the determinant of orphan drug prices in France using a regression analysis. All drugs with a valid orphan designation at the moment of launch for which the price was available in France were included in the analysis. The selection of covariates was based on a literature review and included drug characteristics (Anatomical Therapeutic Chemical (ATC) class, treatment line, age of target population), diseases characteristics (severity, prevalence, availability of alternative therapeutic options), health technology assessment (HTA) details (actual benefit (AB) and improvement in actual benefit (IAB) scores, delay between the HTA and commercialisation), and study characteristics (type of study, comparator, type of endpoint). The main data sources were European public assessment reports, HTA reports, summaries of opinion on orphan designation of the European Medicines Agency, and the French insurance database of drugs and tariffs. A generalized regression model was developed to test the association between the annual treatment cost and selected covariates. A total of 68 drugs were included. The mean annual treatment cost was €96,518. In the univariate analysis, the ATC class (p = 0.01), availability of alternative treatment options (p = 0.02) and the prevalence (p = 0.02) showed a significant correlation with the annual cost. The multivariate analysis demonstrated significant association between the annual cost and availability of alternative treatment options, ATC class, IAB score, type of comparator in the pivotal clinical trial, as well as commercialisation date and delay between the HTA and commercialisation. The
Henrard, S; Speybroeck, N; Hermans, C
2015-11-01
Haemophilia is a rare genetic haemorrhagic disease characterized by partial or complete deficiency of coagulation factor VIII, for haemophilia A, or IX, for haemophilia B. As in any other medical research domain, the field of haemophilia research is increasingly concerned with finding factors associated with binary or continuous outcomes through multivariable models. Traditional models include multiple logistic regressions, for binary outcomes, and multiple linear regressions for continuous outcomes. Yet these regression models are at times difficult to implement, especially for non-statisticians, and can be difficult to interpret. The present paper sought to didactically explain how, why, and when to use classification and regression tree (CART) analysis for haemophilia research. The CART method is non-parametric and non-linear, based on the repeated partitioning of a sample into subgroups based on a certain criterion. Breiman developed this method in 1984. Classification trees (CTs) are used to analyse categorical outcomes and regression trees (RTs) to analyse continuous ones. The CART methodology has become increasingly popular in the medical field, yet only a few examples of studies using this methodology specifically in haemophilia have to date been published. Two examples using CART analysis and previously published in this field are didactically explained in details. There is increasing interest in using CART analysis in the health domain, primarily due to its ease of implementation, use, and interpretation, thus facilitating medical decision-making. This method should be promoted for analysing continuous or categorical outcomes in haemophilia, when applicable. © 2015 John Wiley & Sons Ltd.
Online Statistical Modeling (Regression Analysis) for Independent Responses
NASA Astrophysics Data System (ADS)
Made Tirta, I.; Anggraeni, Dian; Pandutama, Martinus
2017-06-01
Regression analysis (statistical analmodelling) are among statistical methods which are frequently needed in analyzing quantitative data, especially to model relationship between response and explanatory variables. Nowadays, statistical models have been developed into various directions to model various type and complex relationship of data. Rich varieties of advanced and recent statistical modelling are mostly available on open source software (one of them is R). However, these advanced statistical modelling, are not very friendly to novice R users, since they are based on programming script or command line interface. Our research aims to developed web interface (based on R and shiny), so that most recent and advanced statistical modelling are readily available, accessible and applicable on web. We have previously made interface in the form of e-tutorial for several modern and advanced statistical modelling on R especially for independent responses (including linear models/LM, generalized linier models/GLM, generalized additive model/GAM and generalized additive model for location scale and shape/GAMLSS). In this research we unified them in the form of data analysis, including model using Computer Intensive Statistics (Bootstrap and Markov Chain Monte Carlo/ MCMC). All are readily accessible on our online Virtual Statistics Laboratory. The web (interface) make the statistical modeling becomes easier to apply and easier to compare them in order to find the most appropriate model for the data.
Mixed-effects Poisson regression analysis of adverse event reports
Gibbons, Robert D.; Segawa, Eisuke; Karabatsos, George; Amatya, Anup K.; Bhaumik, Dulal K.; Brown, C. Hendricks; Kapur, Kush; Marcus, Sue M.; Hur, Kwan; Mann, J. John
2008-01-01
SUMMARY A new statistical methodology is developed for the analysis of spontaneous adverse event (AE) reports from post-marketing drug surveillance data. The method involves both empirical Bayes (EB) and fully Bayes estimation of rate multipliers for each drug within a class of drugs, for a particular AE, based on a mixed-effects Poisson regression model. Both parametric and semiparametric models for the random-effect distribution are examined. The method is applied to data from Food and Drug Administration (FDA)’s Adverse Event Reporting System (AERS) on the relationship between antidepressants and suicide. We obtain point estimates and 95 per cent confidence (posterior) intervals for the rate multiplier for each drug (e.g. antidepressants), which can be used to determine whether a particular drug has an increased risk of association with a particular AE (e.g. suicide). Confidence (posterior) intervals that do not include 1.0 provide evidence for either significant protective or harmful associations of the drug and the adverse effect. We also examine EB, parametric Bayes, and semiparametric Bayes estimators of the rate multipliers and associated confidence (posterior) intervals. Results of our analysis of the FDA AERS data revealed that newer antidepressants are associated with lower rates of suicide adverse event reports compared with older antidepressants. We recommend improvements to the existing AERS system, which are likely to improve its public health value as an early warning system. PMID:18404622
Risk factors for temporomandibular disorder: binary logistic regression analysis.
Magalhães, Bruno-Gama; de-Sousa, Stéphanie-Trajano; de Mello, Victor-Villaça-Cardoso; da-Silva-Barbosa, André-Cavalcante; de-Assis-Morais, Mariana-Pacheco-Lima; Barbosa-Vasconcelos, Márcia-Maria-Vendiciano; Caldas-Júnior, Arnaldo-de-França
2014-05-01
To analyze the influence of socioeconomic and demographic factors (gender, economic class, age and marital status) on the occurrence of temporomandibular disorder. One hundred individuals from urban areas in the city of Recife (Brazil) registered at Family Health Units was examined using Axis I of the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) which addresses myofascial pain and joint problems (disc displacement, arthralgia, osteoarthritis and oesteoarthrosis). The Brazilian Economic Classification Criteria (CCEB) was used for the collection of socioeconomic and demographic data. Then, it was categorized as Class A (high social class), Classes B/C (middle class) and Classes D/E (very poor social class). The results were analyzed using Pearson's chi-square test for proportions, Fisher's exact test, nonparametric Mann-Whitney test and Binary logistic regression analysis. None of the participants belonged to Class A, 72% belonged to Classes B/C and 28% belonged to Classes D/E. The multivariate analysis revealed that participants from Classes D/E had a 4.35-fold greater chance of exhibiting myofascial pain and 11.3-fold greater chance of exhibiting joint problems. Poverty is a important condition to exhibit myofascial pain and joint problems.
Risk factors for temporomandibular disorder: Binary logistic regression analysis
Magalhães, Bruno G.; de-Sousa, Stéphanie T.; de Mello, Victor V C.; da-Silva-Barbosa, André C.; de-Assis-Morais, Mariana P L.; Barbosa-Vasconcelos, Márcia M V.
2014-01-01
Objectives: To analyze the influence of socioeconomic and demographic factors (gender, economic class, age and marital status) on the occurrence of temporomandibular disorder. Study Design: One hundred individuals from urban areas in the city of Recife (Brazil) registered at Family Health Units was examined using Axis I of the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) which addresses myofascial pain and joint problems (disc displacement, arthralgia, osteoarthritis and oesteoarthrosis). The Brazilian Economic Classification Criteria (CCEB) was used for the collection of socioeconomic and demographic data. Then, it was categorized as Class A (high social class), Classes B/C (middle class) and Classes D/E (very poor social class). The results were analyzed using Pearson’s chi-square test for proportions, Fisher’s exact test, nonparametric Mann-Whitney test and Binary logistic regression analysis. Results: None of the participants belonged to Class A, 72% belonged to Classes B/C and 28% belonged to Classes D/E. The multivariate analysis revealed that participants from Classes D/E had a 4.35-fold greater chance of exhibiting myofascial pain and 11.3-fold greater chance of exhibiting joint problems. Conclusions: Poverty is a important condition to exhibit myofascial pain and joint problems. Key words:Temporomandibular joint disorders, risk factors, prevalence. PMID:24316706
An Introduction to Logistic Regression Analysis and Reporting.
ERIC Educational Resources Information Center
Peng, Chao-Ying Joanne; Lee, Kuk Lida; Ingersoll, Gary M.
2002-01-01
Provides guidelines for what to expect in an article using logistic regression techniques, discussing tables, figures, and charts to be included to comprehensively assess results and assumptions to be verified; demonstrating the preferred pattern for applying logistic methods, with an illustration of logistic regression applied to a data set; and…
An Effect Size for Regression Predictors in Meta-Analysis
ERIC Educational Resources Information Center
Aloe, Ariel M.; Becker, Betsy Jane
2012-01-01
A new effect size representing the predictive power of an independent variable from a multiple regression model is presented. The index, denoted as r[subscript sp], is the semipartial correlation of the predictor with the outcome of interest. This effect size can be computed when multiple predictor variables are included in the regression model…
The Variance Normalization Method of Ridge Regression Analysis.
ERIC Educational Resources Information Center
Bulcock, J. W.; And Others
The testing of contemporary sociological theory often calls for the application of structural-equation models to data which are inherently collinear. It is shown that simple ridge regression, which is commonly used for controlling the instability of ordinary least squares regression estimates in ill-conditioned data sets, is not a legitimate…
Elghafghuf, Adel; Dufour, Simon; Reyher, Kristen; Dohoo, Ian; Stryhn, Henrik
2014-12-01
Mastitis is a complex disease affecting dairy cows and is considered to be the most costly disease of dairy herds. The hazard of mastitis is a function of many factors, both managerial and environmental, making its control a difficult issue to milk producers. Observational studies of clinical mastitis (CM) often generate datasets with a number of characteristics which influence the analysis of those data: the outcome of interest may be the time to occurrence of a case of mastitis, predictors may change over time (time-dependent predictors), the effects of factors may change over time (time-dependent effects), there are usually multiple hierarchical levels, and datasets may be very large. Analysis of such data often requires expansion of the data into the counting-process format - leading to larger datasets - thus complicating the analysis and requiring excessive computing time. In this study, a nested frailty Cox model with time-dependent predictors and effects was applied to Canadian Bovine Mastitis Research Network data in which 10,831 lactations of 8035 cows from 69 herds were followed through lactation until the first occurrence of CM. The model was fit to the data as a Poisson model with nested normally distributed random effects at the cow and herd levels. Risk factors associated with the hazard of CM during the lactation were identified, such as parity, calving season, herd somatic cell score, pasture access, fore-stripping, and proportion of treated cases of CM in a herd. The analysis showed that most of the predictors had a strong effect early in lactation and also demonstrated substantial variation in the baseline hazard among cows and between herds. A small simulation study for a setting similar to the real data was conducted to evaluate the Poisson maximum likelihood estimation approach with both Gaussian quadrature method and Laplace approximation. Further, the performance of the two methods was compared with the performance of a widely used estimation
A flexible count data regression model for risk analysis.
Guikema, Seth D; Coffelt, Jeremy P; Goffelt, Jeremy P
2008-02-01
In many cases, risk and reliability analyses involve estimating the probabilities of discrete events such as hardware failures and occurrences of disease or death. There is often additional information in the form of explanatory variables that can be used to help estimate the likelihood of different numbers of events in the future through the use of an appropriate regression model, such as a generalized linear model. However, existing generalized linear models (GLM) are limited in their ability to handle the types of variance structures often encountered in using count data in risk and reliability analysis. In particular, standard models cannot handle both underdispersed data (variance less than the mean) and overdispersed data (variance greater than the mean) in a single coherent modeling framework. This article presents a new GLM based on a reformulation of the Conway-Maxwell Poisson (COM) distribution that is useful for both underdispersed and overdispersed count data and demonstrates this model by applying it to the assessment of electric power system reliability. The results show that the proposed COM GLM can provide as good of fits to data as the commonly used existing models for overdispered data sets while outperforming these commonly used models for underdispersed data sets.
Power analysis of principal components regression in genetic association studies.
Shen, Yan-feng; Zhu, Jun
2009-10-01
Association analysis provides an opportunity to find genetic variants underlying complex traits. A principal components regression (PCR)-based approach was shown to outperform some competing approaches. However, a limitation of this method is that the principal components (PCs) selected from single nucleotide polymorphisms (SNPs) may be unrelated to the phenotype. In this article, we investigate the theoretical properties of such a method in more detail. We first derive the exact power function of the test based on PCR, and hence clarify the relationship between the test power and the degrees of freedom (DF). Next, we extend the PCR test to a general weighted PCs test, which provides a unified framework for understanding the properties of some related statistics. We then compare the performance of these tests. We also introduce several data-driven adaptive alternatives to overcome difficulties in the PCR approach. Finally, we illustrate our results using simulations based on real genotype data. Simulation study shows the risk of using the unsupervised rule to determine the number of PCs, and demonstrates that there is no single uniformly powerful method for detecting genetic variants.
NASA Astrophysics Data System (ADS)
Kavitha, T.; Velraj, G.
2017-08-01
The molecular structure of 1-(2, 5-Dichloro-4-Sulfophenyl)-3-Methyl-5-Pyrazolone (DSMP) was optimized using DFT/B3LYP/6-31++G(d,p) level and its corresponding experimental as well as theoretical FT-IR, FT-Raman vibrational frequencies and UV-Vis spectral analysis were carried out. The vibrational assignments and total energy distributions of each vibration were presented with the aid of Veda 4xx software. The molecular electrostatic potential, HOMO-LUMO energies, global and local reactivity descriptors and natural bond orbitals were analyzed in order to find the most possible reactive sites of the molecule and it was found that DSMP molecule possess enhanced nucleophilic activity. One of the common known COX2 inhibitor, celecoxib (CXB) was also found to exhibit similar reactivity properties and hence DSMP was also expected to inhibit COX enzymes. In order to detect the COX inhibition nature of DSMP, molecular docking analysis was carried out with the help of Autodock software. For that, the optimized structure was in turn used for docking DSMP with COX enzymes. The binding energy scores and inhibitory constant values reveal that the DSMP molecule possess good binding affinity and low inhibition constant towards COX2 enzyme and hence it can be used as an anti-inflammatory drug after carrying out necessary biological tests.
Janssen, I.; Stebbings, J.H.
1990-01-01
In environmental epidemiology, trace and toxic substance concentrations frequently have very highly skewed distributions ranging over one or more orders of magnitude, and prediction by conventional regression is often poor. Classification and Regression Tree Analysis (CART) is an alternative in such contexts. To compare the techniques, two Pennsylvania data sets and three independent variables are used: house radon progeny (RnD) and gamma levels as predicted by construction characteristics in 1330 houses; and {approximately}200 house radon (Rn) measurements as predicted by topographic parameters. CART may identify structural variables of interest not identified by conventional regression, and vice versa, but in general the regression models are similar. CART has major advantages in dealing with other common characteristics of environmental data sets, such as missing values, continuous variables requiring transformations, and large sets of potential independent variables. CART is most useful in the identification and screening of independent variables, greatly reducing the need for cross-tabulations and nested breakdown analyses. There is no need to discard cases with missing values for the independent variables because surrogate variables are intrinsic to CART. The tree-structured approach is also independent of the scale on which the independent variables are measured, so that transformations are unnecessary. CART identifies important interactions as well as main effects. The major advantages of CART appear to be in exploring data. Once the important variables are identified, conventional regressions seem to lead to results similar but more interpretable by most audiences. 12 refs., 8 figs., 10 tabs.
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.
Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis
ERIC Educational Resources Information Center
Kim, Rae Seon
2011-01-01
When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…
Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis
ERIC Educational Resources Information Center
Kim, Rae Seon
2011-01-01
When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…
DFT analysis and spectral characteristics of Celecoxib a potent COX-2 inhibitor
NASA Astrophysics Data System (ADS)
Vijayakumar, B.; Kannappan, V.; Sathyanarayanamoorthi, V.
2016-10-01
Extensive quantum mechanical studies are carried out on Celecoxib (CXB), a new generation drug to understand the vibrational and electronic spectral characteristics of the molecule. The vibrational frequencies of CXB are computed by HF and B3LYP methods with 6-311++G (d, p) basis set. The theoretical scaled vibrational frequencies have been assigned and they agreed satisfactorily with experimental FT-IR and Raman frequencies. The theoretical maximum wavelength of absorption of CXB are calculated in water and ethanol by TD-DFT method and these values are compared with experimentally determined λmax values. The spectral and Natural bonds orbital (NBO) analysis in conjunction with spectral data established the presence of intra molecular interactions such as mesomeric, hyperconjugative and steric effects in CXB. The electron density at various positions and reactivity descriptors of CXB indicate that the compound functions as a nucleophile and establish that aromatic ring system present in the molecule is the site of drug action. Electronic distribution and HOMO - LUMO energy values of CXB are discussed in terms of intra-molecular interactions. Computed values of Mulliken charges and thermodynamic properties of CXB are reported.
CIN III lesions and regression: retrospective analysis of 635 cases.
Motamedi, Melodi; Böhmer, Gerd; Neumann, Heinrich H; von Wasielewski, Reinhard
2015-11-21
The rate of spontaneous regression in CIN III lesions is controversial. Whereas some studies have reported high regression rates of up to 38% after prolonged biopsy-conus intervals, others have shown rates between 0 and 4% without considering time intervals. Identification of young patients with potentially regressing CIN III could offer the chance to avoid conisation, thus lowering the risk of preterm labour. To further clarify the facts, we retrospectively compared 635 biopsies showing CIN III with the diagnosis of the conisation. Either regression (CIN I or less) or non-regression (CIN II and higher) was recorded. Diagnoses were made by light microscopy and p16 immunostaining. Conisation was performed between 2 and 463 days after biopsy (median 8.9 weeks). Six hundred twenty one (98%) were HPV-HR positive. In 345 cases, HPV subtyping was available, showing HPV16 infection in 57%. Routine processing of the conisation tissue showed no corresponding CIN lesion (< CIN II) in 40 cases (6.3%). Additional step sectioning of the tissue revealed small CIN II+ lesions in 80%. Finally, eight cases (1.3%) fulfilled the criteria of regression. No regression was seen in HPV16 positive cases. Twelve invasive carcinomas were detected by routine processing of the conisation tissue. These results are in contrast with some prior reports that might have overestimated spontaneous regression of CIN III. Study size and an accurate discrimination between CIN II and CIN III lesions by histopathology seem to be the most likely factors to explain the diverging results published. Complete step sectioning of the whole tissue is also mandatory in questionable cases. Although theories exist that the initial biopsy might stimulate the immune system, thus triggering regression within weeks, our data do not substantially support such a mechanism. Overall, the chance of a CIN III lesion to regress rapidly within weeks or months after diagnosis seems to be small. We found more previously
Nagase, Satoshi; Iyoda, Tomokazu; Kanno, Hiroshi; Akase, Tomohide; Arakawa, Ichiro; Inoue, Tadao; Uetsuka, Yoshio
2016-10-01
Phase III clinical trials have comfirmed that the S-1 plus oxaliplatin(SOX)is inferior to the capecitabine plus oxaliplatin (COX)regimen in the treatment of metastatic colorectal cancer.On the basis of these findings, we compared, using a clinical decision analysis-based approach, the cost-effectiveness of the SOX and COX regimens.Herein, we simulated the expected effects and costs of the SOX and COX regimens using the markov model.Clinical data were obtained from Hong's 2012 report.The cost data comprised the costs for pharmacist labor, material, inspection, and treatment for adverse event, as well as the total cost of care at the advanced stage.The result showed that the expected cost of the SOX and COX regimen was 1,538,330 yen, and 1,429,596 yen, respectively, with an expected survival rate of 29.18 months, and 28.63 months, respectively.The incremental cost-effectiveness ratio of the SOX regimen was 197,698 yen/month; thus, the SOX regimen was found to be more cost-effective that the COX regimen.
Seyedmajidi, Maryam; Shafaee, Shahryar; Siadati, Sepideh; Moghaddam, Elham Alizadeh; Ghasemi, Nafiseh; Bijani, Ali; Najafi, Mostafa
2015-01-01
Background: Cyclo-oxygenase-2 (COX-2) is an early response gene that is induced by growth factors, oncogenes and carcinogens and its expression is increased in various tumors. Increased expression of COX-2 plays a significant role in the development and growth of tumors by interfering in biological processes such as cell division, cellular immunity, cell adhesion, apoptosis, and angiogenesis. This study aimed to investigate the immunohistochemical expression of COX-2 in keratocystic odontogenic tumor (KOT) in comparison with ameloblastoma and dentigerous cyst with regards to different clinical behavior and histopathological features of these lesions. Materials and Methods: Paraffined blocks of 45 cases including 15 cases of dentigerous cyst, 15 cases of KOT and 15 cases of ameloblastoma were stained with immunohistochemical method for COX-2. Five high-power fields of each sample were evaluated to determine the percentage of stained cells and the intensity of staining. Degree of immunoreactivity was obtained from the sum of two. Statistical evaluation was performed by the Kruskal-Wallis and ANOVA Mann-Whitney test (P < 0.05). Results: Overexpression of COX-2 in ameloblastoma and KOT was observed compared with dentigerous cyst (P < 0.001). However, no significant difference was observed between the expression of COX-2 in ameloblastoma and KOT (P = 0.148). Conclusion: The COX-2 expression in odontogenic tumors such as ameloblastoma and cystic neoplasm with aggressive behavior such as KOT increases. However, it does not seem that COX-2 affects the development and growth of cysts with noninvasive behavior like dentigerous cyst. PMID:26005470
Hu, Yao-Dong; Pang, Hui-Zhong; Li, De-Sheng; Ling, Shan-Shan; Lan, Dan; Wang, Ye; Zhu, Yun; Li, Di-Yan; Wei, Rong-Ping; Zhang, He-Min; Wang, Cheng-Dong
2016-11-05
As the rate-limiting enzyme of the mitochondrial respiratory chain, cytochrome c oxidase (COX) plays a crucial role in biological metabolism. "Living fossil" giant panda (Ailuropoda melanoleuca) is well-known for its special bamboo diet. In an effort to explore functional variation of COX1 in the energy metabolism behind giant panda's low-energy bamboo diet, we looked at genetic variation of COX1 gene in giant panda, and tested for its selection effect. In 1545 base pairs of the gene from 15 samples, 9 positions were variable and 1 mutation leaded to an amino acid sequence change. COX1 gene produces six haplotypes, nucleotide (pi), haplotype diversity (Hd). In addition, the average number of nucleotide differences (k) is 0.001629±0.001036, 0.8083±0.0694 and 2.517, respectively. Also, dN/dS ratio is significantly below 1. These results indicated that giant panda had a low population genetic diversity, and an obvious purifying selection of the COX1 gene which reduces synthesis of ATP determines giant panda's low-energy bamboo diet. Phylogenetic trees based on the COX1 gene were constructed to demonstrate that giant panda is the sister group of other Ursidae.
NASA Astrophysics Data System (ADS)
Haddad, Khaled; Rahman, Ataur
2012-04-01
SummaryIn this article, an approach using Bayesian Generalised Least Squares (BGLS) regression in a region-of-influence (ROI) framework is proposed for regional flood frequency analysis (RFFA) for ungauged catchments. Using the data from 399 catchments in eastern Australia, the BGLS-ROI is constructed to regionalise the flood quantiles (Quantile Regression Technique (QRT)) and the first three moments of the log-Pearson type 3 (LP3) distribution (Parameter Regression Technique (PRT)). This scheme firstly develops a fixed region model to select the best set of predictor variables for use in the subsequent regression analyses using an approach that minimises the model error variance while also satisfying a number of statistical selection criteria. The identified optimal regression equation is then used in the ROI experiment where the ROI is chosen for a site in question as the region that minimises the predictive uncertainty. To evaluate the overall performances of the quantiles estimated by the QRT and PRT, a one-at-a-time cross-validation procedure is applied. Results of the proposed method indicate that both the QRT and PRT in a BGLS-ROI framework lead to more accurate and reliable estimates of flood quantiles and moments of the LP3 distribution when compared to a fixed region approach. Also the BGLS-ROI can deal reasonably well with the heterogeneity in Australian catchments as evidenced by the regression diagnostics. Based on the evaluation statistics it was found that both BGLS-QRT and PRT-ROI perform similarly well, which suggests that the PRT is a viable alternative to QRT in RFFA. The RFFA methods developed in this paper is based on the database available in eastern Australia. It is expected that availability of a more comprehensive database (in terms of both quality and quantity) will further improve the predictive performance of both the fixed and ROI based RFFA methods presented in this study, which however needs to be investigated in future when such a
Quantile regression provides a fuller analysis of speed data.
Hewson, Paul
2008-03-01
Considerable interest already exists in terms of assessing percentiles of speed distributions, for example monitoring the 85th percentile speed is a common feature of the investigation of many road safety interventions. However, unlike the mean, where t-tests and ANOVA can be used to provide evidence of a statistically significant change, inference on these percentiles is much less common. This paper examines the potential role of quantile regression for modelling the 85th percentile, or any other quantile. Given that crash risk may increase disproportionately with increasing relative speed, it may be argued these quantiles are of more interest than the conditional mean. In common with the more usual linear regression, quantile regression admits a simple test as to whether the 85th percentile speed has changed following an intervention in an analogous way to using the t-test to determine if the mean speed has changed by considering the significance of parameters fitted to a design matrix. Having briefly outlined the technique and briefly examined an application with a widely published dataset concerning speed measurements taken around the introduction of signs in Cambridgeshire, this paper will demonstrate the potential for quantile regression modelling by examining recent data from Northamptonshire collected in conjunction with a "community speed watch" programme. Freely available software is used to fit these models and it is hoped that the potential benefits of using quantile regression methods when examining and analysing speed data are demonstrated.
Analysis of retirement income adequacy using quantile regression: A case study in Malaysia
NASA Astrophysics Data System (ADS)
Alaudin, Ros Idayuwati; Ismail, Noriszura; Isa, Zaidi
2015-09-01
Quantile regression is a statistical analysis that does not restrict attention to the conditional mean and therefore, permitting the approximation of the whole conditional distribution of a response variable. Quantile regression is a robust regression to outliers compared to mean regression models. In this paper, we demonstrate how quantile regression approach can be used to analyze the ratio of projected wealth to needs (wealth-needs ratio) during retirement.
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
Analysis of the labor productivity of enterprises via quantile regression
NASA Astrophysics Data System (ADS)
Türkan, Semra
2017-07-01
In this study, we have analyzed the factors that affect the performance of Turkey's Top 500 Industrial Enterprises using quantile regression. The variable about labor productivity of enterprises is considered as dependent variable, the variableabout assets is considered as independent variable. The distribution of labor productivity of enterprises is right-skewed. If the dependent distribution is skewed, linear regression could not catch important aspects of the relationships between the dependent variable and its predictors due to modeling only the conditional mean. Hence, the quantile regression, which allows modelingany quantilesof the dependent distribution, including the median,appears to be useful. It examines whether relationships between dependent and independent variables are different for low, medium, and high percentiles. As a result of analyzing data, the effect of total assets is relatively constant over the entire distribution, except the upper tail. It hasa moderately stronger effect in the upper tail.
Intermediate and advanced topics in multilevel logistic regression analysis.
Austin, Peter C; Merlo, Juan
2017-09-10
Multilevel data occur frequently in health services, population and public health, and epidemiologic research. In such research, binary outcomes are common. Multilevel logistic regression models allow one to account for the clustering of subjects within clusters of higher-level units when estimating the effect of subject and cluster characteristics on subject outcomes. A search of the PubMed database demonstrated that the use of multilevel or hierarchical regression models is increasing rapidly. However, our impression is that many analysts simply use multilevel regression models to account for the nuisance of within-cluster homogeneity that is induced by clustering. In this article, we describe a suite of analyses that can complement the fitting of multilevel logistic regression models. These ancillary analyses permit analysts to estimate the marginal or population-average effect of covariates measured at the subject and cluster level, in contrast to the within-cluster or cluster-specific effects arising from the original multilevel logistic regression model. We describe the interval odds ratio and the proportion of opposed odds ratios, which are summary measures of effect for cluster-level covariates. We describe the variance partition coefficient and the median odds ratio which are measures of components of variance and heterogeneity in outcomes. These measures allow one to quantify the magnitude of the general contextual effect. We describe an R(2) measure that allows analysts to quantify the proportion of variation explained by different multilevel logistic regression models. We illustrate the application and interpretation of these measures by analyzing mortality in patients hospitalized with a diagnosis of acute myocardial infarction. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Survival Analysis by Penalized Regression and Matrix Factorization
Lai, Yeuntyng; Hayashida, Morihiro; Akutsu, Tatsuya
2013-01-01
Because every disease has its unique survival pattern, it is necessary to find a suitable model to simulate followups. DNA microarray is a useful technique to detect thousands of gene expressions at one time and is usually employed to classify different types of cancer. We propose combination methods of penalized regression models and nonnegative matrix factorization (NMF) for predicting survival. We tried L 1- (lasso), L 2- (ridge), and L 1-L 2 combined (elastic net) penalized regression for diffuse large B-cell lymphoma (DLBCL) patients' microarray data and found that L 1-L 2 combined method predicts survival best with the smallest logrank P value. Furthermore, 80% of selected genes have been reported to correlate with carcinogenesis or lymphoma. Through NMF we found that DLBCL patients can be divided into 4 groups clearly, and it implies that DLBCL may have 4 subtypes which have a little different survival patterns. Next we excluded some patients who were indicated hard to classify in NMF and executed three penalized regression models again. We found that the performance of survival prediction has been improved with lower logrank P values. Therefore, we conclude that after preselection of patients by NMF, penalized regression models can predict DLBCL patients' survival successfully. PMID:23737722
Remedial Education and Student Achievement: A Regression-Discontinuity Analysis.
ERIC Educational Resources Information Center
Jacob, Brian A.; Lefgren, Lars
This study used a regression discontinuity design to examine the causal effect of summer school and grade retention on student achievement. In 1996, the Chicago Public Schools instituted an accountability policy that tied summer school attendance and promotional decisions to performance on standardized tests, which created a highly non-linear…
Measuring Habituation in Infants: An Approach Using Regression Analysis.
ERIC Educational Resources Information Center
Ashmead, Daniel H.; Davis, DeFord L.
1996-01-01
Used computer simulations to examine effectiveness of different criteria for measuring infant visual habituation. Found that a criterion based on fitting a second-order polynomial regression function to looking-time data produced more accurate estimation of looking times and higher power for detecting novelty effects than did the traditional…
Growth in Mathematics Achievement: Analysis with Classification and Regression Trees
ERIC Educational Resources Information Center
Ma, Xin
2005-01-01
A recently developed statistical technique, often referred to as classification and regression trees (CART), holds great potential for researchers to discover how student-level (and school-level) characteristics interactively affect growth in mathematics achievement. CART is a host of advanced statistical methods that statistically cluster…
Grades, Gender, and Encouragement: A Regression Discontinuity Analysis
ERIC Educational Resources Information Center
Owen, Ann L.
2010-01-01
The author employs a regression discontinuity design to provide direct evidence on the effects of grades earned in economics principles classes on the decision to major in economics and finds a differential effect for male and female students. Specifically, for female students, receiving an A for a final grade in the first economics class is…
Grades, Gender, and Encouragement: A Regression Discontinuity Analysis
ERIC Educational Resources Information Center
Owen, Ann L.
2010-01-01
The author employs a regression discontinuity design to provide direct evidence on the effects of grades earned in economics principles classes on the decision to major in economics and finds a differential effect for male and female students. Specifically, for female students, receiving an A for a final grade in the first economics class is…
Li, Qing; Liu, Liu; Liu, Yanling; Zhou, Huirong; Yang, Zhi; Yuan, Keng; Min, Weiping
2015-01-01
The correlationship between COX-2 gene polymorphisms and breast cancer has been wildly studied, but the results remain controversial. Hence, the present meta-analysis aimed to investigate the association between COX-2 SNPs (rs5275, rs20417, rs689466, rs5277, rs2206593) and risk of breast cancer. Data were collected from PubMed, Embase and China National Knowledge Infrastructure. Summary odds ratio (OR) with 95 % confidence interval (CI) was applied to assess the relationship. Heterogeneity test, sensitivity analysis and publication bias test were also performed. There were 17 articles that contained 19 studies in this research. Fourteen case-control studies with 15,007 breast cancer cases and 20,005 controls were concerning rs5275 polymorphism, and 8 case-control studies with 10,216 cases and 12,839 controls were about rs20417 polymorphism. Other three polymorphisms (rs689466, rs2206593, rs5277) were studied in 5, 3 and 3 studies, respectively. COX-2-rs20417 CC genotype was significantly associated with increased risk of breast cancer when comparing to G allele [ORs were 1.231 (1.050-1.444) for CC vs. GG, P = 0.01, 1.223 (1.045-1.432) for CC vs. G carrier, P = 0.01]. Furthermore, the results of the subgroup analysis by ethnicity suggested that C allele significantly contributed to the risk of breast cancer for Asians [1.459 (1.182-1.802) for GC vs. GG, 1.472 (1.201-1.805) for C carrier vs. GG]. However, no association was found for rs5275, rs689466, rs5277 and rs2206593 in all comparison modes. This meta-analysis indicated that the COX-2 rs20417 polymorphism contributed to genetic susceptibility of breast cancer. In contrast, COX-2 rs5275, rs689466, rs2206593 and rs5277 polymorphisms might be not associated with the risk of breast cancer.
Sharbatkhori, Mitra; Fasihi Harandi, Majid; Mirhendi, Hossein; Hajialilo, Elham; Kia, Eshrat Beigom
2011-03-01
Nineteen hydatid cyst isolates collected from camels in central Iran were subjected to sequences analysis of mitochondrial cytochrome c oxidase subunit 1 (cox1) and NADH dehydrogenase subunit 1 (nad1) genes. A consensus sequence obtained containing 366 nucleotides for cox1 and 471 nucleotides for nad1 genes. Overall, the camel isolates indicated five different sequences in cox1 and nine in nad1 genes. The sequences analysis indicated that 26.3%, 42.1%, and 31.6% of isolates belonging to G1, G3, and G6 genotypes of Echinococcus granulosus, respectively. The isolates with G3 genotype indicated one cox1 sequence having 100% homology with reference G3 sequence (AN: M84663) and two different nad1 sequences, one having 100% homology with reference G3 sequence (AN: AJ237634) and the other with a silent mutation (G to A) in position 279. The presence of G3 genotype (buffalo strain) of E. granulosus as dominant genotype in camels is emphasized. As G3 genotype has formerly been reported in human, the epidemiological role of camels is warranted in future surveys.
HIGH RESOLUTION FOURIER ANALYSIS WITH AUTO-REGRESSIVE LINEAR PREDICTION
Barton, J.; Shirley, D.A.
1984-04-01
Auto-regressive linear prediction is adapted to double the resolution of Angle-Resolved Photoemission Extended Fine Structure (ARPEFS) Fourier transforms. Even with the optimal taper (weighting function), the commonly used taper-and-transform Fourier method has limited resolution: it assumes the signal is zero beyond the limits of the measurement. By seeking the Fourier spectrum of an infinite extent oscillation consistent with the measurements but otherwise having maximum entropy, the errors caused by finite data range can be reduced. Our procedure developed to implement this concept adapts auto-regressive linear prediction to extrapolate the signal in an effective and controllable manner. Difficulties encountered when processing actual ARPEFS data are discussed. A key feature of this approach is the ability to convert improved measurements (signal-to-noise or point density) into improved Fourier resolution.
Academic Achievement, Intelligence, and Creativity: A Regression Surface Analysis.
Marjoribanks, K
1976-01-01
Data collected on 400 12-year-old English school children were used to examine relations between measures of intelligence, creativity and academic achievement. Complex multiple regression models, which included terms to account for the possible interaction and curvilinear relations between intelligence, creativity and academic achievement were used to construct regression surfaces. The surfaces showed that the traditional threshold hypothesis, which suggests that beyond a certain level of intelligence academic achievement is related increasingly to creativity and ceases to be related strongly to intelligence, was not supported. For some areas of academic performance the results suggest an alternate proposition, that creativity ceases to be related to achievement after a threshold level of intelligence has been reached. It was also found that at high levels of verbal ability, non-verbal ability and creativity appeared to have differential relations with academic achievement.
Heteroscedastic regression analysis of factors affecting BMD monitoring.
Sadatsafavi, Mohsen; Moayyeri, Alireza; Wang, Liqun; Leslie, William D
2008-11-01
Identifying factors affecting BMD precision and interindividual heterogeneity in BMD change can help optimize BMD monitoring. BMD change for the lumbar spine and total hip for short-term reproducibility (n = 328) and long-term clinical monitoring (n = 2720) populations were analyzed with heteroscedastic regression using linear prediction for mean (monitoring population only) and log-linear prediction for SD (both populations). For clinical monitoring, male sex, baseline body mass index (BMI), and systemic corticosteroid use were associated with greater SD of BMD change. Weight gain was negatively associated with SD for the hip, whereas height change was positively associated with SD for the spine. Each additional year of monitoring increased the SD by 6.5-9.2%. Osteoporosis treatment affected mean change but did not increase dispersion. For short-term reproducibility, performing scans on a different day increased the SD of measurement error by 38-44%. Baseline BMD, difference in bone area, and a repeat scan performed by different technologists were associated with higher measurement error only for the hip. For both samples, heteroscedastic regression outperformed models that assumed homogeneous variance. Heteroscedastic regression techniques are powerful yet underused tools in analyzing longitudinal BMD data and can be used to generate individualized predictions of BMD change and measurement error.
Regression Analysis of Correlated Ordinal Data Using Orthogonalized Residuals
Perin, J.; Preisser, J. S.; Phillips, C.; Qaqish, B.
2015-01-01
Summary Semi-parametric regression models for the joint estimation of marginal mean and within-cluster pairwise association parameters are used in a variety of settings for population-averaged modeling of multivariate categorical outcomes. Recently, a formulation of alternating logistic regressions based on orthogonalized, marginal residuals has been introduced for correlated binary data. Unlike the original procedure based on conditional residuals, its covariance estimator is invariant to the ordering of observations within clusters. In this article, the orthogonalized residuals method is extended to model correlated ordinal data with a global odds ratio, and shown in a simulation study to be more eficient and less biased with regards to estimating within-cluster association parameters than an existing extension to ordinal data of alternating logistic regressions based on conditional residuals. Orthogonalized residuals are used to estimate a model for three correlated ordinal outcomes measured repeatedly in a longitudinal clinical trial of an intervention to improve recovery of patients’ perception of altered sensation following jaw surgery. PMID:25134789
Regression analysis of correlated ordinal data using orthogonalized residuals.
Perin, J; Preisser, J S; Phillips, C; Qaqish, B
2014-12-01
Semi-parametric regression models for the joint estimation of marginal mean and within-cluster pairwise association parameters are used in a variety of settings for population-averaged modeling of multivariate categorical outcomes. Recently, a formulation of alternating logistic regressions based on orthogonalized, marginal residuals has been introduced for correlated binary data. Unlike the original procedure based on conditional residuals, its covariance estimator is invariant to the ordering of observations within clusters. In this article, the orthogonalized residuals method is extended to model correlated ordinal data with a global odds ratio, and shown in a simulation study to be more efficient and less biased with regards to estimating within-cluster association parameters than an existing extension to ordinal data of alternating logistic regressions based on conditional residuals. Orthogonalized residuals are used to estimate a model for three correlated ordinal outcomes measured repeatedly in a longitudinal clinical trial of an intervention to improve recovery of patients' perception of altered sensation following jaw surgery. © 2014, The International Biometric Society.
A new approach in regression analysis for modeling adsorption isotherms.
Marković, Dana D; Lekić, Branislava M; Rajaković-Ognjanović, Vladana N; Onjia, Antonije E; Rajaković, Ljubinka V
2014-01-01
Numerous regression approaches to isotherm parameters estimation appear in the literature. The real insight into the proper modeling pattern can be achieved only by testing methods on a very big number of cases. Experimentally, it cannot be done in a reasonable time, so the Monte Carlo simulation method was applied. The objective of this paper is to introduce and compare numerical approaches that involve different levels of knowledge about the noise structure of the analytical method used for initial and equilibrium concentration determination. Six levels of homoscedastic noise and five types of heteroscedastic noise precision models were considered. Performance of the methods was statistically evaluated based on median percentage error and mean absolute relative error in parameter estimates. The present study showed a clear distinction between two cases. When equilibrium experiments are performed only once, for the homoscedastic case, the winning error function is ordinary least squares, while for the case of heteroscedastic noise the use of orthogonal distance regression or Margart's percent standard deviation is suggested. It was found that in case when experiments are repeated three times the simple method of weighted least squares performed as well as more complicated orthogonal distance regression method.
Multi-Class Sparse Bayesian Regression for Neuroimaging Data Analysis
NASA Astrophysics Data System (ADS)
Michel, Vincent; Eger, Evelyn; Keribin, Christine; Thirion, Bertrand
The use of machine learning tools is gaining popularity in neuroimaging, as it provides a sensitive assessment of the information conveyed by brain images. In particular, finding regions of the brain whose functional signal reliably predicts some behavioral information makes it possible to better understand how this information is encoded or processed in the brain. However, such a prediction is performed through regression or classification algorithms that suffer from the curse of dimensionality, because a huge number of features (i.e. voxels) are available to fit some target, with very few samples (i.e. scans) to learn the informative regions. A commonly used solution is to regularize the weights of the parametric prediction function. However, model specification needs a careful design to balance adaptiveness and sparsity. In this paper, we introduce a novel method, Multi - Class Sparse Bayesian Regression(MCBR), that generalizes classical approaches such as Ridge regression and Automatic Relevance Determination. Our approach is based on a grouping of the features into several classes, where each class is regularized with specific parameters. We apply our algorithm to the prediction of a behavioral variable from brain activation images. The method presented here achieves similar prediction accuracies than reference methods, and yields more interpretable feature loadings.
A New Approach in Regression Analysis for Modeling Adsorption Isotherms
Onjia, Antonije E.
2014-01-01
Numerous regression approaches to isotherm parameters estimation appear in the literature. The real insight into the proper modeling pattern can be achieved only by testing methods on a very big number of cases. Experimentally, it cannot be done in a reasonable time, so the Monte Carlo simulation method was applied. The objective of this paper is to introduce and compare numerical approaches that involve different levels of knowledge about the noise structure of the analytical method used for initial and equilibrium concentration determination. Six levels of homoscedastic noise and five types of heteroscedastic noise precision models were considered. Performance of the methods was statistically evaluated based on median percentage error and mean absolute relative error in parameter estimates. The present study showed a clear distinction between two cases. When equilibrium experiments are performed only once, for the homoscedastic case, the winning error function is ordinary least squares, while for the case of heteroscedastic noise the use of orthogonal distance regression or Margart's percent standard deviation is suggested. It was found that in case when experiments are repeated three times the simple method of weighted least squares performed as well as more complicated orthogonal distance regression method. PMID:24672394
Liu, Xiang; Saat, M Rapik; Qin, Xiao; Barkan, Christopher P L
2013-10-01
Derailments are the most common type of freight-train accidents in the United States. Derailments cause damage to infrastructure and rolling stock, disrupt services, and may cause casualties and harm the environment. Accordingly, derailment analysis and prevention has long been a high priority in the rail industry and government. Despite the low probability of a train derailment, the potential for severe consequences justify the need to better understand the factors influencing train derailment severity. In this paper, a zero-truncated negative binomial (ZTNB) regression model is developed to estimate the conditional mean of train derailment severity. Recognizing that the mean is not the only statistic describing data distribution, a quantile regression (QR) model is also developed to estimate derailment severity at different quantiles. The two regression models together provide a better understanding of train derailment severity distribution. Results of this work can be used to estimate train derailment severity under various operational conditions and by different accident causes. This research is intended to provide insights regarding development of cost-efficient train safety policies.
Striker, Lora K.; Medalie, Laura
1997-01-01
This report provides the results of a detailed Level II analysis of scour potential at structure MORETH00010021 on Town Highway 1 crossing Cox Brook, Moretown, Vermont (figures 1–8). A Level II study is a basic engineering analysis of the site, including a quantitative analysis of stream stability and scour (U.S. Department of Transportation, 1993). Results of a Level I scour investigation also are included in Appendix E of this report. A Level I investigation provides a qualitative geomorphic characterization of the study site. Information on the bridge, gleaned from Vermont Agency of Transportation (VTAOT) files, was compiled prior to conducting Level I and Level II analyses and is found in Appendix D. The site is in the Green Mountain section of the New England physiographic province in north-central Vermont. The 2.85-mi2 drainage area is in a predominantly rural and forested basin. In the vicinity of the study site, the surface cover is predominantly forested. In the study area, Cox Brook has an incised, sinuous channel with a slope of approximately 0.02 ft/ft, an average channel top width of 23 ft and an average bank height of 4 ft. The channel bed material ranges from gravel to cobble with a median grain size (D50) of 47.5 mm (0.156 ft). The geomorphic assessment at the time of the Level I and Level II site visit on July 18, 1996, indicated that the reach was stable. The Town Highway 1 crossing of Cox Brook is a 29-ft-long, two-lane bridge consisting of one 27-foot steel-beam span (Vermont Agency of Transportation, written communication, October 13, 1995). The opening length of the structure parallel to the bridge face is 24.8 ft. The bridge is supported by vertical, concrete abutments with wingwalls. The channel is skewed approximately 60 degrees to the opening while the measured opening-skew-to-roadway is 40 degrees. A scour hole 1.0 ft deeper than the mean thalweg depth was observed along the left abutment downstream during the Level I assessment. The
USDA-ARS?s Scientific Manuscript database
Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...
Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models
ERIC Educational Resources Information Center
Shieh, Gwowen
2009-01-01
In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference…
Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models
ERIC Educational Resources Information Center
Shieh, Gwowen
2009-01-01
In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference…
Dong, Jing; Dai, Juncheng; Zhang, Mingfeng; Hu, Zhibin; Shen, Hongbing
2010-06-01
Three potentially functional polymorphisms: -765G>C, -1195G>A, and 8473T>C in the cyclooxygenase-2 (COX-2) gene were identified and proposed to be associated with cancer susceptibility. The aim of this meta-analysis was to evaluate the association between these three polymorphisms and the risk of cancer in diverse populations. All case-control studies published up to November 2009 on the association between the three polymorphisms of COX-2 and cancer risk were identified by searching PubMed. The cancer risk associated with the three polymorphisms of the COX-2 gene was estimated for each study by OR together with its 95% confidence interval (CI), respectively. A total of 47 case-control studies were included, and variant genotypes GA/AA of -1195G>A were associated with a significantly increased cancer risk (GA/AA vs GG: odds ratio [OR], 1.29; 95% CI, 1.18-1.41; P(heterogeneity) = 0.113), and this significant association was mainly observed within cancers of the digestive system (e.g. colorectal, gastric, esophageal, oral, biliary tract, gallbladder, and pancreatic) without between-study heterogeneity (GA/AA vs GG: OR, 1.36; 95% CI; 1.23-1.51; P(heterogeneity) = 0.149). Furthermore, a stratification analysis showed that the risk of COX-2-1195G>A associated with cancers in the digestive system was more evident among Asians than Caucasians. However, for COX-2-765G>C and 8473T>C, no convincing association between the two polymorphisms and risk of cancer or cancer type was observed. The effect of three potentially functional polymorphisms (-765G>C, -1195G>A, and 8473T>C) in the COX-2 gene on cancer risk provided evidence that the COX-2-1195G>A polymorphism was significantly associated with increased risk of digestive system cancers, especially among Asian populations.
Regression analysis study on the carbon dioxide capture process
Zhou, Q.; Chan, C.W.; Tontiwachiwuthikul, P.
2008-07-15
Research on amine-based carbon dioxide (CO{sub 2}) capture has mainly focused on improving the effectiveness and efficiency of the CO{sub 2} capture process. The objective of our work is to explore relationships among key parameters that affect the CO{sub 2} production rate. From a survey of relevant literature, we observed that the significant parameters influencing the CO{sub 2} production rate include the reboiler heat duty, solvent concentration, solvent circulation rate, and CO{sub 2} lean loading. While it is widely recognized that these parameters are related, the exact nature of the relationships are unknown. This paper presents a regression study conducted with data collected at the International Test Center for CO{sub 2} capture (ITC) located at University of Regina, Saskatchewan, Canada. The regression technique was applied to a data set consisting of data on 113 days of operation of the CO{sub 2} capture plant, and four mathematical models of the key parameters have been developed. The models can be used for predicting the performance of the plant when changes occur in the process. By manipulation of the parameter values, the efficiency of the CO{sub 2} capture process can be improved.
NASA Technical Reports Server (NTRS)
Parsons, Vickie s.
2009-01-01
The request to conduct an independent review of regression models, developed for determining the expected Launch Commit Criteria (LCC) External Tank (ET)-04 cycle count for the Space Shuttle ET tanking process, was submitted to the NASA Engineering and Safety Center NESC on September 20, 2005. The NESC team performed an independent review of regression models documented in Prepress Regression Analysis, Tom Clark and Angela Krenn, 10/27/05. This consultation consisted of a peer review by statistical experts of the proposed regression models provided in the Prepress Regression Analysis. This document is the consultation's final report.
CADDIS Volume 4. Data Analysis: PECBO Appendix - R Scripts for Non-Parametric Regressions
Script for computing nonparametric regression analysis. Overview of using scripts to infer environmental conditions from biological observations, statistically estimating species-environment relationships, statistical scripts.
Regression analysis for multiple-disease group testing data
Zhang, Boan; Bilder, Christopher R.; Tebbs, Joshua M.
2015-01-01
Group testing, where individual specimens are composited into groups to test for the presence of a disease (or other binary characteristic), is a procedure commonly used to reduce the costs of screening a large number of individuals. Group testing data are unique in that only group responses may be available, but inferences are needed at the individual level. A further methodological challenge arises when individuals are tested in groups for multiple diseases simultaneously, because unobserved individual disease statuses are likely correlated. In this paper, we propose new regression techniques for multiple-disease group testing data. We develop an expectation-solution based algorithm that provides consistent parameter estimates and natural large-sample inference procedures. Our proposed methodology is applied to chlamydia and gonorrhea screening data collected in Nebraska as part of the Infertility Prevention Project and to prenatal infectious disease screening data from Kenya. PMID:23703944
Regression analysis for multiple-disease group testing data.
Zhang, Boan; Bilder, Christopher R; Tebbs, Joshua M
2013-12-10
Group testing, where individual specimens are composited into groups to test for the presence of a disease (or other binary characteristic), is a procedure commonly used to reduce the costs of screening a large number of individuals. Group testing data are unique in that only group responses may be available, but inferences are needed at the individual level. A further methodological challenge arises when individuals are tested in groups for multiple diseases simultaneously, because unobserved individual disease statuses are likely correlated. In this paper, we propose new regression techniques for multiple-disease group testing data. We develop an expectation-solution based algorithm that provides consistent parameter estimates and natural large-sample inference procedures. We apply our proposed methodology to chlamydia and gonorrhea screening data collected in Nebraska as part of the Infertility Prevention Project and to prenatal infectious disease screening data from Kenya. Copyright © 2013 John Wiley & Sons, Ltd.
Yang, Man; Wang, Hong-Tao; Zhao, Miao; Meng, Wen-Bo; Ou, Jin-Qing; He, Jun-Hui; Zou, Bing; Lei, Ping-Guang
2015-01-01
Abstract Currently 2 difference classes of cyclooxygenase (COX)-2 inhibitors, coxibs and relatively selective COX-2 inhibitors, are available for patients requiring nonsteroidal anti-inflammatory drug (NSAID) therapy; their gastroprotective effect is hardly directly compared. The aim of this study was to compare the gastroprotective effect of relatively selective COX-2 inhibitors with coxibs. MEDLINE, EMBASE, and the Cochrane Library (from their inception to March 2015) were searched for potential eligible studies. We included randomized controlled trials comparing coxibs (celecoxib, etoricoxib, parecoxib, and lumiracoxib), relatively selective COX-2 inhibitors (nabumetone, meloxicam, and etodolac), and nonselective NSAIDs with a study duration ≥4 weeks. Comparative effectiveness and safety data were pooled by Bayesian network meta-analysis. The primary outcomes were ulcer complications and symptomatic ulcer. Summary effect-size was calculated as risk ratio (RR), together with the 95% confidence interval (CI). This study included 36 trials with a total of 112,351 participants. Network meta-analyses indicated no significant difference between relatively selective COX-2 inhibitors and coxibs regarding ulcer complications (RR, 1.38; 95% CI, 0.47–3.27), symptomatic ulcer (RR, 1.02; 95% CI, 0.09–3.92), and endoscopic ulcer (RR, 1.18; 95% CI, 0.37–2.96). Network meta-analyses adjusting potential influential factors (age, sex, previous ulcer disease, and follow-up time), and sensitivity analyses did not reveal any major change to the main results. Network meta-analyses suggested that relatively selective COX-2 inhibitors and coxibs were associated with comparable incidences of total adverse events (AEs) (RR, 1.09; 95% CI, 0.93–1.31), gastrointestinal AEs (RR, 1.04; 95% CI, 0.87–1.25), total withdrawals (RR, 1.00; 95% CI, 0.74–1.33), and gastrointestinal AE-related withdrawals (RR, 1.02; 95% CI, 0.57–1.74). Relatively selective COX-2 inhibitors appear to be
Quantile regression analysis of body mass and wages.
Johar, Meliyanni; Katayama, Hajime
2012-05-01
Using the National Longitudinal Survey of Youth 1979, we explore the relationship between body mass and wages. We use quantile regression to provide a broad description of the relationship across the wage distribution. We also allow the relationship to vary by the degree of social skills involved in different jobs. Our results find that for female workers body mass and wages are negatively correlated at all points in their wage distribution. The strength of the relationship is larger at higher-wage levels. For male workers, the relationship is relatively constant across wage distribution but heterogeneous across ethnic groups. When controlling for the endogeneity of body mass, we find that additional body mass has a negative causal impact on the wages of white females earning more than the median wages and of white males around the median wages. Among these workers, the wage penalties are larger for those employed in jobs that require extensive social skills. These findings may suggest that labor markets reward white workers for good physical shape differently, depending on the level of wages and the type of job a worker has. Copyright © 2011 John Wiley & Sons, Ltd.
Modeling Information Content Via Dirichlet-Multinomial Regression Analysis.
Ferrari, Alberto
2017-02-16
Shannon entropy is being increasingly used in biomedical research as an index of complexity and information content in sequences of symbols, e.g. languages, amino acid sequences, DNA methylation patterns and animal vocalizations. Yet, distributional properties of information entropy as a random variable have seldom been the object of study, leading to researchers mainly using linear models or simulation-based analytical approach to assess differences in information content, when entropy is measured repeatedly in different experimental conditions. Here a method to perform inference on entropy in such conditions is proposed. Building on results coming from studies in the field of Bayesian entropy estimation, a symmetric Dirichlet-multinomial regression model, able to deal efficiently with the issue of mean entropy estimation, is formulated. Through a simulation study the model is shown to outperform linear modeling in a vast range of scenarios and to have promising statistical properties. As a practical example, the method is applied to a data set coming from a real experiment on animal communication.
Analysis of Covariance with Linear Regression Error Model on Antenna Control Unit Tracking
2015-10-20
412TW-PA-15238 Analysis of Covariance with Linear Regression Error Model on Antenna Control Unit Tracking DANIEL T. LAIRD AIR...COVERED (From - To) 20 OCT 15 – 23 OCT 15 4. TITLE AND SUBTITLE Analysis of Covariance with Linear Regression Error Model on Antenna Control Tracking...supplement technical expertise, rather than rely solely on expertise, which is subjective. In this paper we apply linear regression modeling and
Gogoi, Dhrubajyoti; Bezbaruah, Rajib Lochan; Bordoloi, Manabjyoti; Sarmah, Rajeev; Bora, Tarun Chandra
2012-01-01
Litsea spp of Laural family are traditionally used as herbal medicine for treating inflammation including gastroenterologia, oedema and rheumatic arthritis. Therefore, it is of interest to investigate and understand the molecular principles for such actions. Here, we have illustrated the binding of thirteen Litsea derived biologically active compounds against the inflammation associated target COX (cyclo-oxygenase) -2 enzymes. We compared the binding information of these compounds with a selected number of already known COX-2 inhibitors. The comparison reflected that some of these compounds such as linderol, catechin, 6'-hydroxy-2',3',4' - trimethoxy-chalcone and litseaone have better or equivalent binding features compared to already known inhibitory compounds namely celecoxib, acetylsalicylic acid, rofecoxib. Therefore, all these small compounds reported from plant Litsea spp were found to possess potential medicinal values with anti-inflammatory properties.
Gogoi, Dhrubajyoti; Bezbaruah, Rajib Lochan; Bordoloi, Manabjyoti; Sarmah, Rajeev; Bora, Tarun Chandra
2012-01-01
Litsea spp of Laural family are traditionally used as herbal medicine for treating inflammation including gastroenterologia, oedema and rheumatic arthritis. Therefore, it is of interest to investigate and understand the molecular principles for such actions. Here, we have illustrated the binding of thirteen Litsea derived biologically active compounds against the inflammation associated target COX (cyclo-oxygenase) -2 enzymes. We compared the binding information of these compounds with a selected number of already known COX-2 inhibitors. The comparison reflected that some of these compounds such as linderol, catechin, 6'-hydroxy-2',3',4' - trimethoxy-chalcone and litseaone have better or equivalent binding features compared to already known inhibitory compounds namely celecoxib, acetylsalicylic acid, rofecoxib. Therefore, all these small compounds reported from plant Litsea spp were found to possess potential medicinal values with anti-inflammatory properties. PMID:23139590
Zarei, Zabiholah; Kia, Eshrat Beigom; Heidari, Zahra; Mikaeili, Fattaneh; Mohebali, Mehdi; Sharifdini, Meysam
2016-01-01
Dirofilaria immitis is an important filarial nematode in dogs. In this study, age and sex distribution of this zoonotic nematode among dogs were investigated in northwest of Iran in Meshkin-Shahr city. Molecular characteristics of the isolates, based on cytochrome oxidase subunit 1 (COX1) gene were compared to the isolates from other areas of the world.Blood samples were collected from 91 dogs which were selected by simple classified accidental sampling. Thin and thick blood smear examinations were used to find out infectivity with D. immitis. DNA extraction was performed from adult D. immitis recovered from heart of infected dogs. The COX1 gene was amplified and sequenced. Phylogenetic analysis was carried out using sequences obtained in this study along with relevant sequences deposited in the GenBank. Phylogenetic analysis and sequence variation was performed using MEGA software in comparison with those COX1 sequences deposited in GenBank. Out of 91 dogs, 19 (20.87%) were found positive for infection with D. immitis. There was no statistically significant difference between males and females of dogs in terms of D. immitis infection. However, the rate of infection in dogs more than 2 years old was significantly higher than those with lower age. Both sequences analyzed in this study showed 100% homology to each other. Intra-species variation of these isolates with those from other areas of the world amounted to 0 to 0.50%. Phylogenetic analysis of the COX1 gene suggested that it is conserved, and can be used for study on genetic diversity and classification of filarial nematodes. PMID:28144425
Ritter, Merrill A; Harty, Leesa D; Davis, Kenneth E; Meding, John B; Berend, Michael E
2003-07-01
Range of motion is a crucial measure of the outcome of total knee arthroplasty. The purpose of this study was to determine which factors are predictive of the postoperative range of motion. We retrospectively studied 3066 patients (4727 knees) who had a primary total knee arthroplasty with the same type of implant at the same center between 1983 and 1998. Statistical clustering analysis paired with log-linear regression was used to determine groupings along continuous variables. Regression tree analysis was used to characterize the combinations of variables influencing the postoperative range of motion. The variables considered were preoperative and intraoperative flexion and extension, preoperative alignment, age, gender, and soft-tissue releases. Preoperative flexion was the strongest predictor of the postoperative flexion regardless of preoperative alignment. Other factors that were significantly related to reduced flexion were intraoperative flexion (p < 0.0001), gender (p < 0.0001), preoperative tibiofemoral alignment (p = 0.0005), age (p < 0.0001), and posterior capsular release (p < 0.0001). The removal of posterior osteophytes was related to the greatest increase in postoperative flexion in the group of patients with a varus tibiofemoral alignment preoperatively. The principal predictive factor of the postoperative range of motion was the preoperative range of motion. Removal of posterior osteophytes and release of the deep medial collateral ligament, the semimembranosus tendon, and the pes anserinus tendon in patients with large preoperative varus alignment and the attainment of a good intraoperative range of motion improved the likelihood that a good postoperative range of motion would be achieved.
Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis
ERIC Educational Resources Information Center
Williams, Ryan T.
2012-01-01
Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…
Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis
ERIC Educational Resources Information Center
Williams, Ryan T.
2012-01-01
Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…
A Quality Assessment Tool for Non-Specialist Users of Regression Analysis
ERIC Educational Resources Information Center
Argyrous, George
2015-01-01
This paper illustrates the use of a quality assessment tool for regression analysis. It is designed for non-specialist "consumers" of evidence, such as policy makers. The tool provides a series of questions such consumers of evidence can ask to interrogate regression analysis, and is illustrated with reference to a recent study published…
Advanced GIS Exercise: Predicting Rainfall Erosivity Index Using Regression Analysis
ERIC Educational Resources Information Center
Post, Christopher J.; Goddard, Megan A.; Mikhailova, Elena A.; Hall, Steven T.
2006-01-01
Graduate students from a variety of agricultural and natural resource fields are incorporating geographic information systems (GIS) analysis into their graduate research, creating a need for teaching methodologies that help students understand advanced GIS topics for use in their own research. Graduate-level GIS exercises help students understand…
A Noncentral "t" Regression Model for Meta-Analysis
ERIC Educational Resources Information Center
Camilli, Gregory; de la Torre, Jimmy; Chiu, Chia-Yi
2010-01-01
In this article, three multilevel models for meta-analysis are examined. Hedges and Olkin suggested that effect sizes follow a noncentral "t" distribution and proposed several approximate methods. Raudenbush and Bryk further refined this model; however, this procedure is based on a normal approximation. In the current research literature, this…
Microhabitat analysis using radiotelemetry locations and polytomous logistic regression
Malcolm P. North; Joel H. Reynolds
1996-01-01
Microhabitat analyses often use discriminant function analysis (DFA) to compare vegetation structures or environmental conditions between sites classified by a study animal's presence or absence. These presence/absence studies make questionable assumptions about the habitat value of the comparison sites and the microhabitat data often violate the DFA's...
A Noncentral "t" Regression Model for Meta-Analysis
ERIC Educational Resources Information Center
Camilli, Gregory; de la Torre, Jimmy; Chiu, Chia-Yi
2010-01-01
In this article, three multilevel models for meta-analysis are examined. Hedges and Olkin suggested that effect sizes follow a noncentral "t" distribution and proposed several approximate methods. Raudenbush and Bryk further refined this model; however, this procedure is based on a normal approximation. In the current research literature, this…
Advanced GIS Exercise: Predicting Rainfall Erosivity Index Using Regression Analysis
ERIC Educational Resources Information Center
Post, Christopher J.; Goddard, Megan A.; Mikhailova, Elena A.; Hall, Steven T.
2006-01-01
Graduate students from a variety of agricultural and natural resource fields are incorporating geographic information systems (GIS) analysis into their graduate research, creating a need for teaching methodologies that help students understand advanced GIS topics for use in their own research. Graduate-level GIS exercises help students understand…
An improved multiple linear regression and data analysis computer program package
NASA Technical Reports Server (NTRS)
Sidik, S. M.
1972-01-01
NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.
Analysis of cost regression and post-accident absence
NASA Astrophysics Data System (ADS)
Wojciech, Drozd
2017-07-01
The article presents issues related with costs of work safety. It proves the thesis that economic aspects cannot be overlooked in effective management of occupational health and safety and that adequate expenditures on safety can bring tangible benefits to the company. Reliable analysis of this problem is essential for the description the problem of safety the work. In the article attempts to carry it out using the procedures of mathematical statistics [1, 2, 3].
Regression Analysis of a Disease Onset Distribution Using Diagnosis Data
Young, Jessica G.; Jewell, Nicholas P.; Samuels, Steven J.
2008-01-01
Summary We consider methods for estimating the effect of a covariate on a disease onset distribution when the observed data structure consists of right-censored data on diagnosis times and current status data on onset times amongst individuals who have not yet been diagnosed. Dunson and Baird (2001, Biometrics 57, 306–403) approached this problem using maximum likelihood, under the assumption that the ratio of the diagnosis and onset distributions is monotonic nondecreasing. As an alternative, we propose a two-step estimator, an extension of the approach of van der Laan, Jewell, and Petersen (1997, Biometrika 84, 539–554) in the single sample setting, which is computationally much simpler and requires no assumptions on this ratio. A simulation study is performed comparing estimates obtained from these two approaches, as well as that from a standard current status analysis that ignores diagnosis data. Results indicate that the Dunson and Baird estimator outperforms the two-step estimator when the monotonicity assumption holds, but the reverse is true when the assumption fails. The simple current status estimator loses only a small amount of precision in comparison to the two-step procedure but requires monitoring time information for all individuals. In the data that motivated this work, a study of uterine fibroids and chemical exposure to dioxin, the monotonicity assumption is seen to fail. Here, the two-step and current status estimators both show no significant association between the level of dioxin exposure and the hazard for onset of uterine fibroids; the two-step estimator of the relative hazard associated with increasing levels of exposure has the least estimated variance amongst the three estimators considered. PMID:17680832
Analysis of Differential Item Functioning (DIF) Using Hierarchical Logistic Regression Models.
ERIC Educational Resources Information Center
Swanson, David B.; Clauser, Brian E.; Case, Susan M.; Nungester, Ronald J.; Featherman, Carol
2002-01-01
Outlines an approach to differential item functioning (DIF) analysis using hierarchical linear regression that makes it possible to combine results of logistic regression analyses across items to identify consistent sources of DIF, to quantify the proportion of explained variation in DIF coefficients, and to compare the predictive accuracy of…
L.R. Grosenbaugh
1967-01-01
Describes an expansible computerized system that provides data needed in regression or covariance analysis of as many as 50 variables, 8 of which may be dependent. Alternatively, it can screen variously generated combinations of independent variables to find the regression with the smallest mean-squared-residual, which will be fitted if desired. The user can easily...
Robust analysis of trends in noisy tokamak confinement data using geodesic least squares regression
Verdoolaege, G.; Shabbir, A.; Hornung, G.
2016-11-15
Regression analysis is a very common activity in fusion science for unveiling trends and parametric dependencies, but it can be a difficult matter. We have recently developed the method of geodesic least squares (GLS) regression that is able to handle errors in all variables, is robust against data outliers and uncertainty in the regression model, and can be used with arbitrary distribution models and regression functions. We here report on first results of application of GLS to estimation of the multi-machine scaling law for the energy confinement time in tokamaks, demonstrating improved consistency of the GLS results compared to standard least squares.
Modeling the energy content of municipal solid waste using multiple regression analysis
Liu, J.I.; Paode, R.D.; Holsen, T.M,
1996-07-01
In this research multiple regression analysis was used to develop predictive models of the energy content of municipal solid waste (MSW). The scope of work included collecting waste samples in Kaohsiung City, Taiwan, characterizing the waste, and performing a stepwise forward selection procedure for isolating variables. Two regression models were developed to correlate the energy content with variables derived from physical composition and ultimate analysis. The performance of these models for this particular waste was superior to that of equations developed by other researchers (e.g., Dulong, Steuer) for estimating energy content. Attempts at developing regression models from proximate analysis data were not successful. 6 refs., 8 figs., 2 tabs.
Development of a User Interface for a Regression Analysis Software Tool
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert Manfred; Volden, Thomas R.
2010-01-01
An easy-to -use user interface was implemented in a highly automated regression analysis tool. The user interface was developed from the start to run on computers that use the Windows, Macintosh, Linux, or UNIX operating system. Many user interface features were specifically designed such that a novice or inexperienced user can apply the regression analysis tool with confidence. Therefore, the user interface s design minimizes interactive input from the user. In addition, reasonable default combinations are assigned to those analysis settings that influence the outcome of the regression analysis. These default combinations will lead to a successful regression analysis result for most experimental data sets. The user interface comes in two versions. The text user interface version is used for the ongoing development of the regression analysis tool. The official release of the regression analysis tool, on the other hand, has a graphical user interface that is more efficient to use. This graphical user interface displays all input file names, output file names, and analysis settings for a specific software application mode on a single screen which makes it easier to generate reliable analysis results and to perform input parameter studies. An object-oriented approach was used for the development of the graphical user interface. This choice keeps future software maintenance costs to a reasonable limit. Examples of both the text user interface and graphical user interface are discussed in order to illustrate the user interface s overall design approach.
Quality of life in breast cancer patients--a quantile regression analysis.
Pourhoseingholi, Mohamad Amin; Safaee, Azadeh; Moghimi-Dehkordi, Bijan; Zeighami, Bahram; Faghihzadeh, Soghrat; Tabatabaee, Hamid Reza; Pourhoseingholi, Asma
2008-01-01
Quality of life study has an important role in health care especially in chronic diseases, in clinical judgment and in medical resources supplying. Statistical tools like linear regression are widely used to assess the predictors of quality of life. But when the response is not normal the results are misleading. The aim of this study is to determine the predictors of quality of life in breast cancer patients, using quantile regression model and compare to linear regression. A cross-sectional study conducted on 119 breast cancer patients that admitted and treated in chemotherapy ward of Namazi hospital in Shiraz. We used QLQ-C30 questionnaire to assessment quality of life in these patients. A quantile regression was employed to assess the assocciated factors and the results were compared to linear regression. All analysis carried out using SAS. The mean score for the global health status for breast cancer patients was 64.92+/-11.42. Linear regression showed that only grade of tumor, occupational status, menopausal status, financial difficulties and dyspnea were statistically significant. In spite of linear regression, financial difficulties were not significant in quantile regression analysis and dyspnea was only significant for first quartile. Also emotion functioning and duration of disease statistically predicted the QOL score in the third quartile. The results have demonstrated that using quantile regression leads to better interpretation and richer inference about predictors of the breast cancer patient quality of life.
Multivariate Alternatives to Regression Analysis in the Evaluation of Salary Equity-Parity.
ERIC Educational Resources Information Center
Carter, Richard D.; And Others
1984-01-01
The analysis of salary equity-parity typically involves the use of multiple regression analysis to determine predicted salary and the residual differences between predicted and actual salary. Two alternatives are presented, canonical analysis and multiple discriminant analysis. (Author/MLW)
ERIC Educational Resources Information Center
Dolan, Conor V.; Wicherts, Jelte M.; Molenaar, Peter C. M.
2004-01-01
We consider the question of how variation in the number and reliability of indicators affects the power to reject the hypothesis that the regression coefficients are zero in latent linear regression analysis. We show that power remains constant as long as the coefficient of determination remains unchanged. Any increase in the number of indicators…
Roldan-Valadez, Ernesto; Rios, Camilo; Motola-Kuba, Daniel; Matus-Santos, Juan; Villa, Antonio R; Moreno-Jimenez, Sergio
2016-11-01
A long-lasting concern has prevailed for the identification of predictive biomarkers for high-grade gliomas (HGGs) using MRI. However, a consensus of which imaging parameters assemble a significant survival model is still missing in the literature; we investigated the significant positive or negative contribution of several MR biomarkers in this tumour prognosis. A retrospective cohort of supratentorial HGGs [11 glioblastoma multiforme (GBM) and 17 anaplastic astrocytomas] included 28 patients (9 females and 19 males, respectively, with a mean age of 50.4 years, standard deviation: 16.28 years; range: 13-85 years). Oedema and viable tumour measurements were acquired using regions of interest in T1 weighted, T2 weighted, fluid-attenuated inversion recovery, apparent diffusion coefficient (ADC) and MR spectroscopy (MRS). We calculated Kaplan-Meier curves and obtained Cox's proportional hazards. During the follow-up period (3-98 months), 17 deaths were recorded. The median survival time was 1.73 years (range, 0.287-8.947 years). Only 3 out of 20 covariates (choline-to-N-acetyl aspartate and lipids-lactate-to-creatine ratios and age) showed significance in explaining the variability in the survival hazards model; score test: χ(2) (3) = 9.098, p = 0.028. MRS metabolites overcome volumetric parameters of peritumoral oedema and viable tumour, as well as tumour region ADC measurements. Specific MRS ratios (Cho/Naa, L-L/Cr) might be considered in a regular follow-up for these tumours. Advances in knowledge: Cho/Naa ratio is the strongest survival predictor with a log-hazard function of 2.672 in GBM. Low levels of lipids-lactate/Cr ratio represent up to a 41.6% reduction in the risk of death in GBM.
Rastegari, Azam; Haghdoost, Ali Akbar; Baneshi, Mohammad Reza
2013-01-01
Background Due to the importance of medical studies, researchers of this field should be familiar with various types of statistical analyses to select the most appropriate method based on the characteristics of their data sets. Classification and regression trees (CARTs) can be as complementary to regression models. We compared the performance of a logistic regression model and a CART in predicting drug injection among prisoners. Methods Data of 2720 Iranian prisoners was studied to determine the factors influencing drug injection. The collected data was divided into two groups of training and testing. A logistic regression model and a CART were applied on training data. The performance of the two models was then evaluated on testing data. Findings The regression model and the CART had 8 and 4 significant variables, respectively. Overall, heroin use, history of imprisonment, age at first drug use, and marital status were important factors in determining the history of drug injection. Subjects without the history of heroin use or heroin users with short-term imprisonment were at lower risk of drug injection. Among heroin addicts with long-term imprisonment, individuals with higher age at first drug use and married subjects were at lower risk of drug injection. Although the logistic regression model was more sensitive than the CART, the two models had the same levels of specificity and classification accuracy. Conclusion In this study, both sensitivity and specificity were important. While the logistic regression model had better performance, the graphical presentation of the CART simplifies the interpretation of the results. In general, a combination of different analytical methods is recommended to explore the effects of variables. PMID:24494152
Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha
2012-05-01
Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation.
Detecting overdispersion in count data: A zero-inflated Poisson regression analysis
NASA Astrophysics Data System (ADS)
Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Nor, Maria Elena; Mohamed, Maryati; Ismail, Norradihah
2017-09-01
This study focusing on analysing count data of butterflies communities in Jasin, Melaka. In analysing count dependent variable, the Poisson regression model has been known as a benchmark model for regression analysis. Continuing from the previous literature that used Poisson regression analysis, this study comprising the used of zero-inflated Poisson (ZIP) regression analysis to gain acute precision on analysing the count data of butterfly communities in Jasin, Melaka. On the other hands, Poisson regression should be abandoned in the favour of count data models, which are capable of taking into account the extra zeros explicitly. By far, one of the most popular models include ZIP regression model. The data of butterfly communities which had been called as the number of subjects in this study had been taken in Jasin, Melaka and consisted of 131 number of subjects visits Jasin, Melaka. Since the researchers are considering the number of subjects, this data set consists of five families of butterfly and represent the five variables involve in the analysis which are the types of subjects. Besides, the analysis of ZIP used the SAS procedure of overdispersion in analysing zeros value and the main purpose of continuing the previous study is to compare which models would be better than when exists zero values for the observation of the count data. The analysis used AIC, BIC and Voung test of 5% level significance in order to achieve the objectives. The finding indicates that there is a presence of over-dispersion in analysing zero value. The ZIP regression model is better than Poisson regression model when zero values exist.
A primer for biomedical scientists on how to execute model II linear regression analysis.
Ludbrook, John
2012-04-01
1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.
Introduction to the use of regression models in epidemiology.
Bender, Ralf
2009-01-01
Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.
Hawkey, C J
2005-01-01
The role of selective cyclooxygenase (COX)-2 inhibitors in medical practice has become controversial since evidence emerged that their use is associated with an increased risk of myocardial infarction. Selective COX-2 inhibitors were seen as successor to non-selective non-steroidal anti-inflammatory drugs, in turn successors to aspirin. The importance of pain relief means that such drugs have always attracted attention. The fact that they work through inhibition of cyclooxygenase, are widespread, and have multiple effects also means that adverse effects that were unanticipated (even though predictable) have always emerged. In this paper I therefore present an historical perspective so that the lessons of the past may be applied to the present. PMID:16227351
Wang, Ming; Flanders, W Dana; Bostick, Roberd M; Long, Qi
2012-12-20
Measurement error is common in epidemiological and biomedical studies. When biomarkers are measured in batches or groups, measurement error is potentially correlated within each batch or group. In regression analysis, most existing methods are not applicable in the presence of batch-specific measurement error in predictors. We propose a robust conditional likelihood approach to account for batch-specific error in predictors when batch effect is additive and the predominant source of error, which requires no assumptions on the distribution of measurement error. Although a regression model with batch as a categorical covariable yields the same parameter estimates as the proposed conditional likelihood approach for linear regression, this result does not hold in general for all generalized linear models, in particular, logistic regression. Our simulation studies show that the conditional likelihood approach achieves better finite sample performance than the regression calibration approach or a naive approach without adjustment for measurement error. In the case of logistic regression, our proposed approach is shown to also outperform the regression approach with batch as a categorical covariate. In addition, we also examine a 'hybrid' approach combining the conditional likelihood method and the regression calibration method, which is shown in simulations to achieve good performance in the presence of both batch-specific and measurement-specific errors. We illustrate our method by using data from a colorectal adenoma study.
Deng, Yangyang; Parajuli, Prem B.
2011-08-10
Evaluation of economic feasibility of a bio-gasification facility needs understanding of its unit cost under different production capacities. The objective of this study was to evaluate the unit cost of syngas production at capacities from 60 through 1800Nm 3/h using an economic model with three regression analysis techniques (simple regression, reciprocal regression, and log-log regression). The preliminary result of this study showed that reciprocal regression analysis technique had the best fit curve between per unit cost and production capacity, with sum of error squares (SES) lower than 0.001 and coefficient of determination of (R 2) 0.996. The regression analysis techniques determined the minimum unit cost of syngas production for micro-scale bio-gasification facilities of $0.052/Nm 3, under the capacity of 2,880 Nm 3/h. The results of this study suggest that to reduce cost, facilities should run at a high production capacity. In addition, the contribution of this technique could be the new categorical criterion to evaluate micro-scale bio-gasification facility from the perspective of economic analysis.
Brown, E
1999-01-01
Increasing pharmacy costs are among the fastest growing segments of the health care budget. Health plans are focusing on appropriately managing pharmaceutical costs, both from a long-term global perspective and a short-term approach emphasizing newly marketed products. Over the next six months, cox-2 inhibitors are expected to be approved by the FDA. This new class of drugs, investigated as a safer alternative to non-steroidal anti-inflammatory drugs (NSAIDs), is among the most highly anticipated medications to hit the marketplace. How health plans react to the launch of cox-2 inhibitors may serve as an example for future pharmacy management efforts. A proactive policy regarding the use of cox-2 inhibitors may be challenging, but should include: Reviewing clinical information; evaluating the cost of the new drug; and identifying appropriate patient selection criteria. The available management strategies include precertification, a tiered co-payment system, restricting prescriptions to a provider specialty, retrospective physician profiling, and physician education.
Regression Models for Demand Reduction based on Cluster Analysis of Load Profiles
Yamaguchi, Nobuyuki; Han, Junqiao; Ghatikar, Girish; Piette, Mary Ann; Asano, Hiroshi; Kiliccote, Sila
2009-06-28
This paper provides new regression models for demand reduction of Demand Response programs for the purpose of ex ante evaluation of the programs and screening for recruiting customer enrollment into the programs. The proposed regression models employ load sensitivity to outside air temperature and representative load pattern derived from cluster analysis of customer baseline load as explanatory variables. The proposed models examined their performances from the viewpoint of validity of explanatory variables and fitness of regressions, using actual load profile data of Pacific Gas and Electric Company's commercial and industrial customers who participated in the 2008 Critical Peak Pricing program including Manual and Automated Demand Response.
Muqit, M M K; Marcellino, G R; Henson, D B; Young, L B; Turner, G S; Stanga, P E
2011-11-01
To quantify the 20-ms Pattern Scan Laser (Pascal) panretinal laser photocoagulation (PRP) ablation dosage required for regression of proliferative diabetic retinopathy (PDR), and to explore factors related to long-term regression. We retrospectively studied a cohort of patients who participated in a randomised clinical trial, the Manchester Pascal Study. In all, 36 eyes of 22 patients were investigated over a follow-up period of 18 months. Primary outcome measures included visual acuity (VA) and complete PDR regression. Secondary outcomes included laser burn dosimetry, calculation of retinal PRP ablation areas, and effect of patient-related factors on disease regression. A PDR subgroup analysis was undertaken to assess all factors related to PDR regression according to disease severity. There were no significant changes in logMAR VA for any group over time. In total, 10 eyes (28%) regressed after a single PRP. Following top-up PRP treatment, regression rates varied according to severity: 75% for mild PDR (n=6), 67% for moderate PDR (n=14), and 43% in severe PDR (n=3). To achieve complete disease regression, mild PDR required a mean of 2187 PRP burns and 264 mm(2) ablation area, moderate PDR required 3998 PRP burns and area 456 mm(2), and severe PDR needed 6924 PRP laser burns (836 mm(2); P<0.05). Multiple 20-ms PRP treatments applied over time does not adversely affect visual outcomes, with favourable PDR regression rates and minimal laser burn expansion over 18 months. The average laser dosimetry and retinal ablation areas to achieve complete regression increased significantly with worsening PDR.
Clinical significance of Cox-2, Survivin and Bcl-2 expression in hepatocellular carcinoma (HCC).
Yang, Yu; Zhu, Jiang; Gou, Hongfeng; Cao, Dan; Jiang, Ming; Hou, Mei
2011-09-01
Cox-2, Survivin and Bcl-2 are frequently overexpressed in numerous types of cancers. They are known to be the important regulators of apoptosis. This study was designed to investigate the correlation between the clinical characteristics and the expression of Cox-2, Survivin and Bcl-2 in hepatocellular carcinoma. A total of 63 postoperative hepatocellular carcinoma (HCC) samples, 10 adjacent non-tumor samples and 10 normal liver samples were immunochemically detected for the expression of Cox-2, Survivin and Bcl-2. A median follow-up of 4 years for the 63 HCC patients was conducted. Univariate tests and multivariate Cox regression were performed for statistical analysis. The Kaplan-Meier method was used to analyze the survival. Positive expression of Cox-2 (84.3%) and Survivin (77.8%) was detected significantly more frequently in the HCC samples than in the normal liver tissues (30% and 0, respectively). Bcl-2 was highly expressed in the adjacent non-tumor tissue. Cox-2 was positively correlative to Survivin. Survivin and Bcl-2 were significantly associated with the pathological grade of HCC (P<0.05). Expression of both Cox-2 and Survivin was significantly associated with the poor overall survival (OS) (P=0.0141, P=0.0039). Furthermore, multivariate analysis confirmed the independent prognostic value of Survivin expression, along with tumor size and hepatic function. Cox-2 and Survivin were highly expressed in the HCC tissue. Survivin and Bcl-2 were significantly associated with the pathological grade of HCC. The expression of Survivin was an independent prognostic factor for HCC after a hepatectomy. Treatment that inhibits Survivin may be a promising targeted approach in HCC.
Hu, W; Yu, X G; Wu, S; Tan, L P; Song, M R; Abdulahi, A Y; Wang, Z; Jiang, B; Li, G Q
2016-07-01
Ancylostoma ceylanicum is a common zoonotic nematode. Cats act as natural reservoirs of the hookworm and are involved in transmitting infection to humans, thus posing a potential risk to public health. The prevalence of feline A. ceylanicum in Guangzhou (South China) was surveyed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). In total, 112 faecal samples were examined; 34.8% (39/112) and 43.8% (49/112) samples were positive with hookworms by microscopy and PCR method, respectively. Among them, 40.8% of samples harboured A. ceylanicum. Twelve positive A. ceylanicum samples were selected randomly and used for cox 1 sequence analysis. Sequencing results revealed that they had 97-99% similarity with A. ceylanicum cox 1 gene sequences deposited in GenBank. A phylogenetic tree showed that A. ceylanicum isolates were divided into two groups: one comprising four isolates from Guangzhou (South China), and the other comprising those from Malaysia, Cambodia and Guangzhou. In the latter group, all A. ceylanicum isolates from Guangzhou were clustered into a minor group again. The results indicate that the high prevalence of A. ceylanicum in stray cats in South China poses a potential risk of hookworm transmission from pet cats to humans, and that A. ceylanicum may be a species complex worldwide.
Criteria for the use of regression analysis for remote sensing of sediment and pollutants
NASA Technical Reports Server (NTRS)
Whitlock, C. H.; Kuo, C. Y.; Lecroy, S. R. (Principal Investigator)
1982-01-01
Data analysis procedures for quantification of water quality parameters that are already identified and are known to exist within the water body are considered. The liner multiple-regression technique was examined as a procedure for defining and calibrating data analysis algorithms for such instruments as spectrometers and multispectral scanners.
ERIC Educational Resources Information Center
Ferrer, Alvaro J. Arce; Wang, Lin
This study compared the classification performance among parametric discriminant analysis, nonparametric discriminant analysis, and logistic regression in a two-group classification application. Field data from an organizational survey were analyzed and bootstrapped for additional exploration. The data were observed to depart from multivariate…
Regression Analysis of Physician Distribution to Identify Areas of Need: Some Preliminary Findings.
ERIC Educational Resources Information Center
Morgan, Bruce B.; And Others
A regression analysis was conducted of factors that help to explain the variance in physician distribution and which identify those factors that influence the maldistribution of physicians. Models were developed for different geographic areas to determine the most appropriate unit of analysis for the Western Missouri Area Health Education Center…
Suzuki, Hideaki; Tabata, Takahisa; Koizumi, Hiroki; Hohchi, Nobusuke; Takeuchi, Shoko; Kitamura, Takuro; Fujino, Yoshihisa; Ohbuchi, Toyoaki
2014-12-01
This study aimed to create a multiple regression model for predicting hearing outcomes of idiopathic sudden sensorineural hearing loss (ISSNHL). The participants were 205 consecutive patients (205 ears) with ISSNHL (hearing level ≥ 40 dB, interval between onset and treatment ≤ 30 days). They received systemic steroid administration combined with intratympanic steroid injection. Data were examined by simple and multiple regression analyses. Three hearing indices (percentage hearing improvement, hearing gain, and posttreatment hearing level [HLpost]) and 7 prognostic factors (age, days from onset to treatment, initial hearing level, initial hearing level at low frequencies, initial hearing level at high frequencies, presence of vertigo, and contralateral hearing level) were included in the multiple regression analysis as dependent and explanatory variables, respectively. In the simple regression analysis, the percentage hearing improvement, hearing gain, and HLpost showed significant correlation with 2, 5, and 6 of the 7 prognostic factors, respectively. The multiple correlation coefficients were 0.396, 0.503, and 0.714 for the percentage hearing improvement, hearing gain, and HLpost, respectively. Predicted values of HLpost calculated by the multiple regression equation were reliable with 70% probability with a 40-dB-width prediction interval. Prediction of HLpost by the multiple regression model may be useful to estimate the hearing prognosis of ISSNHL. © The Author(s) 2014.
Modeling of retardance in ferrofluid with Taguchi-based multiple regression analysis
NASA Astrophysics Data System (ADS)
Lin, Jing-Fung; Wu, Jyh-Shyang; Sheu, Jer-Jia
2015-03-01
The citric acid (CA) coated Fe3O4 ferrofluids are prepared by a co-precipitation method and the magneto-optical retardance property is measured by a Stokes polarimeter. Optimization and multiple regression of retardance in ferrofluids are executed by combining Taguchi method and Excel. From the nine tests for four parameters, including pH of suspension, molar ratio of CA to Fe3O4, volume of CA, and coating temperature, influence sequence and excellent program are found. Multiple regression analysis and F-test on the significance of regression equation are performed. It is found that the model F value is much larger than Fcritical and significance level P <0.0001. So it can be concluded that the regression model has statistically significant predictive ability. Substituting excellent program into equation, retardance is obtained as 32.703°, higher than the highest value in tests by 11.4%.
Trend Analysis of Cancer Mortality and Incidence in Panama, Using Joinpoint Regression Analysis.
Politis, Michael; Higuera, Gladys; Chang, Lissette Raquel; Gomez, Beatriz; Bares, Juan; Motta, Jorge
2015-06-01
Cancer is one of the leading causes of death worldwide and its incidence is expected to increase in the future. In Panama, cancer is also one of the leading causes of death. In 1964, a nationwide cancer registry was started and it was restructured and improved in 2012. The aim of this study is to utilize Joinpoint regression analysis to study the trends of the incidence and mortality of cancer in Panama in the last decade. Cancer mortality was estimated from the Panamanian National Institute of Census and Statistics Registry for the period 2001 to 2011. Cancer incidence was estimated from the Panamanian National Cancer Registry for the period 2000 to 2009. The Joinpoint Regression Analysis program, version 4.0.4, was used to calculate trends by age-adjusted incidence and mortality rates for selected cancers. Overall, the trend of age-adjusted cancer mortality in Panama has declined over the last 10 years (-1.12% per year). The cancers for which there was a significant increase in the trend of mortality were female breast cancer and ovarian cancer; while the highest increases in incidence were shown for breast cancer, liver cancer, and prostate cancer. Significant decrease in the trend of mortality was evidenced for the following: prostate cancer, lung and bronchus cancer, and cervical cancer; with respect to incidence, only oral and pharynx cancer in both sexes had a significant decrease. Some cancers showed no significant trends in incidence or mortality. This study reveals contrasting trends in cancer incidence and mortality in Panama in the last decade. Although Panama is considered an upper middle income nation, this study demonstrates that some cancer mortality trends, like the ones seen in cervical and lung cancer, behave similarly to the ones seen in high income countries. In contrast, other types, like breast cancer, follow a pattern seen in countries undergoing a transition to a developed economy with its associated lifestyle, nutrition, and body weight
Trend Analysis of Cancer Mortality and Incidence in Panama, Using Joinpoint Regression Analysis
Politis, Michael; Higuera, Gladys; Chang, Lissette Raquel; Gomez, Beatriz; Bares, Juan; Motta, Jorge
2015-01-01
Abstract Cancer is one of the leading causes of death worldwide and its incidence is expected to increase in the future. In Panama, cancer is also one of the leading causes of death. In 1964, a nationwide cancer registry was started and it was restructured and improved in 2012. The aim of this study is to utilize Joinpoint regression analysis to study the trends of the incidence and mortality of cancer in Panama in the last decade. Cancer mortality was estimated from the Panamanian National Institute of Census and Statistics Registry for the period 2001 to 2011. Cancer incidence was estimated from the Panamanian National Cancer Registry for the period 2000 to 2009. The Joinpoint Regression Analysis program, version 4.0.4, was used to calculate trends by age-adjusted incidence and mortality rates for selected cancers. Overall, the trend of age-adjusted cancer mortality in Panama has declined over the last 10 years (−1.12% per year). The cancers for which there was a significant increase in the trend of mortality were female breast cancer and ovarian cancer; while the highest increases in incidence were shown for breast cancer, liver cancer, and prostate cancer. Significant decrease in the trend of mortality was evidenced for the following: prostate cancer, lung and bronchus cancer, and cervical cancer; with respect to incidence, only oral and pharynx cancer in both sexes had a significant decrease. Some cancers showed no significant trends in incidence or mortality. This study reveals contrasting trends in cancer incidence and mortality in Panama in the last decade. Although Panama is considered an upper middle income nation, this study demonstrates that some cancer mortality trends, like the ones seen in cervical and lung cancer, behave similarly to the ones seen in high income countries. In contrast, other types, like breast cancer, follow a pattern seen in countries undergoing a transition to a developed economy with its associated lifestyle, nutrition, and
An analysis of first-time blood donors return behaviour using regression models.
Kheiri, S; Alibeigi, Z
2015-08-01
Blood products have a vital role in saving many patients' lives. The aim of this study was to analyse blood donor return behaviour. Using a cross-sectional follow-up design of 5-year duration, 864 first-time donors who had donated blood were selected using a systematic sampling. The behaviours of donors via three response variables, return to donation, frequency of return to donation and the time interval between donations, were analysed based on logistic regression, negative binomial regression and Cox's shared frailty model for recurrent events respectively. Successful return to donation rated at 49·1% and the deferral rate was 13·3%. There was a significant reverse relationship between the frequency of return to donation and the time interval between donations. Sex, body weight and job had an effect on return to donation; weight and frequency of donation during the first year had a direct effect on the total frequency of donations. Age, weight and job had a significant effect on the time intervals between donations. Aging decreases the chances of return to donation and increases the time interval between donations. Body weight affects the three response variables, i.e. the higher the weight, the more the chances of return to donation and the shorter the time interval between donations. There is a positive correlation between the frequency of donations in the first year and the total number of return to donations. Also, the shorter the time interval between donations is, the higher the frequency of donations. © 2015 British Blood Transfusion Society.
The use of cognitive ability measures as explanatory variables in regression analysis
Junker, Brian; Schofield, Lynne Steuerle; Taylor, Lowell J
2015-01-01
Cognitive ability measures are often taken as explanatory variables in regression analysis, e.g., as a factor affecting a market outcome such as an individual’s wage, or a decision such as an individual’s education acquisition. Cognitive ability is a latent construct; its true value is unobserved. Nonetheless, researchers often assume that a test score, constructed via standard psychometric practice from individuals’ responses to test items, can be safely used in regression analysis. We examine problems that can arise, and suggest that an alternative approach, a “mixed effects structural equations” (MESE) model, may be more appropriate in many circumstances. PMID:26998417
1982-12-01
An-At25 457 MARKET VALUE ESTIMATION MODELS FOR IMRINE SURFACE "I/ .VESSELS WITH THE USE Of MJLTIPLE REGRESSION ANALYSIS IU) NAVAL POSTGRADUATE SCHOOL...IM8 J B THESIS MARKET VALUE ESTIMATION MODELS FOR MARINE SURFACE VESSELS WITH THE USE OF MULTIPLE REGRESSION ANALYSIS by Thomas D. Johns December...81404 A1110111110014111nws RE O R EPORTTIO mu ra w" O PL T .. v a 4. TITLE (dadmU. S. tYP OP REPORT a PENOO COvearO Market Value Estimation Models for
Analysing count data of Butterflies communities in Jasin, Melaka: A Poisson regression analysis
NASA Astrophysics Data System (ADS)
Afiqah Muhamad Jamil, Siti; Asrul Affendi Abdullah, M.; Kek, Sie Long; Nor, Maria Elena; Mohamed, Maryati; Ismail, Norradihah
2017-09-01
Counting outcomes normally have remaining values highly skewed toward the right as they are often characterized by large values of zeros. The data of butterfly communities, had been taken from Jasin, Melaka and consists of 131 number of subject visits in Jasin, Melaka. In this paper, considering the count data of butterfly communities, an analysis is considered Poisson regression analysis as it is assumed to be an alternative way on better suited to the counting process. This research paper is about analysing count data from zero observation ecological inference of butterfly communities in Jasin, Melaka by using Poisson regression analysis. The software for Poisson regression is readily available and it is becoming more widely used in many field of research and the data was analysed by using SAS software. The purpose of analysis comprised the framework of identifying the concerns. Besides, by using Poisson regression analysis, the study determines the fitness of data for accessing the reliability on using the count data. The finding indicates that the highest and lowest number of subject comes from the third family (Nymphalidae) family and fifth (Hesperidae) family and the Poisson distribution seems to fit the zero values.
A general framework for the use of logistic regression models in meta-analysis.
Simmonds, Mark C; Higgins, Julian Pt
2016-12-01
Where individual participant data are available for every randomised trial in a meta-analysis of dichotomous event outcomes, "one-stage" random-effects logistic regression models have been proposed as a way to analyse these data. Such models can also be used even when individual participant data are not available and we have only summary contingency table data. One benefit of this one-stage regression model over conventional meta-analysis methods is that it maximises the correct binomial likelihood for the data and so does not require the common assumption that effect estimates are normally distributed. A second benefit of using this model is that it may be applied, with only minor modification, in a range of meta-analytic scenarios, including meta-regression, network meta-analyses and meta-analyses of diagnostic test accuracy. This single model can potentially replace the variety of often complex methods used in these areas. This paper considers, with a range of meta-analysis examples, how random-effects logistic regression models may be used in a number of different types of meta-analyses. This one-stage approach is compared with widely used meta-analysis methods including Bayesian network meta-analysis and the bivariate and hierarchical summary receiver operating characteristic (ROC) models for meta-analyses of diagnostic test accuracy. © The Author(s) 2014.
Regression Model Term Selection for the Analysis of Strain-Gage Balance Calibration Data
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert Manfred; Volden, Thomas R.
2010-01-01
The paper discusses the selection of regression model terms for the analysis of wind tunnel strain-gage balance calibration data. Different function class combinations are presented that may be used to analyze calibration data using either a non-iterative or an iterative method. The role of the intercept term in a regression model of calibration data is reviewed. In addition, useful algorithms and metrics originating from linear algebra and statistics are recommended that will help an analyst (i) to identify and avoid both linear and near-linear dependencies between regression model terms and (ii) to make sure that the selected regression model of the calibration data uses only statistically significant terms. Three different tests are suggested that may be used to objectively assess the predictive capability of the final regression model of the calibration data. These tests use both the original data points and regression model independent confirmation points. Finally, data from a simplified manual calibration of the Ames MK40 balance is used to illustrate the application of some of the metrics and tests to a realistic calibration data set.
A multiple additive regression tree analysis of three exposure measures during Hurricane Katrina.
Curtis, Andrew; Li, Bin; Marx, Brian D; Mills, Jacqueline W; Pine, John
2011-01-01
This paper analyses structural and personal exposure to Hurricane Katrina. Structural exposure is measured by flood height and building damage; personal exposure is measured by the locations of 911 calls made during the response. Using these variables, this paper characterises the geography of exposure and also demonstrates the utility of a robust analytical approach in understanding health-related challenges to disadvantaged populations during recovery. Analysis is conducted using a contemporary statistical approach, a multiple additive regression tree (MART), which displays considerable improvement over traditional regression analysis. By using MART, the percentage of improvement in R-squares over standard multiple linear regression ranges from about 62 to more than 100 per cent. The most revealing finding is the modelled verification that African Americans experienced disproportionate exposure in both structural and personal contexts. Given the impact of exposure to health outcomes, this finding has implications for understanding the long-term health challenges facing this population.
Analysis for Regression Model Behavior by Sampling Strategy for Annual Pollutant Load Estimation.
Park, Youn Shik; Engel, Bernie A
2015-11-01
Water quality data are typically collected less frequently than streamflow data due to the cost of collection and analysis, and therefore water quality data may need to be estimated for additional days. Regression models are applicable to interpolate water quality data associated with streamflow data and have come to be extensively used, requiring relatively small amounts of data. There is a need to evaluate how well the regression models represent pollutant loads from intermittent water quality data sets. Both the specific regression model and water quality data frequency are important factors in pollutant load estimation. In this study, nine regression models from the Load Estimator (LOADEST) and one regression model from the Web-based Load Interpolation Tool (LOADIN) were evaluated with subsampled water quality data sets from daily measured water quality data sets for N, P, and sediment. Each water quality parameter had different correlations with streamflow, and the subsampled water quality data sets had various proportions of storm samples. The behaviors of the regression models differed not only by water quality parameter but also by proportion of storm samples. The regression models from LOADEST provided accurate and precise annual sediment and P load estimates using the water quality data of 20 to 40% storm samples. LOADIN provided more accurate and precise annual N load estimates than LOADEST. In addition, the results indicate that avoidance of water quality data extrapolation and availability of water quality data from storm events were crucial in annual pollutant load estimation using pollutant regression models. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.
Box-Cox transformation for QTL mapping.
Yang, Runqing; Yi, Nengjun; Xu, Shizhong
2006-01-01
The maximum likelihood method of QTL mapping assumes that the phenotypic values of a quantitative trait follow a normal distribution. If the assumption is violated, some forms of transformation should be taken to make the assumption approximately true. The Box-Cox transformation is a general transformation method which can be applied to many different types of data. The flexibility of the Box-Cox transformation is due to a variable, called transformation factor, appearing in the Box-Cox formula. We developed a maximum likelihood method that treats the transformation factor as an unknown parameter, which is estimated from the data simultaneously along with the QTL parameters. The method makes an objective choice of data transformation and thus can be applied to QTL analysis for many different types of data. Simulation studies show that (1) Box-Cox transformation can substantially increase the power of QTL detection; (2) Box-Cox transformation can replace some specialized transformation methods that are commonly used in QTL mapping; and (3) applying the Box-Cox transformation to data already normally distributed does not harm the result.
Mitochondrial disease genes COA6, COX6B and SCO2 have overlapping roles in COX2 biogenesis
Ghosh, Alok; Pratt, Anthony T.; Soma, Shivatheja; Theriault, Sarah G.; Griffin, Aaron T.; Trivedi, Prachi P.; Gohil, Vishal M.
2016-01-01
Biogenesis of cytochrome c oxidase (CcO), the terminal enzyme of the mitochondrial respiratory chain, is a complex process facilitated by several assembly factors. Pathogenic mutations were recently reported in one such assembly factor, COA6, and our previous work linked Coa6 function to mitochondrial copper metabolism and expression of Cox2, a copper-containing subunit of CcO. However, the precise role of Coa6 in Cox2 biogenesis remained unknown. Here we show that yeast Coa6 is an orthologue of human COA6, and like Cox2, is regulated by copper availability, further implicating it in copper delivery to Cox2. In order to place Coa6 in the Cox2 copper delivery pathway, we performed a comprehensive genetic epistasis analysis in the yeast Saccharomyces cerevisiae and found that simultaneous deletion of Coa6 and Sco2, a mitochondrial copper metallochaperone, or Coa6 and Cox12/COX6B, a structural subunit of CcO, completely abrogates Cox2 biogenesis. Unlike Coa6 deficient cells, copper supplementation fails to rescue Cox2 levels of these double mutants. Overexpression of Cox12 or Sco proteins partially rescues the coa6Δ phenotype, suggesting their overlapping but non-redundant roles in copper delivery to Cox2. These genetic data are strongly corroborated by biochemical studies demonstrating physical interactions between Coa6, Cox2, Cox12 and Sco proteins. Furthermore, we show that patient mutations in Coa6 disrupt Coa6–Cox2 interaction, providing the biochemical basis for disease pathogenesis. Taken together, these results place COA6 in the copper delivery pathway to CcO and, surprisingly, link it to a previously unidentified function of CcO subunit Cox12 in Cox2 biogenesis. PMID:26669719
Sampson, Maureen L; Gounden, Verena; van Deventer, Hendrik E; Remaley, Alan T
2016-02-01
The main drawback of the periodic analysis of quality control (QC) material is that test performance is not monitored in time periods between QC analyses, potentially leading to the reporting of faulty test results. The objective of this study was to develop a patient based QC procedure for the more timely detection of test errors. Results from a Chem-14 panel measured on the Beckman LX20 analyzer were used to develop the model. Each test result was predicted from the other 13 members of the panel by multiple regression, which resulted in correlation coefficients between the predicted and measured result of >0.7 for 8 of the 14 tests. A logistic regression model, which utilized the measured test result, the predicted test result, the day of the week and time of day, was then developed for predicting test errors. The output of the logistic regression was tallied by a daily CUSUM approach and used to predict test errors, with a fixed specificity of 90%. The mean average run length (ARL) before error detection by CUSUM-Logistic Regression (CSLR) was 20 with a mean sensitivity of 97%, which was considerably shorter than the mean ARL of 53 (sensitivity 87.5%) for a simple prediction model that only used the measured result for error detection. A CUSUM-Logistic Regression analysis of patient laboratory data can be an effective approach for the rapid and sensitive detection of clinical laboratory errors. Published by Elsevier Inc.
Ultrasound-enhanced bioscouring of greige cotton: regression analysis of process factors
USDA-ARS?s Scientific Manuscript database
Process factors of enzyme concentration, time, power and frequency were investigated for ultrasound-enhanced bioscouring of greige cotton. A fractional factorial experimental design and subsequent regression analysis of the process factors were employed to determine the significance of each factor a...
Using Refined Regression Analysis To Assess The Ecological Services Of Restored Wetlands
A hierarchical approach to regression analysis of wetland water treatment was conducted to determine which factors are the most appropriate for characterizing wetlands of differing structure and function. We used this approach in an effort to identify the types and characteristi...
ERIC Educational Resources Information Center
Campbell, S. Duke; Greenberg, Barry
The development of a predictive equation capable of explaining a significant percentage of enrollment variability at Florida International University is described. A model utilizing trend analysis and a multiple regression approach to enrollment forecasting was adapted to investigate enrollment dynamics at the university. Four independent…
Declining Bias and Gender Wage Discrimination? A Meta-Regression Analysis
ERIC Educational Resources Information Center
Jarrell, Stephen B.; Stanley, T. D.
2004-01-01
The meta-regression analysis reveals that there is a strong tendency for discrimination estimates to fall and wage discrimination exist against the woman. The biasing effect of researchers' gender of not correcting for selection bias has weakened and changes in labor market have made it less important.
Multiple Regression Analysis of Sib-Pair Data on Reading to Detect Quantitative Trait Loci.
ERIC Educational Resources Information Center
Fulker, D. W.; And Others
1991-01-01
Applies an extension of an earlier multiple regression model for twin analysis to the problem of detecting linkage in a quantitative trait. Detects a number of possible linkages, indicating that the approach is effective. Discusses detecting genotype-environment interaction and the issue of power. (RS)
Family Background Variables as Instruments for Education in Income Regressions: A Bayesian Analysis
ERIC Educational Resources Information Center
Hoogerheide, Lennart; Block, Joern H.; Thurik, Roy
2012-01-01
The validity of family background variables instrumenting education in income regressions has been much criticized. In this paper, we use data from the 2004 German Socio-Economic Panel and Bayesian analysis to analyze to what degree violations of the strict validity assumption affect the estimation results. We show that, in case of moderate direct…
Catching up with Harvard: Results from Regression Analysis of World Universities League Tables
ERIC Educational Resources Information Center
Li, Mei; Shankar, Sriram; Tang, Kam Ki
2011-01-01
This paper uses regression analysis to test if the universities performing less well according to Shanghai Jiao Tong University's world universities league tables are able to catch up with the top performers, and to identify national and institutional factors that could affect this catching up process. We have constructed a dataset of 461…
ERIC Educational Resources Information Center
Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.
2006-01-01
Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…
Isolating the Effects of Training Using Simple Regression Analysis: An Example of the Procedure.
ERIC Educational Resources Information Center
Waugh, C. Keith
This paper provides a case example of simple regression analysis, a forecasting procedure used to isolate the effects of training from an identified extraneous variable. This case example focuses on results of a three-day sales training program to improve bank loan officers' knowledge, skill-level, and attitude regarding solicitation and sale of…
Catching up with Harvard: Results from Regression Analysis of World Universities League Tables
ERIC Educational Resources Information Center
Li, Mei; Shankar, Sriram; Tang, Kam Ki
2011-01-01
This paper uses regression analysis to test if the universities performing less well according to Shanghai Jiao Tong University's world universities league tables are able to catch up with the top performers, and to identify national and institutional factors that could affect this catching up process. We have constructed a dataset of 461…
ERIC Educational Resources Information Center
Bates, Reid A.; Holton, Elwood F., III; Burnett, Michael F.
1999-01-01
A case study of learning transfer demonstrates the possible effect of influential observation on linear regression analysis. A diagnostic method that tests for violation of assumptions, multicollinearity, and individual and multiple influential observations helps determine which observation to delete to eliminate bias. (SK)
What Satisfies Students?: Mining Student-Opinion Data with Regression and Decision Tree Analysis
ERIC Educational Resources Information Center
Thomas, Emily H.; Galambos, Nora
2004-01-01
To investigate how students' characteristics and experiences affect satisfaction, this study uses regression and decision tree analysis with the CHAID algorithm to analyze student-opinion data. A data mining approach identifies the specific aspects of students' university experience that most influence three measures of general satisfaction. The…
ERIC Educational Resources Information Center
Schulz, Wolfram
Differences in student knowledge about democracy, institutions, and citizenship and students skills in interpreting political communication were studied through multilevel regression analysis of results from the second International Education Association (IEA) Study. This study provides data on 14-year-old students from 28 countries in Europe,…
Multiple Logistic Regression Analysis of Cigarette Use among High School Students
ERIC Educational Resources Information Center
Adwere-Boamah, Joseph
2011-01-01
A binary logistic regression analysis was performed to predict high school students' cigarette smoking behavior from selected predictors from 2009 CDC Youth Risk Behavior Surveillance Survey. The specific target student behavior of interest was frequent cigarette use. Five predictor variables included in the model were: a) race, b) frequency of…
ERIC Educational Resources Information Center
Preacher, Kristopher J.; Curran, Patrick J.; Bauer, Daniel J.
2006-01-01
Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the…
Predictive Discriminant Analysis Versus Logistic Regression in Two-Group Classification Problems.
ERIC Educational Resources Information Center
Meshbane, Alice; Morris, John D.
A method for comparing the cross-validated classification accuracies of predictive discriminant analysis and logistic regression classification models is presented under varying data conditions for the two-group classification problem. With this method, separate-group, as well as total-sample proportions of the correct classifications, can be…
Factor Regression Analysis: A New Method for Weighting Predictors. Final Report.
ERIC Educational Resources Information Center
Curtis, Ervin W.
The optimum weighting of variables to predict a dependent-criterion variable is an important problem in nearly all of the social and natural sciences. Although the predominant method, multiple regression analysis (MR), yields optimum weights for the sample at hand, these weights are not generally optimum in the population from which the sample was…
Using Refined Regression Analysis To Assess The Ecological Services Of Restored Wetlands
A hierarchical approach to regression analysis of wetland water treatment was conducted to determine which factors are the most appropriate for characterizing wetlands of differing structure and function. We used this approach in an effort to identify the types and characteristi...
Quantile regression in the presence of monotone missingness with sensitivity analysis.
Liu, Minzhao; Daniels, Michael J; Perri, Michael G
2016-01-01
In this paper, we develop methods for longitudinal quantile regression when there is monotone missingness. In particular, we propose pattern mixture models with a constraint that provides a straightforward interpretation of the marginal quantile regression parameters. Our approach allows sensitivity analysis which is an essential component in inference for incomplete data. To facilitate computation of the likelihood, we propose a novel way to obtain analytic forms for the required integrals. We conduct simulations to examine the robustness of our approach to modeling assumptions and compare its performance to competing approaches. The model is applied to data from a recent clinical trial on weight management.
Regression analysis of non-contact acousto-thermal signature data
NASA Astrophysics Data System (ADS)
Criner, Amanda; Schehl, Norman
2016-05-01
The non-contact acousto-thermal signature (NCATS) is a nondestructive evaluation technique with potential to detect fatigue in materials such as noisy titanium and polymer matrix composites. The underlying physical mechanisms and properties may be determined by parameter estimation via nonlinear regression. The nonlinear regression analysis formulation, including the underlying models, is discussed. Several models and associated data analyses are given along with the assumptions implicit in the underlying model. The results are anomalous. These anomalous results are evaluated with respect to the accuracy of the implicit assumptions.
Wang, D Z; Wang, C; Shen, C F; Zhang, Y; Zhang, H; Song, G D; Xue, X D; Xu, Z L; Zhang, S; Jiang, G H
2017-05-10
We described the time trend of acute myocardial infarction (AMI) from 1999 to 2013 in Tianjin incidence rate with Cochran-Armitage trend (CAT) test and linear regression analysis, and the results were compared. Based on actual population, CAT test had much stronger statistical power than linear regression analysis for both overall incidence trend and age specific incidence trend (Cochran-Armitage trend P value
Greensmith, David J
2014-01-01
Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow.
Forecasting Model for IPTV Service in Korea Using Bootstrap Ridge Regression Analysis
NASA Astrophysics Data System (ADS)
Lee, Byoung Chul; Kee, Seho; Kim, Jae Bum; Kim, Yun Bae
The telecom firms in Korea are taking new step to prepare for the next generation of convergence services, IPTV. In this paper we described our analysis on the effective method for demand forecasting about IPTV broadcasting. We have tried according to 3 types of scenarios based on some aspects of IPTV potential market and made a comparison among the results. The forecasting method used in this paper is the multi generation substitution model with bootstrap ridge regression analysis.
Reflectance confocal microscopy analysis of equivocal melanocytic lesions with severe regression.
Agozzino, M; Ferrari, A; Cota, C; Franceschini, C; Buccini, P; Eibenshutz, L; Ardigò, M
2017-05-21
The differential diagnosis between regressing nevi and melanoma might be challenging; regressing areas can represent a confounding factor for the diagnosis and the histology still remain mandatory to rule out melanoma. Reflectance confocal microscopy may add valuable information by revealing features suggestive of the nature of the melanocytic proliferation. To assess the impact of confocal microscopy in the management of regressive melanocytic lesions. The dermoscopic analysis of 92 melanocytic lesions showing that more than 30% of regressions have been retrospectively considered, among them, 32 melanocytic lesions with a 7 check point list ≥3 they were assessed at the rcm and subsequently excised. For each selected lesion, dermoscopic features of regression (white scar-like areas, blue areas, blue white areas), distribution of regressing areas (central, peripheral, or both) and the percentage of regression have been examined by an expert in dermoscopy, blinded to the histological and confocal diagnosis. Subsequently, two experts in confocal microscopy revaluated, blinded from histology, RCM images. Of the 32 lesions analyzed, 23 (71.5%) were diagnosed histologically as nevi, and 9 (28.5%) as melanomas. 26 of 32 lesions (81.5%) exhibited regression >50% of the overall. On RCM, 11 lesions have been interpreted as malignant and 21 as benign. On RCM the majority of nevi exhibited regular architecture without cytological atypia. Epidermal disarray, pagetoid infiltration, disarranged dermo-epidermal junction architecture and atypical nests were considered as suspicious for malignancy. Good concordance between confocal readers has been detected. A combined dermoscopic/confocal approach can be used for the management of lesions exhibiting dermoscopic features of regression in order to provide a more conclusive pre-histological diagnosis avoiding a high number of unnecessary excisions. Limits of this study were represented by the relatively small number of lesions and
Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen
2014-01-01
It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models. PMID:24574916
Wang, Wen-Cheng; Cho, Wen-Chien; Chen, Yin-Jen
2014-01-01
It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models.
Cirulli, N; Ballini, A; Cantore, S; Farronato, D; Inchingolo, F; Dipalma, G; Gatto, M R; Alessandri Bonetti, G
2015-01-01
Mixed dentition analysis forms a critical aspect of early orthodontic treatment. In fact an accurate space analysis is one of the important criteria in determining whether the treatment plan may involve serial extraction, guidance of eruption, space maintenance, space regaining or just periodic observation of the patients. The aim of the present study was to calculate linear regression equations in mixed dentition space analysis, measuring 230 dental casts mesiodistal tooth widths, obtained from southern Italian patients (118 females, 112 males, mean age 15±3 years). Students t-test or Wilcoxon test for independent and paired samples were used to determine right/left side and male/female differences. On the basis of the sum of the mesiodistal diameters of the 4 mandibular incisors as predictors for the sum of the widths of the canines and premolars in the mandibular mixed dentition, a new linear regression equation was found: y = 0.613x+7.294 (r= 0.701) for both genders in a southern Italian population. To better estimate the size of leeway space, a new regression equation was found to calculate the mesiodistal size of the second premolar using the sum of the four mandibular incisors, canine and first premolar as a predictor. The equation is y = 0.241x+1.224 (r= 0.732). In conclusion, new regression equations were derived for a southern Italian population.
NASA Astrophysics Data System (ADS)
Mitra, Ashis; Majumdar, Prabal Kumar; Bannerjee, Debamalya
2013-03-01
This paper presents a comparative analysis of two modeling methodologies for the prediction of air permeability of plain woven handloom cotton fabrics. Four basic fabric constructional parameters namely ends per inch, picks per inch, warp count and weft count have been used as inputs for artificial neural network (ANN) and regression models. Out of the four regression models tried, interaction model showed very good prediction performance with a meager mean absolute error of 2.017 %. However, ANN models demonstrated superiority over the regression models both in terms of correlation coefficient and mean absolute error. The ANN model with 10 nodes in the single hidden layer showed very good correlation coefficient of 0.982 and 0.929 and mean absolute error of only 0.923 and 2.043 % for training and testing data respectively.
The stochastic regression analysis as a tool in ecotoxicological QSAR studies.
Devillers, J; Zakarya, D; Chastrette, M; Doré, J C
1989-12-01
Correspondence factor analysis (CFA) was used in conjunction with linear regression analysis to examine the structure-activity relationships of 50 benzene derivatives tested on Pimephales promelas. From nine molecular descriptions (numbers of C, H, O, N, Br, Cl, NO2, OH, and NH2 included in the molecules), CFA made it possible to define five new independent variables which were introduced in a stepwise regression analysis procedure to describe the acute toxicity (96-h LC50) of the aromatic compounds. The model log 1/C = -0.727F1 + 1.248F3 + 4.052 (r = 0.918; s = 0.270) is more relevant to describe the ecotoxicological behavior of the studied compounds on the fathead minnow than that obtained with principal components (log 1/C = 0.151 PC1 -0.271 PC2 + 4.124; r = 0.737; s = 0.460). The heuristic potency of this particular statistical analysis, which is called stochastic regression analysis, is discussed in detail.
Leite, M L; Nicolosi, A
1998-09-01
Ophthalmological studies often deal with correlated binary outcome variables. We propose a weighted logistic regression method to account for the intraclass correlations between eyes. Using simulation studies, we compared this method with two standard logistic regression approaches: a) based on eyes as the unit of analysis and b) treating individuals classified as cases if at least one eye is affected. The considered approaches were evaluated in terms of type I error, power and estimation properties. The simulation results reveal that the subject-based approach can lead to substantial bias in regression coefficient estimates when the correlation between eyes is heterogeneous across groups or when it is low, and that power is directly affected by this bias. Furthermore, the standard eye-based approach, which ignores intrasubject correlations, leads to inflated type I error rates. The proposed weighted approach performed well in all of the situations considered. This is a simple method which can be implemented using any current statistical or epidemiological package that includes logistic regression analysis.
Sun, Xiaoyan; Peng, Limin; Manatunga, Amita; Marcus, Michele
2016-03-01
In many observational longitudinal studies, the outcome of interest presents a skewed distribution, is subject to censoring due to detection limit or other reasons, and is observed at irregular times that may follow a outcome-dependent pattern. In this work, we consider quantile regression modeling of such longitudinal data, because quantile regression is generally robust in handling skewed and censored outcomes and is flexible to accommodate dynamic covariate-outcome relationships. Specifically, we study a longitudinal quantile regression model that specifies covariate effects on the marginal quantiles of the longitudinal outcome. Such a model is easy to interpret and can accommodate dynamic outcome profile changes over time. We propose estimation and inference procedures that can appropriately account for censoring and irregular outcome-dependent follow-up. Our proposals can be readily implemented based on existing software for quantile regression. We establish the asymptotic properties of the proposed estimator, including uniform consistency and weak convergence. Extensive simulations suggest good finite-sample performance of the new method. We also present an analysis of data from a long-term study of a population exposed to polybrominated biphenyls (PBB), which uncovers an inhomogeneous PBB elimination pattern that would not be detected by traditional longitudinal data analysis. © 2015, The International Biometric Society.
The estimation of Aerosol Optical Depth in eastern China based on regression analysis
NASA Astrophysics Data System (ADS)
Wang, Jing; Shi, Runhe; Liu, Chaoshun; Zhou, Cong
2015-09-01
The atmospheric pollution and air quality issues are getting worse in China, the formation mechanism of aerosols and their environment effects attracted more and more attention. Aerosol Optical Depth (AOD) is one of the most important parameters which can indicate the atmospheric turbidity and aerosol load. High-quality AOD data are significant for the study in the atmospheric environment (i.e., air quality). This paper used MODIS/Terra AOD in 2008 to improve the coverage of MODIS/Aqua AOD, which was based on linear regression analysis model. RMSE between estimation value and AquaAOD detected through satellite is 0.132. The average value of test data was 0.812. The average of regression result was 0.807. It showed that the regression model between AODTerra and AODAqua worked well. Also, we built two sets of estimation models (MODIS AOD and OMI AOD) through stepwise regression analysis model. One is using OMI AOD and meteorological elements to estimate MODIS AOD. The value of RMSE was 0.113, which represents 13.916% of the average(R2=0.782). The other one is using MODIS AOD and meteorological elements to estimate OMI AOD. RMSE of the model is 0.132, which represents 18.182% of the average (R2=0.726).
Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.
Hu, Yi-Chung
2014-01-01
On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.
Gulbransen, Dana J; McGlathery, Karen J; Marklund, Maria; Norris, James N; Gurgel, Carlos Frederico D
2012-10-01
Gracilaria vermiculophylla (Ohmi) Papenfuss is an invasive alga that is native to Southeast Asia and has invaded many estuaries in North America and Europe. It is difficult to differentiate G. vermiculophylla from native forms using morphology and therefore molecular techniques are needed. In this study, we used three molecular markers (rbcL, cox2-cox3 spacer, cox1) to identify G. vermiculophylla at several locations in the western Atlantic. RbcL and cox2-cox3 spacer markers confirmed the presence of G. vermiculophylla on the east coast of the USA from Massachusetts to South Carolina. We used a 507 base pair region of cox1 mtDNA to (i) verify the widespread distribution of G. vermiculophylla in the Virginia (VA) coastal bays and (ii) determine the intraspecific diversity of these algae. Cox1 haplotype richness in the VA coastal bays was much higher than that previously found in other invaded locations, as well as some native locations. This difference is likely attributed to the more intensive sampling design used in this study, which was able to detect richness created by multiple, diverse introductions. On the basis of our results, we recommend that future studies take differences in sampling design into account when comparing haplotype richness and diversity between native and non-native studies in the literature.
Tutorial on Biostatistics: Linear Regression Analysis of Continuous Correlated Eye Data.
Ying, Gui-Shuang; Maguire, Maureen G; Glynn, Robert; Rosner, Bernard
2017-04-01
To describe and demonstrate appropriate linear regression methods for analyzing correlated continuous eye data. We describe several approaches to regression analysis involving both eyes, including mixed effects and marginal models under various covariance structures to account for inter-eye correlation. We demonstrate, with SAS statistical software, applications in a study comparing baseline refractive error between one eye with choroidal neovascularization (CNV) and the unaffected fellow eye, and in a study determining factors associated with visual field in the elderly. When refractive error from both eyes were analyzed with standard linear regression without accounting for inter-eye correlation (adjusting for demographic and ocular covariates), the difference between eyes with CNV and fellow eyes was 0.15 diopters (D; 95% confidence interval, CI -0.03 to 0.32D, p = 0.10). Using a mixed effects model or a marginal model, the estimated difference was the same but with narrower 95% CI (0.01 to 0.28D, p = 0.03). Standard regression for visual field data from both eyes provided biased estimates of standard error (generally underestimated) and smaller p-values, while analysis of the worse eye provided larger p-values than mixed effects models and marginal models. In research involving both eyes, ignoring inter-eye correlation can lead to invalid inferences. Analysis using only right or left eyes is valid, but decreases power. Worse-eye analysis can provide less power and biased estimates of effect. Mixed effects or marginal models using the eye as the unit of analysis should be used to appropriately account for inter-eye correlation and maximize power and precision.
Tejera-Vaquerizo, A; Martín-Cuevas, P; Gallego, E; Herrera-Acosta, E; Traves, V; Herrera-Ceballos, E; Nagore, E
2015-04-01
The main aim of this study was to identify predictors of sentinel lymph node (SN) metastasis in cutaneous melanoma. This was a retrospective cohort study of 818 patients in 2 tertiary-level hospitals. The primary outcome variable was SN involvement. Independent predictors were identified using multiple logistic regression and a classification and regression tree (CART) analysis. Ulceration, tumor thickness, and a high mitotic rate (≥6 mitoses/mm(2)) were independently associated with SN metastasis in the multiple regression analysis. The most important predictor in the CART analysis was Breslow thickness. Absence of an inflammatory infiltrate, patient age, and tumor location were predictive of SN metastasis in patients with tumors thicker than 2mm. In the case of thinner melanomas, the predictors were mitotic rate (>6 mitoses/mm(2)), presence of ulceration, and tumor thickness. Patient age, mitotic rate, and tumor thickness and location were predictive of survival. A high mitotic rate predicts a higher risk of SN involvement and worse survival. CART analysis improves the prediction of regional metastasis, resulting in better clinical management of melanoma patients. It may also help select suitable candidates for inclusion in clinical trials. Copyright © 2014 Elsevier España, S.L.U. and AEDV. All rights reserved.
Comparison of cranial sex determination by discriminant analysis and logistic regression.
Amores-Ampuero, Anabel; Alemán, Inmaculada
2016-04-05
Various methods have been proposed for estimating dimorphism. The objective of this study was to compare sex determination results from cranial measurements using discriminant analysis or logistic regression. The study sample comprised 130 individuals (70 males) of known sex, age, and cause of death from San José cemetery in Granada (Spain). Measurements of 19 neurocranial dimensions and 11 splanchnocranial dimensions were subjected to discriminant analysis and logistic regression, and the percentages of correct classification were compared between the sex functions obtained with each method. The discriminant capacity of the selected variables was evaluated with a cross-validation procedure. The percentage accuracy with discriminant analysis was 78.2% for the neurocranium (82.4% in females and 74.6% in males) and 73.7% for the splanchnocranium (79.6% in females and 68.8% in males). These percentages were higher with logistic regression analysis: 85.7% for the neurocranium (in both sexes) and 94.1% for the splanchnocranium (100% in females and 91.7% in males).
NASA Technical Reports Server (NTRS)
Rummler, D. R.
1976-01-01
The results are presented of investigations to apply regression techniques to the development of methodology for creep-rupture data analysis. Regression analysis techniques are applied to the explicit description of the creep behavior of materials for space shuttle thermal protection systems. A regression analysis technique is compared with five parametric methods for analyzing three simulated and twenty real data sets, and a computer program for the evaluation of creep-rupture data is presented.
Air Leakage of US Homes: Regression Analysis and Improvements from Retrofit
Chan, Wanyu R.; Joh, Jeffrey; Sherman, Max H.
2012-08-01
LBNL Residential Diagnostics Database (ResDB) contains blower door measurements and other diagnostic test results of homes in United States. Of these, approximately 134,000 single-family detached homes have sufficient information for the analysis of air leakage in relation to a number of housing characteristics. We performed regression analysis to consider the correlation between normalized leakage and a number of explanatory variables: IECC climate zone, floor area, height, year built, foundation type, duct location, and other characteristics. The regression model explains 68% of the observed variability in normalized leakage. ResDB also contains the before and after retrofit air leakage measurements of approximately 23,000 homes that participated in weatherization assistant programs (WAPs) or residential energy efficiency programs. The two types of programs achieve rather similar reductions in normalized leakage: 30% for WAPs and 20% for other energy programs.
Forecasting municipal solid waste generation using prognostic tools and regression analysis.
Ghinea, Cristina; Drăgoi, Elena Niculina; Comăniţă, Elena-Diana; Gavrilescu, Marius; Câmpean, Teofil; Curteanu, Silvia; Gavrilescu, Maria
2016-11-01
For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction.
Gao, Jun; Johnston, Grace M; Lavergne, M Ruth; McIntyre, Paul
2011-01-01
Classification and regression tree (CART) analysis was used to identify subpopulations with lower palliative care program (PCP) enrolment rates. CART analysis uses recursive partitioning to group predictors. The PCP enrolment rate was 72 percent for the 6,892 adults who died of cancer from 2000 and 2005 in two counties in Nova Scotia, Canada. The lowest PCP enrolment rates were for nursing home residents over 82 years (27 percent), a group residing more than 43 kilometres from the PCP (31 percent), and another group living less than two weeks after their cancer diagnosis (37 percent). The highest rate (86 percent) was for the 2,118 persons who received palliative radiation. Findings from multiple logistic regression (MLR) were provided for comparison. CART findings identified low PCP enrolment subpopulations that were defined by interactions among demographic, social, medical, and health system predictors.
Improvement of minimum paint film thickness for THz paint meters by multiple-regression analysis
NASA Astrophysics Data System (ADS)
Yasuda, Takashi; Iwata, Tetsuo; Araki, Tsutomu; Yasui, Takeshi
2007-10-01
We propose a numerical parameter fitting method to determine the time delay between two temporally overlapped echo pulses in terahertz (THz) tomography measurements. The method is based on multiple-regression analysis with the least-squares method and is applied to decrease the minimum paint film thickness for THz paint meters. Applying multiple-regression analysis to paint thickness measurements is five times more sensitive with regard to the minimum thickness than numerical Fourier deconvolution. We apply the proposed method to determine the optical thickness, geometrical thickness, and group refractive index of dry paint film and wet paint film. The proposed method is useful for decreasing the minimum thickness for a THz paint meter and other THz tomography measurements.
Dimitriu, G; Poiata, Antonia; Tuchiluş, Cristina; Buiuc, D
2006-01-01
Linezolid is a new synthetic antibiotic belonging to the oxazolidinone class, available for the therapy of gram-positive infections, caused by methicillin-resistant staphylococci, vancomycin-resistant enterococci and penicillin-resistant pneumococci. The aim of the study was to determine the in vitro activity of linezolid against staphylococci strains and also to determine the relationship between the minimum inhibitory concentration (MIC) and inhibition zone diameter by calculating the regression analysis. We tested one hundred S. aureus isolates, obtained from healthy persons (naso-pharyngeal swabs) during 2005 year. The antibiotic susceptibility of strains was determined by disk diffusion standardized method and by agar dilution method using a multipoint inoculator. The relationship between the diameter of the inhibition zone produced by a linezolid disc impregnated with a fixed amount (30 eg) was determined by regression performed with the least squares method, considering the log2 of the minimum inhibitory concentrations (MICs) as the independent variable and the zone diameter as the dependent variable. The MIC values expressed in logarithmic form are plotted against inhibition zone diameter (arithmetic scale) of the same strain. The activity of linezolid against staphylococci was very good, with MIC 90 of 1 mg/l. All strains were fully sensitive. The regression line for linezolid passes through a continuous series of points that all are approximately located on the a straight line. For each of the MIC values the differences result no greater than 23 mm in diameter sizes were registered. Regression equation was y= -0.188x + 8.048. In conclusion, the regression line analysis calculated for linezolid, demonstrates a significant correlation between MIC values and the inhibition zone diameters obtained by a 30 mg disc.
ERIC Educational Resources Information Center
Serdahl, Eric
The information that is gained through various analyses of the residual scores yielded by the least squares regression model is explored. In fact, the most widely used methods for detecting data that do not fit this model are based on an analysis of residual scores. First, graphical methods of residual analysis are discussed, followed by a review…
Monitoring heavy metal Cr in soil based on hyperspectral data using regression analysis
NASA Astrophysics Data System (ADS)
Zhang, Ningyu; Xu, Fuyun; Zhuang, Shidong; He, Changwei
2016-10-01
Heavy metal pollution in soils is one of the most critical problems in the global ecology and environment safety nowadays. Hyperspectral remote sensing and its application is capable of high speed, low cost, less risk and less damage, and provides a good method for detecting heavy metals in soil. This paper proposed a new idea of applying regression analysis of stepwise multiple regression between the spectral data and monitoring the amount of heavy metal Cr by sample points in soil for environmental protection. In the measurement, a FieldSpec HandHeld spectroradiometer is used to collect reflectance spectra of sample points over the wavelength range of 325-1075 nm. Then the spectral data measured by the spectroradiometer is preprocessed to reduced the influence of the external factors, and the preprocessed methods include first-order differential equation, second-order differential equation and continuum removal method. The algorithms of stepwise multiple regression are established accordingly, and the accuracy of each equation is tested. The results showed that the accuracy of first-order differential equation works best, which makes it feasible to predict the content of heavy metal Cr by using stepwise multiple regression.
Ruman, M; Olkowska, E; Kozioł, K; Absalon, D; Matysik, M; Polkowska, Ż
2014-03-01
Monitoring contamination in river water is an expensive procedure, particularly for developing countries where pollution is a significant problem. This study was conducted to provide a pollution monitoring strategy that reduces the cost of laboratory analysis. The new monitoring strategy was designed as a result of cluster and regression analysis on field data collected from an industrially influenced river. Pollution sources in the study site were coal mining, metallurgy, chemical industry, and metropolitan sewage. This river resembles those in other areas of the world, including developing countries where environmental monitoring is financially constrained. Data were collected on variability of contaminant concentrations during four seasons at the same points on tributaries of the river. The variables described in the study are pH, electrical conductivity, inorganic ions, trace elements, and selected organic pollutants. These variables were divided into groups using cluster analysis. These groups were then tested using regression models to identify how the behavior of one variable changes in relation to another. It was found that up to 86.8% of variability of one parameter could be determined by another in the dataset. We adopted 60, 65, and 70% determination levels () for accepting a regression model. As a result, monitoring could be reduced by 15 (60% level) and 10 variables (65 and 70%) out of 43, which comprises 35 and 23% of the monitored variable total. Cost reduction would be most effective if trace elements or organic pollutants were excluded from monitoring because these are the constituents most expensive to analyze.
Exact Analysis of Squared Cross-Validity Coefficient in Predictive Regression Models.
Shieh, Gwowen
2009-01-01
In regression analysis, the notion of population validity is of theoretical interest for describing the usefulness of the underlying regression model, whereas the presumably more important concept of population cross-validity represents the predictive effectiveness for the regression equation in future research. It appears that the inference procedures of the squared multiple correlation coefficient have been extensively developed. In contrast, a full range of statistical methods for the analysis of the squared cross-validity coefficient is considerably far from complete. This article considers a distinct expression for the definition of the squared cross-validity coefficient as the direct connection and monotone transformation to the squared multiple correlation coefficient. Therefore, all the currently available exact methods for interval estimation, power calculation, and sample size determination of the squared multiple correlation coefficient are naturally modified and extended to the analysis of the squared cross-validity coefficient. The adequacies of the existing approximate procedures and the suggested exact method are evaluated through a Monte Carlo study. Furthermore, practical applications in areas of psychology and management are presented to illustrate the essential features of the proposed methodologies. The first empirical example uses 6 control variables related to driver characteristics and traffic congestion and their relation to stress in bus drivers, and the second example relates skills, cognitive performance, and personality to team performance measures. The results in this article can facilitate the recommended practice of cross-validation in psychological and other areas of social science research.
Non-Stationary Hydrologic Frequency Analysis using B-Splines Quantile Regression
NASA Astrophysics Data System (ADS)
Nasri, B.; St-Hilaire, A.; Bouezmarni, T.; Ouarda, T.
2015-12-01
Hydrologic frequency analysis is commonly used by engineers and hydrologists to provide the basic information on planning, design and management of hydraulic structures and water resources system under the assumption of stationarity. However, with increasing evidence of changing climate, it is possible that the assumption of stationarity would no longer be valid and the results of conventional analysis would become questionable. In this study, we consider a framework for frequency analysis of extreme flows based on B-Splines quantile regression, which allows to model non-stationary data that have a dependence on covariates. Such covariates may have linear or nonlinear dependence. A Markov Chain Monte Carlo (MCMC) algorithm is used to estimate quantiles and their posterior distributions. A coefficient of determination for quantiles regression is proposed to evaluate the estimation of the proposed model for each quantile level. The method is applied on annual maximum and minimum streamflow records in Ontario, Canada. Climate indices are considered to describe the non-stationarity in these variables and to estimate the quantiles in this case. The results show large differences between the non-stationary quantiles and their stationary equivalents for annual maximum and minimum discharge with high annual non-exceedance probabilities. Keywords: Quantile regression, B-Splines functions, MCMC, Streamflow, Climate indices, non-stationarity.
ERIC Educational Resources Information Center
Johns, Stephanie
2010-01-01
Kathy Cox, the superintendent of schools for Georgia, believes "excellence is not an accident". She made a name for herself by winning $1 million proving she was smarter than a fifth-grader on a popular television show. This article presents a profile of Cox, her family, her role as school superintendent, and her accomplishments.…
ERIC Educational Resources Information Center
Johns, Stephanie
2010-01-01
Kathy Cox, the superintendent of schools for Georgia, believes "excellence is not an accident". She made a name for herself by winning $1 million proving she was smarter than a fifth-grader on a popular television show. This article presents a profile of Cox, her family, her role as school superintendent, and her accomplishments.…
Prediction of severe acute pancreatitis using classification and regression tree analysis.
Hong, Wandong; Dong, Lemei; Huang, Qingke; Wu, Wenzhi; Wu, Jiansheng; Wang, Yumin
2011-12-01
The available prognostic scoring systems for acute pancreatitis have limitations that restrict their clinical value. To develop a decision model based on classification and regression tree (CART) analysis for the prediction of severe acute pancreatitis (SAP). A total of 420 patients with acute pancreatitis were enrolled. Study participants were randomly assigned to the training sample and test sample in a 2:1 ratio. First, univariate analysis and logistic regression analysis were used to identify predictors associated with SAP in the training sample. Then, CART analysis was carried out to develop a simple tree model for the prediction of SAP. A receiver operating characteristic (ROC) curve was constructed in order to assess the performance of the model. The prediction model was then applied to the test sample. Four variables (systemic inflammatory response syndrome [SIRS], pleural effusion, serum calcium, and blood urea nitrogen [BUN]) were identified as important predictors of SAP by logistic regression analysis. A tree model (which consisted of pleural effusion, serum calcium, and BUN) that was developed by CART analysis was able to early identify among cohorts at high (79.03%) and low (7.80%) risk of developing SAP. The area under the ROC curve of the tree model was higher than that of the APACHE II score (0.84 vs. 0.68; P < 0.001). The predicted accuracy of the tree model was validated in the test sample with an area under the ROC curve of 0.86. A decision tree model that consists of pleural effusion, serum calcium, and BUN may be useful for the prediction of SAP.
Hayes, Andrew F; Rockwood, Nicholas J
2017-11-01
There have been numerous treatments in the clinical research literature about various design, analysis, and interpretation considerations when testing hypotheses about mechanisms and contingencies of effects, popularly known as mediation and moderation analysis. In this paper we address the practice of mediation and moderation analysis using linear regression in the pages of Behaviour Research and Therapy and offer some observations and recommendations, debunk some popular myths, describe some new advances, and provide an example of mediation, moderation, and their integration as conditional process analysis using the PROCESS macro for SPSS and SAS. Our goal is to nudge clinical researchers away from historically significant but increasingly old school approaches toward modifications, revisions, and extensions that characterize more modern thinking about the analysis of the mechanisms and contingencies of effects. Copyright © 2016 Elsevier Ltd. All rights reserved.
Inhibition of cyclooxygenase (COX)-2 affects endothelial progenitor cell proliferation
Colleselli, Daniela; Bijuklic, Klaudija; Mosheimer, Birgit A.; Kaehler, Christian M. . E-mail: C.M.Kaehler@uibk.ac.at
2006-09-10
Growing evidence indicates that inducible cyclooxygenase-2 (COX-2) is involved in the pathogenesis of inflammatory disorders and various types of cancer. Endothelial progenitor cells recruited from the bone marrow have been shown to be involved in the formation of new vessels in malignancies and discussed for being a key point in tumour progression and metastasis. However, until now, nothing is known about an interaction between COX and endothelial progenitor cells (EPC). Expression of COX-1 and COX-2 was detected by semiquantitative RT-PCR and Western blot. Proliferation kinetics, cell cycle distribution and rate of apoptosis were analysed by MTT test and FACS analysis. Further analyses revealed an implication of Akt phosphorylation and caspase-3 activation. Both COX-1 and COX-2 expression can be found in bone-marrow-derived endothelial progenitor cells in vitro. COX-2 inhibition leads to a significant reduction in proliferation of endothelial progenitor cells by an increase in apoptosis and cell cycle arrest. COX-2 inhibition leads further to an increased cleavage of caspase-3 protein and inversely to inhibition of Akt activation. Highly proliferating endothelial progenitor cells can be targeted by selective COX-2 inhibition in vitro. These results indicate that upcoming therapy strategies in cancer patients targeting COX-2 may be effective in inhibiting tumour vasculogenesis as well as angiogenic processes.
Yao, Yan; Wang, Chang-yue; Liu, Hui-jun; Tang, Jian-bin; Cai, Jin-hui; Wang, Jing-jun
2015-07-01
Forest bio-fuel, a new type renewable energy, has attracted increasing attention as a promising alternative. In this study, a new method called Sparse Partial Least Squares Regression (SPLS) is used to construct the proximate analysis model to analyze the fuel characteristics of sawdust combining Near Infrared Spectrum Technique. Moisture, Ash, Volatile and Fixed Carbon percentage of 80 samples have been measured by traditional proximate analysis. Spectroscopic data were collected by Nicolet NIR spectrometer. After being filtered by wavelet transform, all of the samples are divided into training set and validation set according to sample category and producing area. SPLS, Principle Component Regression (PCR), Partial Least Squares Regression (PLS) and Least Absolute Shrinkage and Selection Operator (LASSO) are presented to construct prediction model. The result advocated that SPLS can select grouped wavelengths and improve the prediction performance. The absorption peaks of the Moisture is covered in the selected wavelengths, well other compositions have not been confirmed yet. In a word, SPLS can reduce the dimensionality of complex data sets and interpret the relationship between spectroscopic data and composition concentration, which will play an increasingly important role in the field of NIR application.
Length bias correction in gene ontology enrichment analysis using logistic regression.
Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S; Chang, Jeff H
2012-01-01
When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called "length bias", will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible.
Length Bias Correction in Gene Ontology Enrichment Analysis Using Logistic Regression
Mi, Gu; Di, Yanming; Emerson, Sarah; Cumbie, Jason S.; Chang, Jeff H.
2012-01-01
When assessing differential gene expression from RNA sequencing data, commonly used statistical tests tend to have greater power to detect differential expression of genes encoding longer transcripts. This phenomenon, called “length bias”, will influence subsequent analyses such as Gene Ontology enrichment analysis. In the presence of length bias, Gene Ontology categories that include longer genes are more likely to be identified as enriched. These categories, however, are not necessarily biologically more relevant. We show that one can effectively adjust for length bias in Gene Ontology analysis by including transcript length as a covariate in a logistic regression model. The logistic regression model makes the statistical issue underlying length bias more transparent: transcript length becomes a confounding factor when it correlates with both the Gene Ontology membership and the significance of the differential expression test. The inclusion of the transcript length as a covariate allows one to investigate the direct correlation between the Gene Ontology membership and the significance of testing differential expression, conditional on the transcript length. We present both real and simulated data examples to show that the logistic regression approach is simple, effective, and flexible. PMID:23056249
Choudhary, Jaipal S; Naaz, Naiyar; Prabhakar, Chandra S; Lemtur, Moanaro
2016-10-01
The study examined the genetic diversity and demographic history of Bactrocera dorsalis, a destructive and polyphagous insect pest of fruit crops in diverse geographic regions of India. 19 widely dispersed populations of the fly from India and other Asian countries were analysed using partial sequences of mitochondrial cytochrome oxidase I (cox1) and NADH dehydrogenase 1 (nad1) genes to investigate genetic diversity, genetic structure, and demographic history in the region. Genetic diversity indices [number of haplotypes (H), haloptype diversity (Hd), nucleotide diversity (π) and average number of nucleotide difference (k)] of populations revealed that B. dorsalis maintains fairly high level of genetic diversity without isolation by distance among the geographic regions. Demographic analysis showed significant (negative) Tajimas' D and Fu's F S with non significant sum of squared deviations (SSD) values, which indicate the possibility of recent sudden expansion of species and is further supported through distinctively star-like distribution structure of haplotypes among populations. Thus, the results indicate that both ongoing and historical factors have played important role in determining the genetic structure and diversity of the species in India. Consequently, sterile insect technique (SIT) could be a possible management strategy of species in the regions.
Kanbayashi, Yuko; Hosokawa, Toyoshi; Yasui, Kohichiroh; Hongo, Fumiya; Yamaguchi, Kanji; Moriguchi, Michihisa; Miki, Tsuneharu; Itoh, Yoshito
2016-01-01
Predictive factors for sorafenib-induced hand-foot skin reaction (HFSR) using ordered logistic regression analysis were studied. This retrospective analysis evaluated patients admitted to a university hospital in Japan from May 2008 through October 2013. Patients age 20 years or older with relapsed or metastatic renal cell carcinoma, unresectable hepatocellular carcinoma, or gastrointestinal stromal tumor resistant to imatinib and sunitinib were included. Data were manually collected from patients' clinical records and included sex, age, Eastern Cooperative Oncology Group (ECOG) performance status, initial daily dose of sorafenib, duration of sorafenib use, concomitant medications, number of metastases, sites of metastases, physical examination findings, and type of cancer. Laboratory test values related to the patient's medical condition that seemed to influence HFSR or the absorption and pharmacologic effects of sorafenib were also collected. HFSR severity was also assessed. Univariate ordered logistic analysis was performed for HFSR severity outcomes and each candidate independent variable. A multivariate ordered logistic regression model was then constructed using a stepwise forward selection procedure. Data were screened for multicollinearity. Data from 113 patients were evaluated. This analysis identified duration of sorafenib use (odds ratio [OR], 0.0531), use of a proton pump inhibitor (PPI) (OR, 0.351), ECOG performance status (OR, 0.555), C-reactive protein level (OR, 17.74), and male sex (OR, 0.403) as significant factors for the occurrence of HFSR. Multivariate logistic regression analysis revealed that short duration of sorafenib use, avoidance of PPIs, good ECOG performance status, high C-reactive protein level, and female sex were predictive factors for the development of HFSR. Copyright © 2016 by the American Society of Health-System Pharmacists, Inc. All rights reserved.
Cao, Han-Han; Du, Ruo-Fei; Yang, Jia-Ning; Feng, Yi
2014-03-01
In this paper, microcrystalline cellulose WJ101 was used as a model material to investigate the effect of various process parameters on granule yield and friability after dry granulation with a single factor and the effect of comprehensive inspection process parameters on the effect of granule yield and friability, then the correlation between process parameters and granule quality was established. The regress equation was established between process parameters and granule yield and friability by multiple regression analysis, the affecting the order of the size of the order of the process parameters on granule yield and friability was: rollers speed > rollers pressure > speed of horizontal feed. Granule yield was positively correlated with pressure and speed of horizontal feed and negatively correlated rollers speed, while friability was on the contrary. By comparison, fitted value and real value, fitted and real value are basically the same of no significant differences (P > 0.05) and with high precision and reliability.
NASA Astrophysics Data System (ADS)
Dervilis, N.; Worden, K.; Cross, E. J.
2015-07-01
In the data-based approach to structural health monitoring (SHM), the absence of data from damaged structures in many cases forces a dependence on novelty detection as a means of diagnosis. Unfortunately, this means that benign variations in the operating or environmental conditions of the structure must be handled very carefully, lest they lead to false alarms. If novelty detection is implemented in terms of outlier detection, the outliers may arise in the data as the result of both benign and malign causes and it is important to understand their sources. Comparatively recent developments in the field of robust regression have the potential to provide ways of exploring and visualising SHM data as a means of shedding light on the different origins of outliers. The current paper will illustrate the use of robust regression for SHM data analysis through experimental data acquired from the Z24 and Tamar Bridges, although the methods are general and not restricted to SHM or civil infrastructure.
Alados, C.L.; Pueyo, Y.; Giner, M.L.; Navarro, T.; Escos, J.; Barroso, F.; Cabezudo, B.; Emlen, J.M.
2003-01-01
We studied the effect of grazing on the degree of regression of successional vegetation dynamic in a semi-arid Mediterranean matorral. We quantified the spatial distribution patterns of the vegetation by fractal analyses, using the fractal information dimension and spatial autocorrelation measured by detrended fluctuation analyses (DFA). It is the first time that fractal analysis of plant spatial patterns has been used to characterize the regressive ecological succession. Plant spatial patterns were compared over a long-term grazing gradient (low, medium and heavy grazing pressure) and on ungrazed sites for two different plant communities: A middle dense matorral of Chamaerops and Periploca at Sabinar-Romeral and a middle dense matorral of Chamaerops, Rhamnus and Ulex at Requena-Montano. The two communities differed also in the microclimatic characteristics (sea oriented at the Sabinar-Romeral site and inland oriented at the Requena-Montano site). The information fractal dimension increased as we moved from a middle dense matorral to discontinuous and scattered matorral and, finally to the late regressive succession, at Stipa steppe stage. At this stage a drastic change in the fractal dimension revealed a change in the vegetation structure, accurately indicating end successional vegetation stages. Long-term correlation analysis (DFA) revealed that an increase in grazing pressure leads to unpredictability (randomness) in species distributions, a reduction in diversity, and an increase in cover of the regressive successional species, e.g. Stipa tenacissima L. These comparisons provide a quantitative characterization of the successional dynamic of plant spatial patterns in response to grazing perturbation gradient. ?? 2002 Elsevier Science B.V. All rights reserved.
A refined method for multivariate meta-analysis and meta-regression.
Jackson, Daniel; Riley, Richard D
2014-02-20
Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects' standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples.
Kotrashetti, Vijayalakshmi S; Hollikatti, Kiran; Mallapur, M D; Hallikeremath, Seema R; Kale, Alka D
2011-11-01
Palatal rugae patterns are relatively unique to an individual and are well protected by the lips, buccal pad of fat and teeth. They are considered to be stable throughout life following completion of growth, although there is considerable debate on the matter, they can be used successfully in post mortem identification provided an antemortem record exists. Thus the aim of this study was to examine palatal rugae shape among two Indian populations and determine the accuracy in defining the Indian population using logistic regression analysis. The study comprises two groups from geographically different regions of India with basic origin from Maharashtra and Karnataka state. The sample includes 100 plaster cast equally distributed between two populations and genders with age ranging between 18 and 40 years. Impression of maxillary arch was obtained using alginate impression material and plaster cast was made. The rugae was delineated on the cast using a sharp graphite pencil under adequate light and magnification and recorded according to classification given by Kapali et al. and Thomas and Kotze (1983). Chi-Square analysis showed significant difference in wavy, circular and divergent pattern between the two populations. The straight and wavy forms were significant in logistic regression analysis. A predictive value of 71% was obtained in determining the original cases correctly when straight, wavy, curved and circular patterns were assessed. 70% of predictive value was achieved when all rugae patterns were assessed. Mean number of rugae was greater in females compared to males with straight pattern showing statistically significant difference between males and females. Significant difference was recorded among straight, wavy, circular and divergent pattern between two populations. Consequently this study demonstrates moderate accuracy of palatal rugae pattern using logistic regression analysis in identification of Indians. Copyright © 2011 Elsevier Ltd and Faculty
Rhodes, Kirsty M; Turner, Rebecca M; White, Ian R; Jackson, Dan; Spiegelhalter, David J; Higgins, Julian P T
2016-12-20
Many meta-analyses combine results from only a small number of studies, a situation in which the between-study variance is imprecisely estimated when standard methods are applied. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, providing the potential for more robust inference on the effect size of interest. We present a method for performing Bayesian meta-analysis using data augmentation, in which we represent an informative conjugate prior for between-study variance by pseudo data and use meta-regression for estimation. To assist in this, we derive predictive inverse-gamma distributions for the between-study variance expected in future meta-analyses. These may serve as priors for heterogeneity in new meta-analyses. In a simulation study, we compare approximate Bayesian methods using meta-regression and pseudo data against fully Bayesian approaches based on importance sampling techniques and Markov chain Monte Carlo (MCMC). We compare the frequentist properties of these Bayesian methods with those of the commonly used frequentist DerSimonian and Laird procedure. The method is implemented in standard statistical software and provides a less complex alternative to standard MCMC approaches. An importance sampling approach produces almost identical results to standard MCMC approaches, and results obtained through meta-regression and pseudo data are very similar. On average, data augmentation provides closer results to MCMC, if implemented using restricted maximum likelihood estimation rather than DerSimonian and Laird or maximum likelihood estimation. The methods are applied to real datasets, and an extension to network meta-analysis is described. The proposed method facilitates Bayesian meta-analysis in a way that is accessible to applied researchers. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
Regression analysis of mixed recurrent-event and panel-count data with additive rate models.
Zhu, Liang; Zhao, Hui; Sun, Jianguo; Leisenring, Wendy; Robison, Leslie L
2015-03-01
Event-history studies of recurrent events are often conducted in fields such as demography, epidemiology, medicine, and social sciences (Cook and Lawless, 2007, The Statistical Analysis of Recurrent Events. New York: Springer-Verlag; Zhao et al., 2011, Test 20, 1-42). For such analysis, two types of data have been extensively investigated: recurrent-event data and panel-count data. However, in practice, one may face a third type of data, mixed recurrent-event and panel-count data or mixed event-history data. Such data occur if some study subjects are monitored or observed continuously and thus provide recurrent-event data, while the others are observed only at discrete times and hence give only panel-count data. A more general situation is that each subject is observed continuously over certain time periods but only at discrete times over other time periods. There exists little literature on the analysis of such mixed data except that published by Zhu et al. (2013, Statistics in Medicine 32, 1954-1963). In this article, we consider the regression analysis of mixed data using the additive rate model and develop some estimating equation-based approaches to estimate the regression parameters of interest. Both finite sample and asymptotic properties of the resulting estimators are established, and the numerical studies suggest that the proposed methodology works well for practical situations. The approach is applied to a Childhood Cancer Survivor Study that motivated this study.
An application of a microcomputer compiler program to multiple logistic regression analysis.
Sakai, R
1988-01-01
Microcomputer programs for multiple logistic regression analysis were written in BASIC language to determine the usefulness of microcomputers for multivariate analysis, which is an important method in epidemiological studies. The program, carried out by an interpreter system, required a comparatively long computing time for a small amount of data. For example, it took approximately thirty minutes to compute the data of 6 independent variables and 63 matched sets of case and controls (1:4). The majority of the calculation time was spent computing a matrix. The matrix computation time increased cumulatively in proportion to additions in the number of subjects, and increased exponentially with the number of variables. A BASIC compiler was utilized for the program of multiple logistic regression analysis. The compiled program carried out the same computations as above, but within 4 minutes. Therefore, it is evident that a compiler can be an extremely convenient tool for computing multivariate analysis. The two programs produced here were also easily linked with spreadsheet packages to enter data.
Application of artificial neural network to fMRI regression analysis.
Misaki, Masaya; Miyauchi, Satoru
2006-01-15
We used an artificial neural network (ANN) to detect correlations between event sequences and fMRI (functional magnetic resonance imaging) signals. The layered feed-forward neural network, given a series of events as inputs and the fMRI signal as a supervised signal, performed a non-linear regression analysis. This type of ANN is capable of approximating any continuous function, and thus this analysis method can detect any fMRI signals that correlated with corresponding events. Because of the flexible nature of ANNs, fitting to autocorrelation noise is a problem in fMRI analyses. We avoided this problem by using cross-validation and an early stopping procedure. The results showed that the ANN could detect various responses with different time courses. The simulation analysis also indicated an additional advantage of ANN over non-parametric methods in detecting parametrically modulated responses, i.e., it can detect various types of parametric modulations without a priori assumptions. The ANN regression analysis is therefore beneficial for exploratory fMRI analyses in detecting continuous changes in responses modulated by changes in input values.
Irrechukwu, Onyi N; Reiter, David A; Lin, Ping-Chang; Roque, Remigio A; Fishbein, Kenneth W; Spencer, Richard G
2012-06-01
Increased sensitivity in the characterization of cartilage matrix status by magnetic resonance (MR) imaging, through the identification of surrogate markers for tissue quality, would be of great use in the noninvasive evaluation of engineered cartilage. Recent advances in MR evaluation of cartilage include multiexponential and multiparametric analysis, which we now extend to engineered cartilage. We studied constructs which developed from chondrocytes seeded in collagen hydrogels. MR measurements of transverse relaxation times were performed on samples after 1, 2, 3, and 4 weeks of development. Corresponding biochemical measurements of sulfated glycosaminoglycan (sGAG) were also performed. sGAG per wet weight increased from 7.74±1.34 μg/mg in week 1 to 21.06±4.14 μg/mg in week 4. Using multiexponential T₂ analysis, we detected at least three distinct water compartments, with T₂ values and weight fractions of (45 ms, 3%), (200 ms, 4%), and (500 ms, 97%), respectively. These values are consistent with known properties of engineered cartilage and previous studies of native cartilage. Correlations between sGAG and MR measurements were examined using conventional univariate analysis with T₂ data from monoexponential fits with individual multiexponential compartment fractions and sums of these fractions, through multiple linear regression based on linear combinations of fractions, and, finally, with multivariate analysis using the support vector regression (SVR) formalism. The phenomenological relationship between T₂ from monoexponential fitting and sGAG exhibited a correlation coefficient of r²=0.56, comparable to the more physically motivated correlations between individual fractions or sums of fractions and sGAG; the correlation based on the sum of the two proteoglycan-associated fractions was r²=0.58. Correlations between measured sGAG and those calculated using standard linear regression were more modest, with r² in the range 0
Ryu, Duchwan; Li, Erning; Mallick, Bani K
2011-06-01
We consider nonparametric regression analysis in a generalized linear model (GLM) framework for data with covariates that are the subject-specific random effects of longitudinal measurements. The usual assumption that the effects of the longitudinal covariate processes are linear in the GLM may be unrealistic and if this happens it can cast doubt on the inference of observed covariate effects. Allowing the regression functions to be unknown, we propose to apply Bayesian nonparametric methods including cubic smoothing splines or P-splines for the possible nonlinearity and use an additive model in this complex setting. To improve computational efficiency, we propose the use of data-augmentation schemes. The approach allows flexible covariance structures for the random effects and within-subject measurement errors of the longitudinal processes. The posterior model space is explored through a Markov chain Monte Carlo (MCMC) sampler. The proposed methods are illustrated and compared to other approaches, the "naive" approach and the regression calibration, via simulations and by an application that investigates the relationship between obesity in adulthood and childhood growth curves.
Bayesian analysis of a multivariate null intercept errors-in-variables regression model.
Aoki, Reiko; Bolfarine, Heleno; Achcar, Jorge A; Dorival, Leão P Júnior
2003-11-01
Longitudinal data are of great interest in analysis of clinical trials. In many practical situations the covariate can not be measured precisely and a natural alternative model is the errors-in-variables regression models. In this paper we study a null intercept errors-in-variables regression model with a structure of dependency between the response variables within the same group. We apply the model to real data presented in Hadgu and Koch (Hadgu, A., Koch, G. (1999). Application of generalized estimating equations to a dental randomized clinical trial. J. Biopharmaceutical Statistics 9(1):161-178). In that study volunteers with preexisting dental plaque were randomized to two experimental mouth rinses (A and B) or a control mouth rinse with double blinding. The dental plaque index was measured for each subject in the beginning of the study and at two follow-up times, which leads to the presence of an interclass correlation. We propose the use of a Bayesian approach to model a multivariate null intercept errors-in-variables regression model to the longitudinal data. The proposed Bayesian approach accommodates the correlated measurements and incorporates the restriction that the slopes must lie in the (0, 1) interval. A Gibbs sampler is used to perform the computations.
Dhanya, S; Kumari Roshni, V S
2016-01-01
Textures play an important role in image classification. This paper proposes a high performance texture classification method using a combination of multiresolution analysis tool and linear regression modelling by channel elimination. The correlation between different frequency regions has been validated as a sort of effective texture characteristic. This method is motivated by the observation that there exists a distinctive correlation between the image samples belonging to the same kind of texture, at different frequency regions obtained by a wavelet transform. Experimentally, it is observed that this correlation differs across textures. The linear regression modelling is employed to analyze this correlation and extract texture features that characterize the samples. Our method considers not only the frequency regions but also the correlation between these regions. This paper primarily focuses on applying the Dual Tree Complex Wavelet Packet Transform and the Linear Regression model for classification of the obtained texture features. Additionally the paper also presents a comparative assessment of the classification results obtained from the above method with two more types of wavelet transform methods namely the Discrete Wavelet Transform and the Discrete Wavelet Packet Transform.
Chikae, Miyuki; Ikeda, Ryuzoh; Kerman, Kagan; Morita, Yasutaka; Tamiya, Eiichi
2006-11-01
The composting process of food wastes and tree cuttings was examined on four composting types composed from two kinds of systems and added mixture of microorganisms. The time courses of 32 parameters in each composting type were observed. The efficient composting system was found to be the static aerated reactor system in comparison with the turning pile one. Using the multiple regression analysis of all the data (159 samples) obtained from this study, some parameters were selected to predict the germination index (GI) value, which was adopted as a marker of compost maturity. For example, using the regression model generated from pH, NH(4)(+) concentration, acid phosphatase activity, and esterase activity of water extracts of the compost, GI value was expressed by the multi-linear regression equation (p<0.0001). High correlations between the measured GI value and the predicted one were made in each type of compost. As a result of these observations, the compost maturity might be predicted by only sensing of the water extract at the composting site without any requirements for a large-size equipment and skill, and this prediction system could contribute to the production of a stable compost in wide-spread use for the recycling market.
Regression Analysis of Stage Variability for West-Central Florida Lakes
Sacks, Laura A.; Ellison, Donald L.; Swancar, Amy
2008-01-01
The variability in a lake's stage depends upon many factors, including surface-water flows, meteorological conditions, and hydrogeologic characteristics near the lake. An understanding of the factors controlling lake-stage variability for a population of lakes may be helpful to water managers who set regulatory levels for lakes. The goal of this study is to determine whether lake-stage variability can be predicted using multiple linear regression and readily available lake and basin characteristics defined for each lake. Regressions were evaluated for a recent 10-year period (1996-2005) and for a historical 10-year period (1954-63). Ground-water pumping is considered to have affected stage at many of the 98 lakes included in the recent period analysis, and not to have affected stage at the 20 lakes included in the historical period analysis. For the recent period, regression models had coefficients of determination (R2) values ranging from 0.60 to 0.74, and up to five explanatory variables. Standard errors ranged from 21 to 37 percent of the average stage variability. Net leakage was the most important explanatory variable in regressions describing the full range and low range in stage variability for the recent period. The most important explanatory variable in the model predicting the high range in stage variability was the height over median lake stage at which surface-water outflow would occur. Other explanatory variables in final regression models for the recent period included the range in annual rainfall for the period and several variables related to local and regional hydrogeology: (1) ground-water pumping within 1 mile of each lake, (2) the amount of ground-water inflow (by category), (3) the head gradient between the lake and the Upper Floridan aquifer, and (4) the thickness of the intermediate confining unit. Many of the variables in final regression models are related to hydrogeologic characteristics, underscoring the importance of ground
Li, Wen-wen; Ren, Yi-jing; Li, Jian; Huang, Wei-yi
2015-04-01
To observe the ultrastructure of adult Gnathostoma doloresi worms isolated from wild boar by using scanning electron microscope (SEM), and analyze its phylogenetic relationships based on ITS2 and COXI gene sequences. Two adult G. doloresi worms were fixed by glutaraldehyde and osmium peroxide. Ultrastructural characters of those samples were observed under SEM. Amplification and sequencing of the ITS2 and COXI genes were performed following the extraction of total genomic DNA. Sequence analysis was performed based on multiple alignments and phylogenetic analysis was made by Neighbor-Joining method using MEGA 6.0. The bottle-shaped adult worm covered with numerous small spines. The cervical groove connected head bulb and body without spines. There was obvious distinction in body spines which surround cervical papillae and swollen area in the middle part of the body. The fragments of ITS2 (418 bp) and COXI (381 bp) gene were obtained by PCR combined with sequencing, and were registered to the GenBank database with the accession No. of JN408329 and JN408299, respectively. The BLAST results showed that, two sequences had 99% and 98% consistency with G. doloresi ITS2 (GenBank accession No. AB181156) and COX1 (No. AB180100) gene sequences, respectively. The phylogenetic tree indicated that the two G. doloresi worms were at the same clade with a bootstrap value at 100% and 85% based on the ITS2 and COXI sequences, respectively. G. doloresi and G. hispidum were also clustered together. The results provide adequate information for the SEM morphological data of adult G. doloresi worms, and its phylogenetic relationship.
NASA Astrophysics Data System (ADS)
Liu, Pudong; Shi, Runhe; Wang, Hong; Bai, Kaixu; Gao, Wei
2014-10-01
Leaf pigments are key elements for plant photosynthesis and growth. Traditional manual sampling of these pigments is labor-intensive and costly, which also has the difficulty in capturing their temporal and spatial characteristics. The aim of this work is to estimate photosynthetic pigments at large scale by remote sensing. For this purpose, inverse model were proposed with the aid of stepwise multiple linear regression (SMLR) analysis. Furthermore, a leaf radiative transfer model (i.e. PROSPECT model) was employed to simulate the leaf reflectance where wavelength varies from 400 to 780 nm at 1 nm interval, and then these values were treated as the data from remote sensing observations. Meanwhile, simulated chlorophyll concentration (Cab), carotenoid concentration (Car) and their ratio (Cab/Car) were taken as target to build the regression model respectively. In this study, a total of 4000 samples were simulated via PROSPECT with different Cab, Car and leaf mesophyll structures as 70% of these samples were applied for training while the last 30% for model validation. Reflectance (r) and its mathematic transformations (1/r and log (1/r)) were all employed to build regression model respectively. Results showed fair agreements between pigments and simulated reflectance with all adjusted coefficients of determination (R2) larger than 0.8 as 6 wavebands were selected to build the SMLR model. The largest value of R2 for Cab, Car and Cab/Car are 0.8845, 0.876 and 0.8765, respectively. Meanwhile, mathematic transformations of reflectance showed little influence on regression accuracy. We concluded that it was feasible to estimate the chlorophyll and carotenoids and their ratio based on statistical model with leaf reflectance data.
Spatial regression analysis on 32 years of total column ozone data
NASA Astrophysics Data System (ADS)
Knibbe, J. S.; van der A, R. J.; de Laat, A. T. J.
2014-08-01
Multiple-regression analyses have been performed on 32 years of total ozone column data that was spatially gridded with a 1 × 1.5° resolution. The total ozone data consist of the MSR (Multi Sensor Reanalysis; 1979-2008) and 2 years of assimilated SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) ozone data (2009-2010). The two-dimensionality in this data set allows us to perform the regressions locally and investigate spatial patterns of regression coefficients and their explanatory power. Seasonal dependencies of ozone on regressors are included in the analysis. A new physically oriented model is developed to parameterize stratospheric ozone. Ozone variations on nonseasonal timescales are parameterized by explanatory variables describing the solar cycle, stratospheric aerosols, the quasi-biennial oscillation (QBO), El Niño-Southern Oscillation (ENSO) and stratospheric alternative halogens which are parameterized by the effective equivalent stratospheric chlorine (EESC). For several explanatory variables, seasonally adjusted versions of these explanatory variables are constructed to account for the difference in their effect on ozone throughout the year. To account for seasonal variation in ozone, explanatory variables describing the polar vortex, geopotential height, potential vorticity and average day length are included. Results of this regression model are compared to that of a similar analysis based on a more commonly applied statistically oriented model. The physically oriented model provides spatial patterns in the regression results for each explanatory variable. The EESC has a significant depleting effect on ozone at mid- and high latitudes, the solar cycle affects ozone positively mostly in the Southern Hemisphere, stratospheric aerosols affect ozone negatively at high northern latitudes, the effect of QBO is positive and negative in the tropics and mid- to high latitudes, respectively, and ENSO affects ozone negatively
Regression models for the analysis of longitudinal Gaussian data from multiple sources.
O'Brien, Liam M; Fitzmaurice, Garrett M
2005-06-15
We present a regression model for the joint analysis of longitudinal multiple source Gaussian data. Longitudinal multiple source data arise when repeated measurements are taken from two or more sources, and each source provides a measure of the same underlying variable and on the same scale. This type of data generally produces a relatively large number of observations per subject; thus estimation of an unstructured covariance matrix often may not be possible. We consider two methods by which parsimonious models for the covariance can be obtained for longitudinal multiple source data. The methods are illustrated with an example of multiple informant data arising from a longitudinal interventional trial in psychiatry.
NASA Technical Reports Server (NTRS)
Waller, M. C.
1976-01-01
An electro-optical device called an oculometer which tracks a subject's lookpoint as a time function has been used to collect data in a real-time simulation study of instrument landing system (ILS) approaches. The data describing the scanning behavior of a pilot during the instrument approaches have been analyzed by use of a stepwise regression analysis technique. A statistically significant correlation between pilot workload, as indicated by pilot ratings, and scanning behavior has been established. In addition, it was demonstrated that parameters derived from the scanning behavior data can be combined in a mathematical equation to provide a good representation of pilot workload.
Estimating the causes of traffic accidents using logistic regression and discriminant analysis.
Karacasu, Murat; Ergül, Barış; Altin Yavuz, Arzu
2014-01-01
Factors that affect traffic accidents have been analysed in various ways. In this study, we use the methods of logistic regression and discriminant analysis to determine the damages due to injury and non-injury accidents in the Eskisehir Province. Data were obtained from the accident reports of the General Directorate of Security in Eskisehir; 2552 traffic accidents between January and December 2009 were investigated regarding whether they resulted in injury. According to the results, the effects of traffic accidents were reflected in the variables. These results provide a wealth of information that may aid future measures toward the prevention of undesired results.
Zlatarić, Dubravka Knezović; Celebić, Asja
2008-01-01
This study aimed to analyze factors related to patients' general satisfaction with removable partial dentures (RPDs), such as esthetics, retention, speech, chewing, and comfort. A total of 103 patients with Kennedy Class I RPDs (34 to 82 years old; mean age: 63; 35 men, 68 women) assessed their satisfaction with dentures. Stepwise multiple regression analysis was used to evaluate the relationship among the factors. Significant correlations were found between general satisfaction and each of the individual components (P < .05). The patients' assessment of esthetics explained almost 50% of general satisfaction in both arches (P < .05). Esthetics, chewing, and speech had significant effects on the patients' general satisfaction with dentures.
NASA Astrophysics Data System (ADS)
Mandal, Nilrudra; Doloi, Biswanath; Mondal, Biswanath
2016-01-01
In the present study, an attempt has been made to apply the Taguchi parameter design method and regression analysis for optimizing the cutting conditions on surface finish while machining AISI 4340 steel with the help of the newly developed yttria based Zirconia Toughened Alumina (ZTA) inserts. These inserts are prepared through wet chemical co-precipitation route followed by powder metallurgy process. Experiments have been carried out based on an orthogonal array L9 with three parameters (cutting speed, depth of cut and feed rate) at three levels (low, medium and high). Based on the mean response and signal to noise ratio (SNR), the best optimal cutting condition has been arrived at A3B1C1 i.e. cutting speed is 420 m/min, depth of cut is 0.5 mm and feed rate is 0.12 m/min considering the condition smaller is the better approach. Analysis of Variance (ANOVA) is applied to find out the significance and percentage contribution of each parameter. The mathematical model of surface roughness has been developed using regression analysis as a function of the above mentioned independent variables. The predicted values from the developed model and experimental values are found to be very close to each other justifying the significance of the model. A confirmation run has been carried out with 95 % confidence level to verify the optimized result and the values obtained are within the prescribed limit.
Ziemssen, Tjalf; Reimann, Manja; Gasch, Julia; Rüdiger, Heinz
2013-09-01
Biological rhythms, describing the temporal variation of biological processes, are a characteristic feature of complex systems. The analysis of biological rhythms can provide important insights into the pathophysiology of different diseases, especially, in cardiovascular medicine. In the field of the autonomic nervous system, heart rate variability (HRV) and baroreflex sensitivity (BRS) describe important fluctuations of blood pressure and heart rate which are often analyzed by Fourier transformation. However, these parameters are stochastic with overlaying rhythmical structures. R-R intervals as independent variables of time are not equidistant. That is why the trigonometric regressive spectral (TRS) analysis--reviewed in this paper--was introduced, considering both the statistical and rhythmical features of such time series. The data segments required for TRS analysis can be as short as 20 s allowing for dynamic evaluation of heart rate and blood pressure interaction over longer periods. Beyond HRV, TRS also estimates BRS based on linear regression analyses of coherent heart rate and blood pressure oscillations. An additional advantage is that all oscillations are analyzed by the same (maximal) number of R-R intervals thereby providing a high number of individual BRS values. This ensures a high confidence level of BRS determination which, along with short recording periods, may be of profound clinical relevance. The dynamic assessment of heart rate and blood pressure spectra by TRS allows a more precise evaluation of cardiovascular modulation under different settings as has already been demonstrated in different clinical studies.
Neck-focused panic attacks among Cambodian refugees; a logistic and linear regression analysis.
Hinton, Devon E; Chhean, Dara; Pich, Vuth; Um, Khin; Fama, Jeanne M; Pollack, Mark H
2006-01-01
Consecutive Cambodian refugees attending a psychiatric clinic were assessed for the presence and severity of current--i.e., at least one episode in the last month--neck-focused panic. Among the whole sample (N=130), in a logistic regression analysis, the Anxiety Sensitivity Index (ASI; odds ratio=3.70) and the Clinician-Administered PTSD Scale (CAPS; odds ratio=2.61) significantly predicted the presence of current neck panic (NP). Among the neck panic patients (N=60), in the linear regression analysis, NP severity was significantly predicted by NP-associated flashbacks (beta=.42), NP-associated catastrophic cognitions (beta=.22), and CAPS score (beta=.28). Further analysis revealed the effect of the CAPS score to be significantly mediated (Sobel test [Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182]) by both NP-associated flashbacks and catastrophic cognitions. In the care of traumatized Cambodian refugees, NP severity, as well as NP-associated flashbacks and catastrophic cognitions, should be specifically assessed and treated.
Selenium Exposure and Cancer Risk: an Updated Meta-analysis and Meta-regression
Cai, Xianlei; Wang, Chen; Yu, Wanqi; Fan, Wenjie; Wang, Shan; Shen, Ning; Wu, Pengcheng; Li, Xiuyang; Wang, Fudi
2016-01-01
The objective of this study was to investigate the associations between selenium exposure and cancer risk. We identified 69 studies and applied meta-analysis, meta-regression and dose-response analysis to obtain available evidence. The results indicated that high selenium exposure had a protective effect on cancer risk (pooled OR = 0.78; 95%CI: 0.73–0.83). The results of linear and nonlinear dose-response analysis indicated that high serum/plasma selenium and toenail selenium had the efficacy on cancer prevention. However, we did not find a protective efficacy of selenium supplement. High selenium exposure may have different effects on specific types of cancer. It decreased the risk of breast cancer, lung cancer, esophageal cancer, gastric cancer, and prostate cancer, but it was not associated with colorectal cancer, bladder cancer, and skin cancer. PMID:26786590
Selenium Exposure and Cancer Risk: an Updated Meta-analysis and Meta-regression.
Cai, Xianlei; Wang, Chen; Yu, Wanqi; Fan, Wenjie; Wang, Shan; Shen, Ning; Wu, Pengcheng; Li, Xiuyang; Wang, Fudi
2016-01-20
The objective of this study was to investigate the associations between selenium exposure and cancer risk. We identified 69 studies and applied meta-analysis, meta-regression and dose-response analysis to obtain available evidence. The results indicated that high selenium exposure had a protective effect on cancer risk (pooled OR = 0.78; 95%CI: 0.73-0.83). The results of linear and nonlinear dose-response analysis indicated that high serum/plasma selenium and toenail selenium had the efficacy on cancer prevention. However, we did not find a protective efficacy of selenium supplement. High selenium exposure may have different effects on specific types of cancer. It decreased the risk of breast cancer, lung cancer, esophageal cancer, gastric cancer, and prostate cancer, but it was not associated with colorectal cancer, bladder cancer, and skin cancer.
Jalal, Hawre; Goldhaber-Fiebert, Jeremy D.; Kuntz, Karen M.
2016-01-01
Decision makers often desire both guidance on the most cost-effective interventions given current knowledge and also the value of collecting additional information to improve the decisions made [i.e., from value of information (VOI) analysis]. Unfortunately, VOI analysis remains underutilized due to the conceptual, mathematical and computational challenges of implementing Bayesian decision theoretic approaches in models of sufficient complexity for real-world decision making. In this study, we propose a novel practical approach for conducting VOI analysis using a combination of probabilistic sensitivity analysis, linear regression metamodeling, and unit normal loss integral function – a parametric approach to VOI analysis. We adopt a linear approximation and leverage a fundamental assumption of VOI analysis which requires that all sources of prior uncertainties be accurately specified. We provide examples of the approach and show that the assumptions we make do not induce substantial bias but greatly reduce the computational time needed to perform VOI analysis. Our approach avoids the need to analytically solve or approximate joint Bayesian updating, requires only one set of probabilistic sensitivity analysis simulations, and can be applied in models with correlated input parameters. PMID:25840900
Barros, Aluísio JD; Hirakata, Vânia N
2003-01-01
Background Cross-sectional studies with binary outcomes analyzed by logistic regression are frequent in the epidemiological literature. However, the odds ratio can importantly overestimate the prevalence ratio, the measure of choice in these studies. Also, controlling for confounding is not equivalent for the two measures. In this paper we explore alternatives for modeling data of such studies with techniques that directly estimate the prevalence ratio. Methods We compared Cox regression with constant time at risk, Poisson regression and log-binomial regression against the standard Mantel-Haenszel estimators. Models with robust variance estimators in Cox and Poisson regressions and variance corrected by the scale parameter in Poisson regression were also evaluated. Results Three outcomes, from a cross-sectional study carried out in Pelotas, Brazil, with different levels of prevalence were explored: weight-for-age deficit (4%), asthma (31%) and mother in a paid job (52%). Unadjusted Cox/Poisson regression and Poisson regression with scale parameter adjusted by deviance performed worst in terms of interval estimates. Poisson regression with scale parameter adjusted by χ2 showed variable performance depending on the outcome prevalence. Cox/Poisson regression with robust variance, and log-binomial regression performed equally well when the model was correctly specified. Conclusions Cox or Poisson regression with robust variance and log-binomial regression provide correct estimates and are a better alternative for the analysis of cross-sectional studies with binary outcomes than logistic regression, since the prevalence ratio is more interpretable and easier to communicate to non-specialists than the odds ratio. However, precautions are needed to avoid estimation problems in specific situations. PMID:14567763
Prasanna, S; Manivannan, E; Chaturvedi, S C
2005-04-15
As a part of our continuing efforts in discerning the structural and physicochemical requirements for selective COX-2 over COX-1 inhibition among the fused pyrazole ring systems, herein we report the QSAR analyses of the title compounds. The conformational flexibility of the title compounds was examined using a simple connection table representation. The conformational investigation was aided by calculating a connection table parameter called fraction of rotable bonds, b_rotR encompassing the number of rotable bonds and b_count, the number of bonds including implicit hydrogens of each ligand. The hydrophobic and steric correlation of the title compounds towards selective COX-2 inhibition was reported previously in one of our recent publications. In this communication, we attempt to calculate Wang-Ford charges of the non-hydrogen common atoms of AM1 optimized geometries of the title compounds. Owing to the partial conformational flexibility of title compounds, conformationally restricted and unrestricted descriptors were calculated from MOE. Correlation analysis of these 2D, 3D and Wang-Ford charges was accomplished by linear regression analysis. 2D molecular descriptor b_single, 3D molecular descriptors glob, std_dim3 showed significant contribution towards COX-2 inhibitory activity. Balaban J, a connectivity topological index showed a negative and positive contribution towards COX-1 and selective COX-2 over COX-1 inhibition, respectively. Wang-Ford charges calculated on C(7) showed a significant contribution towards COX-1 inhibitory activity whereas charges calculated on C(8) were crucial in governing the selectivity of COX-2 over COX-1 inhibition among these congeners.
NASA Astrophysics Data System (ADS)
Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.
2013-06-01
This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.
Automated particle identification through regression analysis of size, shape and colour
NASA Astrophysics Data System (ADS)
Rodriguez Luna, J. C.; Cooper, J. M.; Neale, S. L.
2016-04-01
Rapid point of care diagnostic tests and tests to provide therapeutic information are now available for a range of specific conditions from the measurement of blood glucose levels for diabetes to card agglutination tests for parasitic infections. Due to a lack of specificity these test are often then backed up by more conventional lab based diagnostic methods for example a card agglutination test may be carried out for a suspected parasitic infection in the field and if positive a blood sample can then be sent to a lab for confirmation. The eventual diagnosis is often achieved by microscopic examination of the sample. In this paper we propose a computerized vision system for aiding in the diagnostic process; this system used a novel particle recognition algorithm to improve specificity and speed during the diagnostic process. We will show the detection and classification of different types of cells in a diluted blood sample using regression analysis of their size, shape and colour. The first step is to define the objects to be tracked by a Gaussian Mixture Model for background subtraction and binary opening and closing for noise suppression. After subtracting the objects of interest from the background the next challenge is to predict if a given object belongs to a certain category or not. This is a classification problem, and the output of the algorithm is a Boolean value (true/false). As such the computer program should be able to "predict" with reasonable level of confidence if a given particle belongs to the kind we are looking for or not. We show the use of a binary logistic regression analysis with three continuous predictors: size, shape and color histogram. The results suggest this variables could be very useful in a logistic regression equation as they proved to have a relatively high predictive value on their own.
Yin, Wenjing; Xu, Zhengliang; Sheng, Jiagen; Zhang, Changqing; Zhu, Zhenhong
2016-05-16
To evaluate the potential risk factors of the development of femoral head osteonecrosis after healed intertrochanteric fractures. We retrospectively reviewed all patients who were operated upon with closed reduction and internal fixation for intertrochanteric fractures by our medical group from December 1993 to December 2012. Patients with healed fractures were identified. Age, gender, comorbidities favouring osteonecrosis, causes of injuries, fracture patterns, the location of the primary fracture line, time from injury to surgery, fixation methods, and the development of femur head osteonecrosis of these patients were summarised. Univariate and multivariate logistic regression analysis were performed to evaluate the correlation between potential risk factors and the development of femoral head osteonecrosis. A total of 916 patients with healed intertrochanteric fractures were identified. Femoral head osteonecrosis was found in 8 cases (0.87%). According to the results of univariate logistic regression, a more proximal fracture line, fixation with dynamic hip screws and age were found to be statistically significant factors. The results of multivariate logistic regression analysis indicated that the statistically significant predictors of femoral head osteonecrosis were younger age (odds ratio [OR] = 17.103; 95% confidence interval [CI], 1.988-147.111), a more proximal fracture line (OR = 31.439; 95% CI, 3.700-267.119) and applying dynamic hip screw as the internal fixation (OR = 11.114; 95% CI, 2.064-59.854). Regular follow-up is commended in young patients with a proximal fracture line who underwent closed reduction and internal fixation with dynamic hip screw, even though the bone had healed.
Regression analysis of growth responses to water depth in three wetland plant species
Sorrell, Brian K.; Tanner, Chris C.; Brix, Hans
2012-01-01
Background and aims Plant species composition in wetlands and on lakeshores often shows dramatic zonation, which is frequently ascribed to differences in flooding tolerance. This study compared the growth responses to water depth of three species (Phormium tenax, Carex secta and Typha orientalis) differing in depth preferences in wetlands, using non-linear and quantile regression analyses to establish how flooding tolerance can explain field zonation. Methodology Plants were established for 8 months in outdoor cultures in waterlogged soil without standing water, and then randomly allocated to water depths from 0 to 0.5 m. Morphological and growth responses to depth were followed for 54 days before harvest, and then analysed by repeated-measures analysis of covariance, and non-linear and quantile regression analysis (QRA), to compare flooding tolerances. Principal results Growth responses to depth differed between the three species, and were non-linear. Phormium tenax growth decreased rapidly in standing water >0.25 m depth, C. secta growth increased initially with depth but then decreased at depths >0.30 m, accompanied by increased shoot height and decreased shoot density, and T. orientalis was unaffected by the 0- to 0.50-m depth range. In P. tenax the decrease in growth was associated with a decrease in the number of leaves produced per ramet and in C. secta the effect of water depth was greatest for the tallest shoots. Allocation patterns were unaffected by depth. Conclusions The responses are consistent with the principle that zonation in the field is primarily structured by competition in shallow water and by physiological flooding tolerance in deep water. Regression analyses, especially QRA, proved to be powerful tools in distinguishing genuine phenotypic responses to water depth from non-phenotypic variation due to size and developmental differences. PMID:23259044
Structured exercise improves mobility after hip fracture: a meta-analysis with meta-regression.
Diong, Joanna; Allen, Natalie; Sherrington, Catherine
2016-03-01
To determine the effect of structured exercise on overall mobility in people after hip fracture. To explore associations between trial-level characteristics and overall mobility. Systematic review, meta-analysis and meta-regression. MEDLINE, EMBASE, CINAHL, the Cochrane Central Register of Controlled Trials, the Cochrane Bone, Joint and Muscle Trauma Group Specialised Register and the Physiotherapy Evidence Database to May 2014. Randomised controlled trials of structured exercise, which aimed to improve mobility compared with a control intervention in adult participants after surgery for hip fracture were included. Data were extracted by one investigator and checked by an independent investigator. Standardised mean differences (SMD) of overall mobility were meta-analysed using random effects models. Random effects meta-regression was used to explore associations between trial-level characteristics and overall mobility. 13 trials included in the meta-analysis involved 1903 participants. The pooled Hedges' g SMD for overall mobility was 0.35 (95% CI 0.12 to 0.58, p=0.002) in favour of the intervention. Meta-regression showed greater treatment effects in trials that included progressive resistance exercise (change in SMD=0.58, 95% CI 0.17 to 0.98, p=0.008, adjusted R2=60%) and delivered interventions in settings other than hospital alone (change in SMD=0.50, 95% CI 0.08 to 0.93, p=0.024, adjusted R2=49%). Structured exercise produced small improvements on overall mobility after hip fracture. Interventions that included progressive resistance training and were delivered in other settings were more effective, although the latter may have been confounded by duration of interventions. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
Wei, Wang; Yuan-Yuan, Jin; Ci, Yan; Ahan, Alayi; Ming-Qin, Cao
2016-10-06
The spatial interplay between socioeconomic factors and tuberculosis (TB) cases contributes to the understanding of regional tuberculosis burdens. Historically, local Poisson Geographically Weighted Regression (GWR) has allowed for the identification of the geographic disparities of TB cases and their relevant socioeconomic determinants, thereby forecasting local regression coefficients for the relations between the incidence of TB and its socioeconomic determinants. Therefore, the aims of this study were to: (1) identify the socioeconomic determinants of geographic disparities of smear positive TB in Xinjiang, China (2) confirm if the incidence of smear positive TB and its associated socioeconomic determinants demonstrate spatial variability (3) compare the performance of two main models: one is Ordinary Least Square Regression (OLS), and the other local GWR model. Reported smear-positive TB cases in Xinjiang were extracted from the TB surveillance system database during 2004-2010. The average number of smear-positive TB cases notified in Xinjiang was collected from 98 districts/counties. The population density (POPden), proportion of minorities (PROmin), number of infectious disease network reporting agencies (NUMagen), proportion of agricultural population (PROagr), and per capita annual gross domestic product (per capita GDP) were gathered from the Xinjiang Statistical Yearbook covering a period from 2004 to 2010. The OLS model and GWR model were then utilized to investigate socioeconomic determinants of smear-positive TB cases. Geoda 1.6.7, and GWR 4.0 software were used for data analysis. Our findings indicate that the relations between the average number of smear-positive TB cases notified in Xinjiang and their socioeconomic determinants (POPden, PROmin, NUMagen, PROagr, and per capita GDP) were significantly spatially non-stationary. This means that in some areas more smear-positive TB cases could be related to higher socioeconomic determinant regression
COX2 Inhibition Reduces Aortic Valve Calcification In Vivo
Wirrig, Elaine E.; Gomez, M. Victoria; Hinton, Robert B.; Yutzey, Katherine E.
2016-01-01
Objective Calcific aortic valve disease (CAVD) is a significant cause of morbidity and mortality, which affects approximately 1% of the US population and is characterized by calcific nodule formation and stenosis of the valve. Klotho-deficient mice were used to study the molecular mechanisms of CAVD as they develop robust aortic valve (AoV) calcification. Through microarray analysis of AoV tissues from klotho-deficient and wild type mice, increased expression of the gene encoding cyclooxygenase 2/COX2 (Ptgs2) was found. COX2 activity contributes to bone differentiation and homeostasis, thus the contribution of COX2 activity to AoV calcification was assessed. Approach and Results In klotho-deficient mice, COX2 expression is increased throughout regions of valve calcification and is induced in the valvular interstitial cells (VICs) prior to calcification formation. Similarly, COX2 expression is increased in human diseased AoVs. Treatment of cultured porcine aortic VICs with osteogenic media induces bone marker gene expression and calcification in vitro, which is blocked by inhibition of COX2 activity. In vivo, genetic loss of function of COX2 cyclooxygenase activity partially rescues AoV calcification in klotho-deficient mice. Moreover, pharmacologic inhibition of COX2 activity in klotho-deficient mice via celecoxib-containing diet reduces AoV calcification and blocks osteogenic gene expression. Conclusions COX2 expression is upregulated in CAVD and its activity contributes to osteogenic gene induction and valve calcification in vitro and in vivo. PMID:25722432
Is periodontitis associated with halitosis? A systematic review and meta-regression analysis.
Silva, Manuela F; Cademartori, Mariana G; Leite, Fábio R M; López, Rodrigo; Demarco, Flávio F; Nascimento, Gustavo G
2017-10-01
To systematically review the literature in order to investigate a potential association between periodontitis and halitosis. Electronic searches were performed in four different databases: PubMed, Scopus, Web of Science and Scielo. Population-based observational studies that tested the association between periodontitis and halitosis were included. Additionally, meta-analysis, meta-regression and subgroup analyses were performed to synthesize the evidence. A total of 1,107 articles were identified in electronic searches; out of which, five were included within the meta-analysis. Pooled estimates revealed that individuals with periodontitis presented 3.16 times higher odds (OR 3.16; 95% CI: 1.12-8.95) of having halitosis. Meta-regression and subgroups analyses showed that criteria used for halitosis and periodontitis assessment explained nearly 45% and 24% of heterogeneity between studies, respectively. Positive association between periodontitis and halitosis was found in pooled results of population-based observational studies. However, this evidence is derived from cross-sectional studies. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Jiang, Mingfeng; Zhu, Lingyan; Wang, Yaming; Xia, Ling; Shou, Guofa; Liu, Feng; Crozier, Stuart
2011-03-21
Non-invasively reconstructing the transmembrane potentials (TMPs) from body surface potentials (BSPs) constitutes one form of the inverse ECG problem that can be treated as a regression problem with multi-inputs and multi-outputs, and which can be solved using the support vector regression (SVR) method. In developing an effective SVR model, feature extraction is an important task for pre-processing the original input data. This paper proposes the application of principal component analysis (PCA) and kernel principal component analysis (KPCA) to the SVR method for feature extraction. Also, the genetic algorithm and simplex optimization method is invoked to determine the hyper-parameters of the SVR. Based on the realistic heart-torso model, the equivalent double-layer source method is applied to generate the data set for training and testing the SVR model. The experimental results show that the SVR method with feature extraction (PCA-SVR and KPCA-SVR) can perform better than that without the extract feature extraction (single SVR) in terms of the reconstruction of the TMPs on epi- and endocardial surfaces. Moreover, compared with the PCA-SVR, the KPCA-SVR features good approximation and generalization ability when reconstructing the TMPs.
Regression Analysis for Complex Doping of X8R Ceramics Based on Uniform Design
NASA Astrophysics Data System (ADS)
Tang, Bin; Zhang, Shuren; Zhou, Xiaohua; Wang, Ding; Yuan, Ying
2007-10-01
Regression analysis based on uniform design was introduced as a new approach for designing BaTiO3-based X8R ceramics. The amounts of Nb2O5, Nd2O3, Zn0.8Mg0.2TiO3 (ZMT), and magnesium lithium borosilicate (MLBS) were the four investigated factors with respect to the dielectric constant at room temperature (ɛ) and temperature-capacitance characteristics (TCC) at 125°C (TCC125°C) and TCC150°C. Experiments were designed according to the uniform design with four factors for each at twelve levels. For each response, the second-order polynomial equations were obtained by multiple regression analysis. As a result, the empirical mathematical models could successfully predict the experimental results with very good accuracy. Finally, based on optimization strategy, we succeeded in producing lead-free X8R ceramics with various dielectric constants ranging from 1500 to 3300, which is promising for developing X8R MLCC with different capacities.
Morotti, Stefano; Grandi, Eleonora
2017-01-01
Population-based computational approaches have been developed in recent years and helped to gain insight into arrhythmia mechanisms, and intra- and inter-patient variability (e.g., in drug responses). Here, we illustrate the use of multivariable logistic regression to analyze the factors that enhance or reduce the susceptibility to cellular arrhythmogenic events. As an example, we generate 1000 model variants by randomly modifying ionic conductances and maximal rates of ion transports in our atrial myocyte model and simulate an arrhythmia-provoking protocol that enhances early afterdepolarization (EAD) proclivity. We then treat EAD occurrence as a categorical, yes or no variable, and perform logistic regression to relate perturbations in model parameters to the presence/absence of EADs. We find that EAD formation is sensitive to the conductance of the voltage-gated Na(+), the acetylcholine-sensitive and ultra-rapid K(+) channels, and the Na(+)/Ca(2+) exchange current, which matches our mechanistic understanding of the process and preliminary sensitivity analysis. The described technique: •allows investigating the factors underlying dichotomous outcomes, and is therefore a useful tool improve our understanding of arrhythmic risk;•is valid for analyzing both deterministic and stochastic models, and various phenomena (e.g., delayed afterdepolarizations and Ca(2+) sparks);•is computationally more efficient than one-at-a-time parameter sensitivity analysis.
The analysis of internet addiction scale using multivariate adaptive regression splines.
Kayri, M
2010-01-01
Determining real effects on internet dependency is too crucial with unbiased and robust statistical method. MARS is a new non-parametric method in use in the literature for parameter estimations of cause and effect based research. MARS can both obtain legible model curves and make unbiased parametric predictions. In order to examine the performance of MARS, MARS findings will be compared to Classification and Regression Tree (C&RT) findings, which are considered in the literature to be efficient in revealing correlations between variables. The data set for the study is taken from "The Internet Addiction Scale" (IAS), which attempts to reveal addiction levels of individuals. The population of the study consists of 754 secondary school students (301 female, 443 male students with 10 missing data). MARS 2.0 trial version is used for analysis by MARS method and C&RT analysis was done by SPSS. MARS obtained six base functions of the model. As a common result of these six functions, regression equation of the model was found. Over the predicted variable, MARS showed that the predictors of daily Internet-use time on average, the purpose of Internet-use, grade of students and occupations of mothers had a significant effect (P< 0.05). In this comparative study, MARS obtained different findings from C&RT in dependency level prediction. The fact that MARS revealed extent to which the variable, which was considered significant, changes the character of the model was observed in this study.
Regression analysis of mixed panel count data with dependent terminal events.
Yu, Guanglei; Zhu, Liang; Li, Yang; Sun, Jianguo; Robison, Leslie L
2017-05-10
Event history studies are commonly conducted in many fields, and a great deal of literature has been established for the analysis of the two types of data commonly arising from these studies: recurrent event data and panel count data. The former arises if all study subjects are followed continuously, while the latter means that each study subject is observed only at discrete time points. In reality, a third type of data, a mixture of the two types of the data earlier, may occur and furthermore, as with the first two types of the data, there may exist a dependent terminal event, which may preclude the occurrences of recurrent events of interest. This paper discusses regression analysis of mixed recurrent event and panel count data in the presence of a terminal event and an estimating equation-based approach is proposed for estimation of regression parameters of interest. In addition, the asymptotic properties of the proposed estimator are established, and a simulation study conducted to assess the finite-sample performance of the proposed method suggests that it works well in practical situations. Finally, the methodology is applied to a childhood cancer study that motivated this study. Copyright © 2017 John Wiley & Sons, Ltd.
Regression-based adaptive sparse polynomial dimensional decomposition for sensitivity analysis
NASA Astrophysics Data System (ADS)
Tang, Kunkun; Congedo, Pietro; Abgrall, Remi
2014-11-01
Polynomial dimensional decomposition (PDD) is employed in this work for global sensitivity analysis and uncertainty quantification of stochastic systems subject to a large number of random input variables. Due to the intimate structure between PDD and Analysis-of-Variance, PDD is able to provide simpler and more direct evaluation of the Sobol' sensitivity indices, when compared to polynomial chaos (PC). Unfortunately, the number of PDD terms grows exponentially with respect to the size of the input random vector, which makes the computational cost of the standard method unaffordable for real engineering applications. In order to address this problem of curse of dimensionality, this work proposes a variance-based adaptive strategy aiming to build a cheap meta-model by sparse-PDD with PDD coefficients computed by regression. During this adaptive procedure, the model representation by PDD only contains few terms, so that the cost to resolve repeatedly the linear system of the least-square regression problem is negligible. The size of the final sparse-PDD representation is much smaller than the full PDD, since only significant terms are eventually retained. Consequently, a much less number of calls to the deterministic model is required to compute the final PDD coefficients.
NASA Astrophysics Data System (ADS)
Buck, J. A.; Underhill, P. R.; Morelli, J.; Krause, T. W.
2016-02-01
Nuclear steam generators (SGs) are a critical component for ensuring safe and efficient operation of a reactor. Life management strategies are implemented in which SG tubes are regularly inspected by conventional eddy current testing (ECT) and ultrasonic testing (UT) technologies to size flaws, and safe operating life of SGs is predicted based on growth models. ECT, the more commonly used technique, due to the rapidity with which full SG tube wall inspection can be performed, is challenged when inspecting ferromagnetic support structure materials in the presence of magnetite sludge and multiple overlapping degradation modes. In this work, an emerging inspection method, pulsed eddy current (PEC), is being investigated to address some of these particular inspection conditions. Time-domain signals were collected by an 8 coil array PEC probe in which ferromagnetic drilled support hole diameter, depth of rectangular tube frets and 2D tube off-centering were varied. Data sets were analyzed with a modified principal components analysis (MPCA) to extract dominant signal features. Multiple linear regression models were applied to MPCA scores to size hole diameter as well as size rectangular outer diameter tube frets. Models were improved through exploratory factor analysis, which was applied to MPCA scores to refine selection for regression models inputs by removing nonessential information.
Application of Regression-Discontinuity Analysis in Pharmaceutical Health Services Research
Zuckerman, Ilene H; Lee, Euni; Wutoh, Anthony K; Xue, Zhenyi; Stuart, Bruce
2006-01-01
Objective To demonstrate how a relatively underused design, regression-discontinuity (RD), can provide robust estimates of intervention effects when stronger designs are impossible to implement. Data Sources/Study Setting Administrative claims from a Mid-Atlantic state Medicaid program were used to evaluate the effectiveness of an educational drug utilization review intervention. Study Design Quasi-experimental design. Data Collection/Extraction Methods A drug utilization review study was conducted to evaluate a letter intervention to physicians treating Medicaid children with potentially excessive use of short-acting β2-agonist inhalers (SAB). The outcome measure is change in seasonally-adjusted SAB use 5 months pre- and postintervention. To determine if the intervention reduced monthly SAB utilization, results from an RD analysis are compared to findings from a pretest–posttest design using repeated-measure ANOVA. Principal Findings Both analyses indicated that the intervention significantly reduced SAB use among the high users. Average monthly SAB use declined by 0.9 canisters per month (p<.001) according to the repeated-measure ANOVA and by 0.2 canisters per month (p<.001) from RD analysis. Conclusions Regression-discontinuity design is a useful quasi-experimental methodology that has significant advantages in internal validity compared to other pre–post designs when assessing interventions in which subjects' assignment is based on cutoff scores for a critical variable. PMID:16584464
Error analysis of leaf area estimates made from allometric regression models
NASA Technical Reports Server (NTRS)
Feiveson, A. H.; Chhikara, R. S.
1986-01-01
Biological net productivity, measured in terms of the change in biomass with time, affects global productivity and the quality of life through biochemical and hydrological cycles and by its effect on the overall energy balance. Estimating leaf area for large ecosystems is one of the more important means of monitoring this productivity. For a particular forest plot, the leaf area is often estimated by a two-stage process. In the first stage, known as dimension analysis, a small number of trees are felled so that their areas can be measured as accurately as possible. These leaf areas are then related to non-destructive, easily-measured features such as bole diameter and tree height, by using a regression model. In the second stage, the non-destructive features are measured for all or for a sample of trees in the plots and then used as input into the regression model to estimate the total leaf area. Because both stages of the estimation process are subject to error, it is difficult to evaluate the accuracy of the final plot leaf area estimates. This paper illustrates how a complete error analysis can be made, using an example from a study made on aspen trees in northern Minnesota. The study was a joint effort by NASA and the University of California at Santa Barbara known as COVER (Characterization of Vegetation with Remote Sensing).
Regression analysis of mixed recurrent-event and panel-count data
Zhu, Liang; Tong, Xinwei; Sun, Jianguo; Chen, Manhua; Srivastava, Deo Kumar; Leisenring, Wendy; Robison, Leslie L.
2014-01-01
In event history studies concerning recurrent events, two types of data have been extensively discussed. One is recurrent-event data (Cook and Lawless, 2007. The Analysis of Recurrent Event Data. New York: Springer), and the other is panel-count data (Zhao and others, 2010. Nonparametric inference based on panel-count data. Test 20, 1–42). In the former case, all study subjects are monitored continuously; thus, complete information is available for the underlying recurrent-event processes of interest. In the latter case, study subjects are monitored periodically; thus, only incomplete information is available for the processes of interest. In reality, however, a third type of data could occur in which some study subjects are monitored continuously, but others are monitored periodically. When this occurs, we have mixed recurrent-event and panel-count data. This paper discusses regression analysis of such mixed data and presents two estimation procedures for the problem. One is a maximum likelihood estimation procedure, and the other is an estimating equation procedure. The asymptotic properties of both resulting estimators of regression parameters are established. Also, the methods are applied to a set of mixed recurrent-event and panel-count data that arose from a Childhood Cancer Survivor Study and motivated this investigation. PMID:24648408
Poisson regression analysis of mortality among male workers at a thorium-processing plant
Liu, Zhiyuan; Lee, Tze-San; Kotek, T.J.
1991-12-31
Analyses of mortality among a cohort of 3119 male workers employed between 1915 and 1973 at a thorium-processing plant were updated to the end of 1982. Of the whole group, 761 men were deceased and 2161 men were still alive, while 197 men were lost to follow-up. A total of 250 deaths was added to the 511 deaths observed in the previous study. The standardized mortality ratio (SMR) for all causes of death was 1.12 with 95% confidence interval (CI) of 1.05-1.21. The SMRs were also significantly increased for all malignant neoplasms (SMR = 1.23, 95% CI = 1.04-1.43) and lung cancer (SMR = 1.36, 95% CI = 1.02-1.78). Poisson regression analysis was employed to evaluate the joint effects of job classification, duration of employment, time since first employment, age and year at first employment on mortality of all malignant neoplasms and lung cancer. A comparison of internal and external analyses with the Poisson regression model was also conducted and showed no obvious difference in fitting the data on lung cancer mortality of the thorium workers. The results of the multivariate analysis showed that there was no significant effect of all the study factors on mortality due to all malignant neoplasms and lung cancer. Therefore, further study is needed for the former thorium workers.
NASA Astrophysics Data System (ADS)
Grégoire, G.
2014-12-01
This chapter deals with the multiple linear regression. That is we investigate the situation where the mean of a variable depends linearly on a set of covariables. The noise is supposed to be gaussian. We develop the least squared method to get the parameter estimators and estimates of their precisions. This leads to design confidence intervals, prediction intervals, global tests, individual tests and more generally tests of submodels defined by linear constraints. Methods for model's choice and variables selection, measures of the quality of the fit, residuals study, diagnostic methods are presented. Finally identification of departures from the model's assumptions and the way to deal with these problems are addressed. A real data set is used to illustrate the methodology with software R. Note that this chapter is intended to serve as a guide for other regression methods, like logistic regression or AFT models and Cox regression.
NASA Astrophysics Data System (ADS)
Junek, W. N.; Jones, W. L.; Woods, M. T.
2011-12-01
An automated event tree analysis system for estimating the probability of short term volcanic activity is presented. The algorithm is driven by a suite of empirical statistical models that are derived through logistic regression. Each model is constructed from a multidisciplinary dataset that was assembled from a collection of historic volcanic unrest episodes. The dataset consists of monitoring measurements (e.g. InSAR, seismic), source modeling results, and historic eruption activity. This provides a simple mechanism for simultaneously accounting for the geophysical changes occurring within the volcano and the historic behavior of analog volcanoes. The algorithm is extensible and can be easily recalibrated to include new or additional monitoring, modeling, or historic information. Standard cross validation techniques are employed to optimize its forecasting capabilities. Analysis results from several recent volcanic unrest episodes are presented.
NASA Astrophysics Data System (ADS)
Păniţă, Ovidiu
2015-09-01
In the years 2012-2014 on Banu-Maracine DRS there were tested an assortment of 25 isogenic lines of wheat (Triticum aestivum ssp.vulgare), the analyzed characters being the number of seeds/spike, seeds weight/spike (g), no. of spikes/m2, weight of a thousand seeds (WTS) (g) and no. of emerged plants/m2. Based on recorded data and statistical processing of those, they were identified a numbers of links between these characters. Also available regression models were identified between some of the studied characters. Based on component analysis, no. of seeds/spike and seeds weight/spike are components that influence in excess of 88% variance analysis, a total of seven genotypes with positive scores for both factors.
Probabilistic partial least squares regression for quantitative analysis of Raman spectra.
Li, Shuo; Nyagilo, James O; Dave, Digant P; Wang, Wei; Zhang, Baoju; Gao, Jean
2015-01-01
With the latest development of Surface-Enhanced Raman Scattering (SERS) technique, quantitative analysis of Raman spectra has shown the potential and promising trend of development in vivo molecular imaging. Partial Least Squares Regression (PLSR) is state-of-the-art method. But it only relies on training samples, which makes it difficult to incorporate complex domain knowledge. Based on probabilistic Principal Component Analysis (PCA) and probabilistic curve fitting idea, we propose a probabilistic PLSR (PPLSR) model and an Estimation Maximisation (EM) algorithm for estimating parameters. This model explains PLSR from a probabilistic viewpoint, describes its essential meaning and provides a foundation to develop future Bayesian nonparametrics models. Two real Raman spectra datasets were used to evaluate this model, and experimental results show its effectiveness.
Regression analysis of overdispersed correlated count data with subject specific covariates.
Solis-Trapala, I L; Farewell, V T
2005-08-30
A robust likelihood approach for the analysis of overdispersed correlated count data that takes into account cluster varying covariates is proposed. We emphasise two characteristics of the proposed method: That the correlation structure satisfies the constraints on the second moments and that the estimation of the correlation structure guarantees consistent estimates of the regression coefficients. In addition we extend the mean specification to include within- and between-cluster effects. The method is illustrated through the analysis of data from two studies. In the first study, cross-sectional count data from a randomised controlled trial are analysed to evaluate the efficacy of a communication skills training programme. The second study involves longitudinal count data which represent counts of damaged hand joints in patients with psoriatic arthritis. Motivated by this study, we generalize our model to accommodate for a subpopulation of patients who are not susceptible to the development of damaged hand joints. Copyright 2005 John Wiley & Sons, Ltd
Modelling and analysis of turbulent datasets using Auto Regressive Moving Average processes
Faranda, Davide Dubrulle, Bérengère; Daviaud, François; Pons, Flavio Maria Emanuele; Saint-Michel, Brice; Herbert, Éric; Cortet, Pierre-Philippe
2014-10-15
We introduce a novel way to extract information from turbulent datasets by applying an Auto Regressive Moving Average (ARMA) statistical analysis. Such analysis goes well beyond the analysis of the mean flow and of the fluctuations and links the behavior of the recorded time series to a discrete version of a stochastic differential equation which is able to describe the correlation structure in the dataset. We introduce a new index Υ that measures the difference between the resulting analysis and the Obukhov model of turbulence, the simplest stochastic model reproducing both Richardson law and the Kolmogorov spectrum. We test the method on datasets measured in a von Kármán swirling flow experiment. We found that the ARMA analysis is well correlated with spatial structures of the flow, and can discriminate between two different flows with comparable mean velocities, obtained by changing the forcing. Moreover, we show that the Υ is highest in regions where shear layer vortices are present, thereby establishing a link between deviations from the Kolmogorov model and coherent structures. These deviations are consistent with the ones observed by computing the Hurst exponents for the same time series. We show that some salient features of the analysis are preserved when considering global instead of local observables. Finally, we analyze flow configurations with multistability features where the ARMA technique is efficient in discriminating different stability branches of the system.
Analysis of changes in extreme temperature and precipitation using quantile regression
NASA Astrophysics Data System (ADS)
Lee, Kyoungmi; Baek, Hee-Jeong; Cho, ChunHo
2013-04-01
One of the important research areas in climatology is to identify whether the long-period tendencies of change in meteorological variables appear. In the past, the analysis has been limited by the estimation of long-period trends for annual or seasonal average values on meteorological variables. However, recently, the interest in the trends regarding the whole range of values for meteorological variables, including the extreme ones, has arisen. The quantile regression is the regression analysis method for estimating the regression slopes for the values of any quantile from 0 to 1 of dependent variable distributions. This method provides a more complete picture for the conditional distribution of the dependent variable given the independent variable when both lower and upper or all quantiles are of interest. This study examines the changes in regional extreme temperature and precipitation in South Korea using quantile regression, which is applied to analyze trends, not only in the mean but in all parts of the data distribution. The results show considerable diversity across space and quantile level in South Korea. For daily temperatures in winter, the slopes in lower quantiles generally have a more distinct increase trend compared to the upper quantiles. The time series for daily minimum temperature during the winter season only shows a significant increasing trend in the lower quantile. In case of summer, most sites show an increase trend in both lower and upper quantiles for daily minimum temperature, while there are a number of sites with a decrease trend for daily maximum temperature. It was also found that the increase trend of extreme low temperature in large urban areas (0.80°C/decade) is much larger than in rural areas (0.54°C/decade) due to the effects of urbanization. Extreme climate events can have greater negative impacts on society, economy and natural environments than changes in climate means. The fast growth of population and industrialization in
Rodríguez-Barranco, Miguel; Tobías, Aurelio; Redondo, Daniel; Molina-Portillo, Elena; Sánchez, María José
2017-03-17
Meta-analysis is very useful to summarize the effect of a treatment or a risk factor for a given disease. Often studies report results based on log-transformed variables in order to achieve the principal assumptions of a linear regression model. If this is the case for some, but not all studies, the effects need to be homogenized. We derived a set of formulae to transform absolute changes into relative ones, and vice versa, to allow including all results in a meta-analysis. We applied our procedure to all possible combinations of log-transformed independent or dependent variables. We also evaluated it in a simulation based on two variables either normally or asymmetrically distributed. In all the scenarios, and based on different change criteria, the effect size estimated by the derived set of formulae was equivalent to the real effect size. To avoid biased estimates of the effect, this procedure should be used with caution in the case of independent variables with asymmetric distributions that significantly differ from the normal distribution. We illustrate an application of this procedure by an application to a meta-analysis on the potential effects on neurodevelopment in children exposed to arsenic and manganese. The procedure proposed has been shown to be valid and capable of expressing the effect size of a linear regression model based on different change criteria in the variables. Homogenizing the results from different studies beforehand allows them to be combined in a meta-analysis, independently of whether the transformations had been performed on the dependent and/or independent variables.
Evaluation of Visual Field Progression in Glaucoma: Quasar Regression Program and Event Analysis.
Díaz-Alemán, Valentín T; González-Hernández, Marta; Perera-Sanz, Daniel; Armas-Domínguez, Karintia
2016-01-01
To determine the sensitivity, specificity and agreement between the Quasar program, glaucoma progression analysis (GPA II) event analysis and expert opinion in the detection of glaucomatous progression. The Quasar program is based on linear regression analysis of both mean defect (MD) and pattern standard deviation (PSD). Each series of visual fields was evaluated by three methods; Quasar, GPA II and four experts. The sensitivity, specificity and agreement (kappa) for each method was calculated, using expert opinion as the reference standard. The study included 439 SITA Standard visual fields of 56 eyes of 42 patients, with a mean of 7.8 ± 0.8 visual fields per eye. When suspected cases of progression were considered stable, sensitivity and specificity of Quasar, GPA II and the experts were 86.6% and 70.7%, 26.6% and 95.1%, and 86.6% and 92.6% respectively. When suspected cases of progression were considered as progressing, sensitivity and specificity of Quasar, GPA II and the experts were 79.1% and 81.2%, 45.8% and 90.6%, and 85.4% and 90.6% respectively. The agreement between Quasar and GPA II when suspected cases were considered stable or progressing was 0.03 and 0.28 respectively. The degree of agreement between Quasar and the experts when suspected cases were considered stable or progressing was 0.472 and 0.507. The degree of agreement between GPA II and the experts when suspected cases were considered stable or progressing was 0.262 and 0.342. The combination of MD and PSD regression analysis in the Quasar program showed better agreement with the experts and higher sensitivity than GPA II.
Bareth, Bettina; Dennerlein, Sven; Mick, David U; Nikolov, Miroslav; Urlaub, Henning; Rehling, Peter
2013-10-01
Cox1, the core subunit of the cytochrome c oxidase, receives two heme a cofactors during assembly of the 13-subunit enzyme complex. However, at which step of the assembly process and how heme is inserted into Cox1 have remained an enigma. Shy1, the yeast SURF1 homolog, has been implicated in heme transfer to Cox1, whereas the heme a synthase, Cox15, catalyzes the final step of heme a synthesis. Here we performed a comprehensive analysis of cytochrome c oxidase assembly intermediates containing Shy1. Our analyses suggest that Cox15 displays a role in cytochrome c oxidase assembly, which is independent of its functions as the heme a synthase. Cox15 forms protein complexes with Shy1 and also associates with Cox1-containing complexes independently of Shy1 function. These findings indicate that Shy1 does not serve as a mobile heme carrier between the heme a synthase and maturing Cox1 but rather cooperates with Cox15 for heme transfer and insertion in early assembly intermediates of cytochrome c oxidase.
Askelöf, P; Korsfeldt, M; Mannervik, B
1976-10-01
Knowledge of the error structure of a given set of experimental data is a necessary prerequisite for incisive analysis and for discrimination between alternative mathematical models of the data set. A reaction system consisting of glutathione S-transferase A (glutathione S-aryltransferase), glutathione, and 3,4-dichloro-1-nitrobenzene was investigated under steady-state conditions. It was found that the experimental error increased with initial velocity, v, and that the variance (estimated by replicates) could be described by a polynomial in v Var (v) = K0 + K1 - v + K2 - v2 or by a power function Var (v) = K0 + K1 - vK2. These equations were good approximations irrespective of whether different v values were generated by changing substrate or enzyme concentrations. The selection of these models was based mainly on experiments involving varying enzyme concentration, which, unlike v, is not considered a stochastic variable. Different models of the variance, expressed as functions of enzyme concentration, were examined by regression analysis, and the models could then be transformed to functions in which velocity is substituted for enzyme concentration owing to the proportionality between these variables. Thus, neither the absolute nor the relative error was independent of velocity, a result previously obtained for glutathione reductase in this laboratory [BioSystems 7, 101-119 (1975)]. If the experimental errors or velocities were standardized by division with their corresponding mean velocity value they showed a normal (Gaussian) distribution provided that the coefficient of variation was approximately constant for the data considered. Furthermore, it was established that the errors in the independent variables (enzyme and substrate concentrations) were small in comparison with the error in the velocity determinations. For weighting in regression analysis the inverted value of the local variance in each experimental point should be used. It was found that the
P300 Amplitude in Alzheimer's Disease: A Meta-Analysis and Meta-Regression.
Hedges, Dawson; Janis, Rebecca; Mickelson, Stephen; Keith, Cierra; Bennett, David; Brown, Bruce L
2016-01-01
Alzheimer's disease accounts for 60% of all dementia. Numerous biomarkers have been developed that can help in making an early diagnosis. The P300 is an event-related potential that may be abnormal in Alzheimer's disease. Given the possible association between P300 amplitude and Alzheimer's disease and the need for biomarkers in early Alzheimer's disease, the main purpose of this meta-analysis and meta-regression was to characterize P300 amplitude in probable Alzheimer's disease compared to healthy controls. Using online search engines, we identified peer-reviewed articles containing amplitude measures for the P300 in response to a visual or auditory oddball stimulus in subjects with Alzheimer's disease and in a healthy control group and pooled effect sizes for differences in P300 amplitude between Alzheimer's disease and control groups to obtain summary effect sizes. We also used meta-regression to determine whether age, sex, educational attainment, or dementia severity affected the association between P300 amplitude and Alzheimer's disease. Twenty articles containing a total of 646 subjects met inclusion and exclusion criteria. The overall effect size from all electrode locations was 1.079 (95% confidence interval=0.745-1.412, P<.001). The pooled effect sizes for the Cz, Fz, and Pz locations were 1.226 (P<.001), 0.724 (P=.0007), and 1.430 (P<.001), respectively. Meta-regression showed an association between amplitude and educational attainment, but no association between amplitude and age, sex, and dementia severity. In conclusion, P300 amplitude is smaller in subjects with Alzheimer's disease than in healthy controls.
Fu, Yuan-Yuan; Wang, Ji-Hua; Yang, Gui-Jun; Song, Xiao-Yu; Xu, Xin-Gang; Feng, Hai-Kuan
2013-05-01
The major limitation of using existing vegetation indices for crop biomass estimation is that it approaches a saturation level asymptotically for a certain range of biomass. In order to resolve this problem, band depth analysis and partial least square regression (PLSR) were combined to establish winter wheat biomass estimation model in the present study. The models based on the combination of band depth analysis and PLSR were compared with the models based on common vegetation indexes from the point of view of estimation accuracy, subsequently. Band depth analysis was conducted in the visible spectral domain (550-750 nm). Band depth, band depth ratio (BDR), normalized band depth index, and band depth normalized to area were utilized to represent band depth information. Among the calibrated estimation models, the models based on the combination of band depth analysis and PLSR reached higher accuracy than those based on the vegetation indices. Among them, the combination of BDR and PLSR got the highest accuracy (R2 = 0.792, RMSE = 0.164 kg x m(-2)). The results indicated that the combination of band depth analysis and PLSR could well overcome the saturation problem and improve the biomass estimation accuracy when winter wheat biomass is large.
Liu, Zhi-yu; Zhong, Meng; Hai, Yan; Du, Qi-yun; Wang, Ai-hua; Xie, Dong-hua
2012-11-01
To understand the situation of depression and its related influencing factors among medical staff in Hunan province. Data were collected through random sampling with multi-stage stratified cluster. Wilcoxon rank sum test, Kruskal-Wallis H test and Ordinal regression analysis were used for data analysis by SPSS 17.0 software. This survey was including 16,000 medical personnel with 14, 988 valid questionnaires and the effective rate was 93.68%. from the single factor analysis showed that factors as: level of the hospital grading, gender, education background, age, occupation, title, departments, the number of continue education, income, working overtime every week, the frequency of night work, the number of patients treated in the emergency room etc., had statistical significances (P < 0.05). Data from ordinal regression showed that the probabilities related to depression that clinicians and nurses suffering from were 1.58 times more than the pharmacists (OR = 1.58, 95%CI: 1.30 - 1.92). The probability among those whose income was less than 2000 Yuan/month was 2.19 times of the ones whose earned more than 3000 Yuan/month (OR = 2.19, 95%CI: 2.05 - 2.35). The higher the numbers of days with working overtime every week, the frequencies of night work, and the numbers of patients being treated at the emergency room, with more probabilities of the people with depression seen in our study. Depression seemed to be common among doctors and nurses. We suggested that the government need to increase the monthly income and to reduce the workload and intensity, lessen the overworking time, etc.
A cautionary note on the use of EESC-based regression analysis for ozone trend studies
NASA Astrophysics Data System (ADS)
Kuttippurath, J.; Bodeker, G. E.; Roscoe, H. K.; Nair, P. J.
2015-01-01
Equivalent effective stratospheric chlorine (EESC) construct of ozone regression models attributes ozone changes to EESC changes using a single value of the sensitivity of ozone to EESC over the whole period. Using space-based total column ozone (TCO) measurements, and a synthetic TCO time series constructed such that EESC does not fall below its late 1990s maximum, we demonstrate that the EESC-based estimates of ozone changes in the polar regions (70-90°) after 2000 may, falsely, suggest an EESC-driven increase in ozone over this period. An EESC-based regression of our synthetic "failed Montreal Protocol with constant EESC" time series suggests a positive TCO trend that is statistically significantly different from zero over 2001-2012 when, in fact, no recovery has taken place. Our analysis demonstrates that caution needs to be exercised when using explanatory variables, with a single fit coefficient, fitted to the entire data record, to interpret changes in only part of the record.
Combining regression analysis and air quality modelling to predict benzene concentration levels
NASA Astrophysics Data System (ADS)
Vlachokostas, Ch.; Achillas, Ch.; Chourdakis, E.; Moussiopoulos, N.
2011-05-01
State of the art epidemiological research has found consistent associations between traffic-related air pollution and various outcomes, such as respiratory symptoms and premature mortality. However, many urban areas are characterised by the absence of the necessary monitoring infrastructure, especially for benzene (C 6H 6), which is a known human carcinogen. The use of environmental statistics combined with air quality modelling can be of vital importance in order to assess air quality levels of traffic-related pollutants in an urban area in the case where there are no available measurements. This paper aims at developing and presenting a reliable approach, in order to forecast C 6H 6 levels in urban environments, demonstrated for Thessaloniki, Greece. Multiple stepwise regression analysis is used and a strong statistical relationship is detected between C 6H 6 and CO. The adopted regression model is validated in order to depict its applicability and representativeness. The presented results demonstrate that the adopted approach is capable of capturing C 6H 6 concentration trends and should be considered as complementary to air quality monitoring.
Árnadóttir, Í.; Gíslason, M. K.; Carraro, U.
2016-01-01
Muscle degeneration has been consistently identified as an independent risk factor for high mortality in both aging populations and individuals suffering from neuromuscular pathology or injury. While there is much extant literature on its quantification and correlation to comorbidities, a quantitative gold standard for analyses in this regard remains undefined. Herein, we hypothesize that rigorously quantifying entire radiodensitometric distributions elicits more muscle quality information than average values reported in extant methods. This study reports the development and utility of a nonlinear trimodal regression analysis method utilized on radiodensitometric distributions of upper leg muscles from CT scans of a healthy young adult, a healthy elderly subject, and a spinal cord injury patient. The method was then employed with a THA cohort to assess pre- and postsurgical differences in their healthy and operative legs. Results from the initial representative models elicited high degrees of correlation to HU distributions, and regression parameters highlighted physiologically evident differences between subjects. Furthermore, results from the THA cohort echoed physiological justification and indicated significant improvements in muscle quality in both legs following surgery. Altogether, these results highlight the utility of novel parameters from entire HU distributions that could provide insight into the optimal quantification of muscle degeneration. PMID:28115982
Classification of Effective Soil Depth by Using Multinomial Logistic Regression Analysis
NASA Astrophysics Data System (ADS)
Chang, C. H.; Chan, H. C.; Chen, B. A.
2016-12-01
Classification of effective soil depth is a task of determining the slopeland utilizable limitation in Taiwan. The "Slopeland Conservation and Utilization Act" categorizes the slopeland into agriculture and husbandry land, land suitable for forestry and land for enhanced conservation according to the factors including average slope, effective soil depth, soil erosion and parental rock. However, sit investigation of the effective soil depth requires a cost-effective field work. This research aimed to classify the effective soil depth by using multinomial logistic regression with the environmental factors. The Wen-Shui Watershed located at the central Taiwan was selected as the study areas. The analysis of multinomial logistic regression is performed by the assistance of a Geographic Information Systems (GIS). The effective soil depth was categorized into four levels including deeper, deep, shallow and shallower. The environmental factors of slope, aspect, digital elevation model (DEM), curvature and normalized difference vegetation index (NDVI) were selected for classifying the soil depth. An Error Matrix was then used to assess the model accuracy. The results showed an overall accuracy of 75%. At the end, a map of effective soil depth was produced to help planners and decision makers in determining the slopeland utilizable limitation in the study areas.
Yu, Rongqin; Geddes, John R; Fazel, Seena
2012-10-01
The risk of antisocial outcomes in individuals with personality disorder (PD) remains uncertain. The authors synthesize the current evidence on the risks of antisocial behavior, violence, and repeat offending in PD, and they explore sources of heterogeneity in risk estimates through a systematic review and meta-regression analysis of observational studies comparing antisocial outcomes in personality disordered individuals with controls groups. Fourteen studies examined risk of antisocial and violent behavior in 10,007 individuals with PD, compared with over 12 million general population controls. There was a substantially increased risk of violent outcomes in studies with all PDs (random-effects pooled odds ratio [OR] = 3.0, 95% CI = 2.6 to 3.5). Meta-regression revealed that antisocial PD and gender were associated with higher risks (p = .01 and .07, respectively). The odds of all antisocial outcomes were also elevated. Twenty-five studies reported the risk of repeat offending in PD compared with other offenders. The risk of a repeat offense was also increased (fixed-effects pooled OR = 2.4, 95% CI = 2.2 to 2.7) in offenders with PD. The authors conclude that although PD is associated with antisocial outcomes and repeat offending, the risk appears to differ by PD category, gender, and whether individuals are offenders or not.
An innovative land use regression model incorporating meteorology for exposure analysis.
Su, Jason G; Brauer, Michael; Ainslie, Bruce; Steyn, Douw; Larson, Timothy; Buzzelli, Michael
2008-02-15
The advent of spatial analysis and geographic information systems (GIS) has led to studies of chronic exposure and health effects based on the rationale that intra-urban variations in ambient air pollution concentrations are as great as inter-urban differences. Such studies typically rely on local spatial covariates (e.g., traffic, land use type) derived from circular areas (buffers) to predict concentrations/exposures at receptor sites, as a means of averaging the annual net effect of meteorological influences (i.e., wind speed, wind direction and insolation). This is the approach taken in the now popular land use regression (LUR) method. However spatial studies of chronic exposures and temporal studies of acute exposures have not been adequately integrated. This paper presents an innovative LUR method implemented in a GIS environment that reflects both temporal and spatial variability and considers the role of meteorology. The new source area LUR integrates wind speed, wind direction and cloud cover/insolation to estimate hourly nitric oxide (NO) and nitrogen dioxide (NO(2)) concentrations from land use types (i.e., road network, commercial land use) and these concentrations are then used as covariates to regress against NO and NO(2) measurements at various receptor sites across the Vancouver region and compared directly with estimates from a regular LUR. The results show that, when variability in seasonal concentration measurements is present, the source area LUR or SA-LUR model is a better option for concentration estimation.
Improved Regression Analysis of Temperature-Dependent Strain-Gage Balance Calibration Data
NASA Technical Reports Server (NTRS)
Ulbrich, N.
2015-01-01
An improved approach is discussed that may be used to directly include first and second order temperature effects in the load prediction algorithm of a wind tunnel strain-gage balance. The improved approach was designed for the Iterative Method that fits strain-gage outputs as a function of calibration loads and uses a load iteration scheme during the wind tunnel test to predict loads from measured gage outputs. The improved approach assumes that the strain-gage balance is at a constant uniform temperature when it is calibrated and used. First, the method introduces a new independent variable for the regression analysis of the balance calibration data. The new variable is designed as the difference between the uniform temperature of the balance and a global reference temperature. This reference temperature should be the primary calibration temperature of the balance so that, if needed, a tare load iteration can be performed. Then, two temperature{dependent terms are included in the regression models of the gage outputs. They are the temperature difference itself and the square of the temperature difference. Simulated temperature{dependent data obtained from Triumph Aerospace's 2013 calibration of NASA's ARC-30K five component semi{span balance is used to illustrate the application of the improved approach.
Imai, Chisato; Hashizume, Masahiro
2015-03-01
Time series analysis is suitable for investigations of relatively direct and short-term effects of exposures on outcomes. In environmental epidemiology studies, this method has been one of the standard approaches to assess impacts of environmental factors on acute non-infectious diseases (e.g. cardiovascular deaths), with conventionally generalized linear or additive models (GLM and GAM). However, the same analysis practices are often observed with infectious diseases despite of the substantial differences from non-infectious diseases that may result in analytical challenges. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, systematic review was conducted to elucidate important issues in assessing the associations between environmental factors and infectious diseases using time series analysis with GLM and GAM. Published studies on the associations between weather factors and malaria, cholera, dengue, and influenza were targeted. Our review raised issues regarding the estimation of susceptible population and exposure lag times, the adequacy of seasonal adjustments, the presence of strong autocorrelations, and the lack of a smaller observation time unit of outcomes (i.e. daily data). These concerns may be attributable to features specific to infectious diseases, such as transmission among individuals and complicated causal mechanisms. The consequence of not taking adequate measures to address these issues is distortion of the appropriate risk quantifications of exposures factors. Future studies should pay careful attention to details and examine alternative models or methods that improve studies using time series regression analysis for environmental determinants of infectious diseases.
Factors predicting the failure of Bernese periacetabular osteotomy: a meta-regression analysis.
Sambandam, Senthil Nathan; Hull, Jason; Jiranek, William A
2009-12-01
There is no clear evidence regarding the outcome of Bernese periacetabular osteotomy (PAO) in different patient populations. We performed systematic meta-regression analysis of 23 eligible studies. There were 1,113 patients of which 61 patients had total hip arthroplasty (THA) (endpoint) as a result of failed Bernese PAO. Univariate analysis revealed significant correlation between THA and presence of grade 2/grade 3 arthritis, Merle de'Aubigne score (MDS), Harris hip score and Tonnis angle, change in lateral centre edge (LCE) angle, late proximal femoral osteotomies, and heterotrophic ossification (HO) resection. Multivariate analysis showed that the odds of having THA increases with grade 2/grade 3 osteoarthritis (3.36 times), joint penetration (3.12 times), low preoperative MDS (1.59 times), late PFO (1.59 times), presence of preoperative subluxation (1.22 times), previous hip operations (1.14 times), and concomitant PFO (1.09 times). In the absence of randomised controlled studies, the findings of this analysis can help the surgeon to make treatment decisions.
Factors predicting the failure of Bernese periacetabular osteotomy: a meta-regression analysis
Hull, Jason; Jiranek, William A.
2008-01-01
There is no clear evidence regarding the outcome of Bernese periacetabular osteotomy (PAO) in different patient populations. We performed systematic meta-regression analysis of 23 eligible studies. There were 1,113 patients of which 61 patients had total hip arthroplasty (THA) (endpoint) as a result of failed Bernese PAO. Univariate analysis revealed significant correlation between THA and presence of grade 2/grade 3 arthritis, Merle de’Aubigne score (MDS), Harris hip score and Tonnis angle, change in lateral centre edge (LCE) angle, late proximal femoral osteotomies, and heterotrophic ossification (HO) resection. Multivariate analysis showed that the odds of having THA increases with grade 2/grade 3 osteoarthritis (3.36 times), joint penetration (3.12 times), low preoperative MDS (1.59 times), late PFO (1.59 times), presence of preoperative subluxation (1.22 times), previous hip operations (1.14 times), and concomitant PFO (1.09 times). In the absence of randomised controlled studies, the findings of this analysis can help the surgeon to make treatment decisions. PMID:18719916
A refined method for multivariate meta-analysis and meta-regression
Jackson, Daniel; Riley, Richard D
2014-01-01
Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects’ standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:23996351
Shen, Chung-Wei; Chen, Yi-Hau
2015-10-01
Missing observations and covariate measurement error commonly arise in longitudinal data. However, existing methods for model selection in marginal regression analysis of longitudinal data fail to address the potential bias resulting from these issues. To tackle this problem, we propose a new model selection criterion, the Generalized Longitudinal Information Criterion, which is based on an approximately unbiased estimator for the expected quadratic error of a considered marginal model accounting for both data missingness and covariate measurement error. The simulation results reveal that the proposed method performs quite well in the presence of missing data and covariate measurement error. On the contrary, the naive procedures without taking care of such complexity in data may perform quite poorly. The proposed method is applied to data from the Taiwan Longitudinal Study on Aging to assess the relationship of depression with health and social status in the elderly, accommodating measurement error in the covariate as well as missing observations.
Multiple Regression Analysis Approach To The Automatic Design Of Adaptive Image Processing Systems
NASA Astrophysics Data System (ADS)
Otsu, N.
1984-01-01
Multiple regression analysis for modeling the correspondence between a set of input variates and an output variate or a set of variates seems to be one of the most promising and direct approaches to automatically designing adaptive (or learning) systems for image pro-cessing and computer vision. Some approaches are shown with experimental results, such as automatic design of adaptive filters for image enhancement and restoration by giving the input image and the desired out-put image as a pair. The advantage of such an approach is the capability to simulate in an automatic and gen-eral way the functional "black boxes" (solutions) which are imposed by real problems regard-less of their inner detail, while the usual approaches are based on the so-called trial and error methods where any method proposed is repeatedly tried and checked for its results.
Melanin and blood concentration in human skin studied by multiple regression analysis: experiments
NASA Astrophysics Data System (ADS)
Shimada, M.; Yamada, Y.; Itoh, M.; Yatagai, T.
2001-09-01
Knowledge of the mechanism of human skin colour and measurement of melanin and blood concentration in human skin are needed in the medical and cosmetic fields. The absorbance spectrum from reflectance at the visible wavelength of human skin increases under several conditions such as a sunburn or scalding. The change of the absorbance spectrum from reflectance including the scattering effect does not correspond to the molar absorption spectrum of melanin and blood. The modified Beer-Lambert law is applied to the change in the absorbance spectrum from reflectance of human skin as the change in melanin and blood is assumed to be small. The concentration of melanin and blood was estimated from the absorbance spectrum reflectance of human skin using multiple regression analysis. Estimated concentrations were compared with the measured one in a phantom experiment and this method was applied to in vivo skin.
A semiparametric likelihood-based method for regression analysis of mixed panel-count data.
Zhu, Liang; Zhang, Ying; Li, Yimei; Sun, Jianguo; Robison, Leslie L
2017-09-15
Panel-count data arise when each study subject is observed only at discrete time points in a recurrent event study, and only the numbers of the event of interest between observation time points are recorded (Sun and Zhao, 2013). However, sometimes the exact number of events between some observation times is unknown and what we know is only whether the event of interest has occurred. In this article, we will refer this type of data to as mixed panel-count data and propose a likelihood-based semiparametric regression method for their analysis by using the nonhomogeneous Poisson process assumption. However, we establish the asymptotic properties of the resulting estimator by employing the empirical process theory and without using the Poisson assumption. Also, we conduct an extensive simulation study, which suggests that the proposed method works well in practice. Finally, the method is applied to a Childhood Cancer Survivor Study that motivated this study. © 2017, The International Biometric Society.
Lee, Yueh-Chiang; Sun, Ya Chung
2009-01-01
Even though use of the internet by adolescents has grown exponentially, little is known about the correlation between their interaction via Instant Messaging (IM) and the evolution of their interpersonal relationships in real life. In the present study, 369 junior high school students in Taiwan responded to questions regarding their IM usage and their dispositional measures of real-life interpersonal relationships. Descriptive statistics, factor analysis, and quantile regression methods were used to analyze the data. Results indicate that (1) IM helps define adolescents' self-identity (forming and maintaining individual friendships) and social-identity (belonging to a peer group), and (2) how development of an interpersonal relationship is impacted by the use of IM since it appears that adolescents use IM to improve their interpersonal relationships in real life.
NASA Astrophysics Data System (ADS)
Gad, R. S.; Parab, J. S.; Naik, G. M.
2010-11-01
Multivariate system spectroscopic model plays important role in understanding chemometrics of ensemble under study. Here in this manuscript we discuss various approaches of modeling of spectroscopic system and demonstrate how Lorentz oscillator can be used to model any general spectroscopic system. Chemometric studies require customized templates design for the corresponding variants participating in ensemble, which generates the characteristic matrix of the ensemble under study. The typical biological system that resembles human blood tissue consisting of five major constituents i.e., alanine, urea, lactate, glucose, ascorbate; has been tested on the model. The model was validated using three approaches, namely, root mean square error (RMSE) analysis in the range of ±5% confidence interval, clerk gird error plot, and RMSE versus percent noise level study. Also the model was tested across various template sizes (consisting of samples ranging from 10 up to 1000) to ascertain the validity of partial least squares regression. The model has potential in understanding the chemometrics of proteomics pathways.
Tam, Vivian W Y; Wang, K; Tam, C M
2008-04-01
Recycled demolished concrete (DC) as recycled aggregate (RA) and recycled aggregate concrete (RAC) is generally suitable for most construction applications. Low-grade applications, including sub-base and roadwork, have been implemented in many countries; however, higher-grade activities are rarely considered. This paper examines relationships among DC characteristics, properties of their RA and strength of their RAC using regression analysis. Ten samples collected from demolition sites are examined. The results show strong correlation among the DC samples, properties of RA and RAC. It should be highlighted that inferior quality of DC will lower the quality of RA and thus their RAC. Prediction of RAC strength is also formulated from the DC characteristics and the RA properties. From that, the RAC performance from DC and RA can be estimated. In addition, RAC design requirements can also be developed at the initial stage of concrete demolition. Recommendations are also given to improve the future concreting practice.
Melanin and blood concentration in human skin studied by multiple regression analysis: experiments.
Shimada, M; Yamada, Y; Itoh, M; Yatagai, T
2001-09-01
Knowledge of the mechanism of human skin colour and measurement of melanin and blood concentration in human skin are needed in the medical and cosmetic fields. The absorbance spectrum from reflectance at the visible wavelength of human skin increases under several conditions such as a sunburn or scalding. The change of the absorbance spectrum from reflectance including the scattering effect does not correspond to the molar absorption spectrum of melanin and blood. The modified Beer-Lambert law is applied to the change in the absorbance spectrum from reflectance of human skin as the change in melanin and blood is assumed to be small. The concentration of melanin and blood was estimated from the absorbance spectrum reflectance of human skin using multiple regression analysis. Estimated concentrations were compared with the measured one in a phantom experiment and this method was applied to in vivo skin.
Kang, Kookjin; Roh, Yongrae
2003-09-01
The performance of an acoustic transducer is determined by the effects of many structural variables, and in most cases the influences of these variables are not linearly independent of each other. To achieve optimal performance of an acoustic transducer, we must consider the cross-coupled effects of its structural variables. In this study, with the finite-element method, the variation of the operation frequency and sound pressure of a flextensional transducer in relation to its structural variables is analyzed. Through statistical multiple regression analysis of the results, functional forms of the operation frequency and sound pressure of the transducer in terms of the structural variables were derived, with which the optimal structure of the transducer was determined by means of a constrained optimization technique, the sequential quadratic programming method of Phenichny and Danilin. The proposed method can reflect all the cross-coupled effects of multiple structural variables, and can be extended to the design of general acoustic transducers.
Moon, Sung-Chul; Kim, Hong-Kyun; Kwon, Taek-Ka; Han, Seong Ho; An, Chang-Hyeon; Park, Young-Seok
2013-06-01
To understand the growth patterns of skeletal open bite and deepbite, we present observations from 9 years of pure longitudinal data based on lateral cephalometric radiographs using mixed-effects regression model analysis. In total, 51 children (14 years old) with extreme values for the ratio of lower anterior facial height to total anterior facial height were assigned to 1 of 2 groups: a skeletal open-bite group (11 boys, 14 girls) or a skeletal deepbite group (14 boys, 12 girls). Measurements of total anterior facial height, upper anterior facial height, lower anterior facial height, total posterior facial height, ramus height, and ratio of lower anterior facial height to total anterior facial height were obtained for all subjects. All data were analyzed and interpreted using a mixed-effects regression model analysis with random effects. From these 4 groups at 14 years old, statistically significant differences were observed between the groups when subjects of the same sex were compared; however, statistical significance was not reached between subjects of opposite sexes in each group. Morphologic differences were clearly evident from the start and became more pronounced with age. There were statistical significances in the initial values and increases with age in all 6 variables except for increases with age in the ratio of lower anterior facial height to total anterior facial height. Statistical significance was also reached for morphologic differences between the annual increases in the ratio of lower anterior facial height to total anterior facial height and lower anterior facial height. In general, individual random variability was high in all variables when compared with the annual changes over time. Divergent patterns were established early and became more pronounced with age, with anterior facial height dimensions primarily contributing to these differences. Individual variations were so pronounced that caution is recommended for all clinical decisions
Brain networks of temporal preparation: A multiple regression analysis of neuropsychological data.
Triviño, Mónica; Correa, Ángel; Lupiáñez, Juan; Funes, María Jesús; Catena, Andrés; He, Xun; Humphreys, Glyn W
2016-11-15
There are only a few studies on the brain networks involved in the ability to prepare in time, and most of them followed a correlational rather than a neuropsychological approach. The present neuropsychological study performed multiple regression analysis to address the relationship between both grey and white matter (measured by magnetic resonance imaging in patients with brain lesion) and different effects in temporal preparation (Temporal orienting, Foreperiod and Sequential effects). Two versions of a temporal preparation task were administered to a group of 23 patients with acquired brain injury. In one task, the cue presented (a red versus green square) to inform participants about the time of appearance (early versus late) of a target stimulus was blocked, while in the other task the cue was manipulated on a trial-by-trial basis. The duration of the cue-target time intervals (400 versus 1400ms) was always manipulated within blocks in both tasks. Regression analysis were conducted between either the grey matter lesion size or the white matter tracts disconnection and the three temporal preparation effects separately. The main finding was that each temporal preparation effect was predicted by a different network of structures, depending on cue expectancy. Specifically, the Temporal orienting effect was related to both prefrontal and temporal brain areas. The Foreperiod effect was related to right and left prefrontal structures. Sequential effects were predicted by both parietal cortex and left subcortical structures. These findings show a clear dissociation of brain circuits involved in the different ways to prepare in time, showing for the first time the involvement of temporal areas in the Temporal orienting effect, as well as the parietal cortex in the Sequential effects.
Japanese elderly persons walk faster than non-Asian elderly persons: a meta-regression analysis
Ando, Masataka; Kamide, Naoto
2015-01-01
[Purpose] The purpose of this study was to clarify ethnic differences in walking speed by comparing walking speed in both Japanese and non-Asian elderly individuals and to investigate the necessity of consideration of ethnic differences in walking speed. [Subjects and Methods] Articles that reported comfortable walking speeds for community-dwelling elderly individuals were identified from electronic databases. Articles that involved community-dwelling individuals who were 60 years old or older and well functioning were included in the study. Articles that involved Asians were excluded. Weighted means for 5-m walking times were calculated as walking speeds from the Japanese and non-Asian sample data. The effects of age, gender, and ethnicity on 5-m walking times were then investigated using meta-regression analysis. [Results] Twenty studies (34 groups) were included for Japanese, and 16 studies (28 groups) were included for non-Asians. The weighted mean 5-m walking time was estimated to be 4.15 sec (95% confidence interval [CI]: 3.87–4.44) for Japanese and 4.24 sec (95% CI: 4.09–4.40) for non-Asians. Furthermore, using meta-regression analysis adjusted for age and gender, the 5-m walking time was 0.40 sec faster (95% CI: 0.03–0.77) for Japanese than for non-Asian elderly individuals. [Conclusion] Walking speed appeared faster for Japanese community-dwelling elderly individuals than for non-Asian elderly individuals. PMID:26696722
Damghi, Nada; Khoudri, Ibtissam; Oualili, Latifa; Abidi, Khalid; Madani, Naoufel; Zeggwagh, Amine Ali; Abouqal, Redouane
2008-07-01
Meeting the needs of patients' family members becomes an essential part of responsibilities of intensive care unit physicians. The aim of this study was to evaluate the satisfaction of patients' family members using the Arabic version of the Society of Critical Care Medicine's Family Needs Assessment questionnaire and to assess the predictors of family satisfaction using the classification and regression tree method. The authors conducted a prospective study. This study was conducted at a 12-bed medical intensive care unit in Morocco. Family representatives (n = 194) of consecutive patients with a length of stay >48 hrs were included in the study. Intervention was the Society of Critical Care Medicine's Family Needs Assessment questionnaire. Demographic data for relatives included age, gender, relationship with patients, education level, and intensive care unit commuting time. Clinical data for patients included age, gender, diagnoses, intensive care unit length of stay, Acute Physiology and Chronic Health Evaluation, MacCabe index, Therapeutic Interventioning Scoring System, and mechanical ventilation. The Arabic version of the Society of Critical Care Medicine's Family Needs Assessment questionnaire was administered between the third and fifth days after admission. Of family representatives, 81% declared being satisfied with information provided by physicians, 27% would like more information about the diagnosis, 30% about prognosis, and 45% about treatment. In univariate analysis, family satisfaction (small Society of Critical Care Medicine's Family Needs Assessment questionnaire score) increased with a lower family education level (p = .005), when the information was given by a senior physician (p = .014), and when the Society of Critical Care Medicine's Family Needs Assessment questionnaire was administered by an investigator (p = .002). Multivariate analysis (classification and regression tree) showed that the education level was the predominant factor
Fornetti, Jaime; Jindal, Sonali; Middleton, Kara A; Borges, Virginia F; Schedin, Pepper
2014-04-01
Cyclooxygenase-2 (COX-2) overexpression is implicated in increased risk and poorer outcomes in breast cancer in young women. We investigated COX-2 regulation in normal premenopausal breast tissue and its relationship to malignancy in young women. Quantitative COX-2 immunohistochemistry was performed on adjacent normal and breast cancer tissues from 96 premenopausal women with known clinical reproductive histories, and on rat mammary glands with distinct ovarian hormone exposures. COX-2 expression in the normal breast epithelium varied more than 40-fold between women and was associated with COX-2 expression levels in ductal carcinoma in situ and invasive cancer. Normal breast COX-2 expression was independent of known breast cancer prognostic indicators, including tumor stage and clinical subtype, indicating that factors regulating physiological COX-2 expression may be the primary drivers of COX-2 expression in breast cancer. Ovarian hormones, particularly at pregnancy levels, were identified as modulators of COX-2 in normal mammary epithelium. However, serial breast biopsy analysis in nonpregnant premenopausal women suggested relatively stable baseline levels of COX-2 expression, which persisted independent of menstrual cycling. These data provide impetus to investigate how baseline COX-2 expression is regulated in premenopausal breast tissue because COX-2 levels in normal breast epithelium may prove to be an indicator of breast cancer risk in young women, and predict the chemopreventive and therapeutic efficacy of COX-2 inhibitors in this population.
Fornetti, Jaime; Jindal, Sonali; Middleton, Kara A.; Borges, Virginia F.; Schedin, Pepper
2015-01-01
Cyclooxygenase-2 (COX-2) overexpression is implicated in increased risk and poorer outcomes in breast cancer in young women. We investigated COX-2 regulation in normal premenopausal breast tissue and its relationship to malignancy in young women. Quantitative COX-2 immunohistochemistry was performed on adjacent normal and breast cancer tissues from 96 premenopausal women with known clinical reproductive histories, and on rat mammary glands with distinct ovarian hormone exposures. COX-2 expression in the normal breast epithelium varied more than 40-fold between women and was associated with COX-2 expression levels in ductal carcinoma in situ and invasive cancer. Normal breast COX-2 expression was independent of known breast cancer prognostic indicators, including tumor stage and clinical subtype, indicating that factors regulating physiological COX-2 expression may be the primary drivers of COX-2 expression in breast cancer. Ovarian hormones, particularly at pregnancy levels, were identified as modulators of COX-2 in normal mammary epithelium. However, serial breast biopsy analysis in nonpregnant premenopausal women suggested relatively stable baseline levels of COX-2 expression, which persisted independent of menstrual cycling. These data provide impetus to investigate how baseline COX-2 expression is regulated in premenopausal breast tissue because COX-2 levels in normal breast epithelium may prove to be an indicator of breast cancer risk in young women, and predict the chemopreventive and therapeutic efficacy of COX-2 inhibitors in this population. PMID:24518566
Montgomery, M E; White, M E; Martin, S W
1987-01-01
Results from discriminant analysis and logistic regression were compared using two data sets from a study on predictors of coliform mastitis in dairy cows. Both techniques selected the same set of variables as important predictors and were of nearly equal value in classifying cows as having, or not having mastitis. The logistic regression model made fewer classification errors. The magnitudes of the effects were considerably different for some variables. Given the failure to meet the underlying assumptions of discriminant analysis, the coefficients from logistic regression are preferable. PMID:3453271
An integrated study of surface roughness in EDM process using regression analysis and GSO algorithm
NASA Astrophysics Data System (ADS)
Zainal, Nurezayana; Zain, Azlan Mohd; Sharif, Safian; Nuzly Abdull Hamed, Haza; Mohamad Yusuf, Suhaila
2017-09-01
The aim of this study is to develop an integrated study of surface roughness (Ra) in the die-sinking electrical discharge machining (EDM) process of Ti-6AL-4V titanium alloy with positive polarity of copper-tungsten (Cu-W) electrode. Regression analysis and glowworm swarm optimization (GSO) algorithm were considered for modelling and optimization process. Pulse on time (A), pulse off time (B), peak current (C) and servo voltage (D) were selected as the machining parameters with various levels. The experiments have been conducted based on the two levels of full factorial design with an added center point design of experiments (DOE). Moreover, mathematical models with linear and 2 factor interaction (2FI) effects of the parameters chosen were developed. The validity test of the fit and the adequacy of the developed mathematical models have been carried out by using analysis of variance (ANOVA) and F-test. The statistical analysis showed that the 2FI model outperformed with the most minimal value of Ra compared to the linear model and experimental result.
Spontaneous skin regression and predictors of skin regression in Thai scleroderma patients.
Foocharoen, Chingching; Mahakkanukrauh, Ajanee; Suwannaroj, Siraphop; Nanagara, Ratanavadee
2011-09-01
Skin tightness is a major clinical manifestation of systemic sclerosis (SSc). Importantly for both clinicians and patients, spontaneous regression of the fibrosis process has been documented. The purpose of this study is to identify the incidence and related clinical characteristics of spontaneous regression among Thai SSc patients. A historical cohort with 4 years of follow-up was performed among SSc patients over 15 years of age diagnosed with SSc between January 1, 2005 and December 31, 2006 in Khon Kaen, Thailand. The start date was the date of the first symptom and the end date was the date of the skin score ≤2. To estimate the respective probability of regression and to assess the associated factors, the Kaplan-Meier method and Cox regression analysis was used. One hundred seventeen cases of SSc were included with a female to male ratio of 1.5:1. Thirteen patients (11.1%) experienced regression. The incidence rate of spontaneous skin regression was 0.31 per 100 person-months and the average duration of SSc at the time of regression was 35.9±15.6 months (range, 15.7-60 months). The factors that negatively correlated with regression were (a) diffuse cutaneous type, (b) Raynaud's phenomenon, (c) esophageal dysmotility, and (d) colchicine treatment at onset with a respective hazard ratio (HR) of 0.19, 0.19, 0.26, and 0.20. By contrast, the factor that positively correlated with regression was active alveolitis with cyclophosphamide therapy at onset with an HR of 4.23 (95% CI, 1.23-14.10). After regression analysis, only Raynaud's phenomenon at onset and diffuse cutaneous type had a significantly negative correlation to regression. A spontaneous regression of the skin fibrosis process was not uncommon among Thai SSc patients. The factors suggesting a poor predictor for cutaneous manifestation were Raynaud's phenomenon, diffuse cutaneous type while early cyclophosphamide therapy might be related to a better skin outcome.
Lebl, Darren R; Bono, Christopher M; Velmahos, George; Metkar, Umesh; Nguyen, Joseph; Harris, Mitchel B
2013-07-15
Retrospective analysis of prospective registry data. To determine the patient characteristics, risk factors, and fracture patterns associated with vertebral artery injury (VAI) in patients with blunt cervical spine injury. VAI associated with cervical spine trauma has the potential for catastrophical clinical sequelae. The patterns of cervical spine injury and patient characteristics associated with VAI remain to be determined. A retrospective review of prospectively collected data from the American College of Surgeons trauma registries at 3 level-1 trauma centers identified all patients with a cervical spine injury on multidetector computed tomographic scan during a 3-year period (January 1, 2007, to January 1, 2010). Fracture pattern and patient characteristics were recorded. Logistic multivariate regression analysis of independent predictors for VAI and subgroup analysis of neurological events related to VAI was performed. Twenty-one percent of 1204 patients with cervical injuries (n = 253) underwent screening for VAI by multidetector computed tomography angiogram. VAI was diagnosed in 17% (42 of 253), unilateral in 15% (38 of 253), and bilateral in 1.6% (4 of 253) and was associated with a lower Glasgow coma scale (P < 0.001), a higher injury severity score (P < 0.01), and a higher mortality (P < 0.001). VAI was associated with ankylosing spondylitis/diffuse idiopathic skeletal hyperosteosis (crude odds ratio [OR] = 8.04; 95% confidence interval [CI], 1.30-49.68; P = 0.034), and occipitocervical dissociation (P < 0.001) by univariate analysis and fracture displacement into the transverse foramen 1 mm or more (adjusted OR = 3.29; 95% CI, 1.15-9.41; P = 0.026), and basilar skull fracture (adjusted OR = 4.25; 95% CI, 1.25-14.47; P= 0.021), by multivariate regression model. Subgroup analyses of neurological events secondary to VAI occurred in 14% (6 of 42) and the stroke-related mortality rate was 4.8% (2 of 42). Neurological events were associated with male sex (P
JOINT STRUCTURE SELECTION AND ESTIMATION IN THE TIME-VARYING COEFFICIENT COX MODEL
Xiao, Wei; Lu, Wenbin; Zhang, Hao Helen
2016-01-01
Time-varying coefficient Cox model has been widely studied and popularly used in survival data analysis due to its flexibility for modeling covariate effects. It is of great practical interest to accurately identify the structure of covariate effects in a time-varying coefficient Cox model, i.e. covariates with null effect, constant effect and truly time-varying effect, and estimate the corresponding regression coefficients. Combining the ideas of local polynomial smoothing and group nonnegative garrote, we develop a new penalization approach to achieve such goals. Our method is able to identify the underlying true model structure with probability tending to one and simultaneously estimate the time-varying coefficients consistently. The asymptotic normalities of the resulting estimators are also established. We demonstrate the performance of our method using simulations and an application to the primary biliary cirrhosis data. PMID:27540275
NASA Astrophysics Data System (ADS)
Bunch, N. L.; Spasojevic, M.; Shprits, Y.; Golden, D. I.
2011-12-01
Outer radiation belt fluxes vary by orders of magnitude on time scales of hours to days (Li et al., 2001). Wave-particle interactions involving lower band chorus waves are thought to play a major role in acceleration and loss of energetic electrons in the outer belt. Wave particle interactions involving chorus and the highest energy electrons (>MeV) is possible only at latitudes above about 20° (Shprits and Ni, 2009). Despite their perceived importance in controlling energetic electron populations in the radiation belts, relatively insubstantial statistical characterization exists from which to base radiation belt model inputs for chorus. Recent investigations employing a database of chorus events observed by the Polar spacecraft have begun to characterize chorus waves at 20° magnetic latitude and above (Bunch et al., 2011). This study utilizes the Polar wave database to parameterize wave intensities as a function of spatial location and geomagnetic driving conditions (e.g. AE, Vsw, Kp, etc.). The relative correlation of chorus occurrence and amplitude with geomagnetic conditions is also examined using an auto regressive moving average (ARMA) technique for non-independent observations, such as those made by orbiting spacecraft. Regression analysis shows significant correlation of chorus with increased AE, Vsw, and Kp, and much lower correlations with proton density, and pressure. Wave parameterizations show, for fixed range in L, an increase in chorus amplitude with magnetic latitude in the dawn sector. Amplitudes appear more constant over a range of latitudes at noon, particularly for increased activity levels. These results represent significant steps forward toward a more complete characterization of the chorus wave environment and understanding the role chorus plays in regulation of the radiation belt environment.
NASA Technical Reports Server (NTRS)
Jolly, William H.
1992-01-01
Relationships defining the ballistic limit of Space Station Freedom's (SSF) dual wall protection systems have been determined. These functions were regressed from empirical data found in Marshall Space Flight Center's (MSFC) Hypervelocity Impact Testing Summary (HITS) for the velocity range between three and seven kilometers per second. A stepwise linear least squares regression was used to determine the coefficients of several expressions that define a ballistic limit surface. Using statistical significance indicators and graphical comparisons to other limit curves, a final set of expressions is recommended for potential use in Probability of No Critical Flaw (PNCF) calculations for Space Station. The three equations listed below represent the mean curves for normal, 45 degree, and 65 degree obliquity ballistic limits, respectively, for a dual wall protection system consisting of a thin 6061-T6 aluminum bumper spaced 4.0 inches from a .125 inches thick 2219-T87 rear wall with multiple layer thermal insulation installed between the two walls. Normal obliquity is d(sub c) = 1.0514 v(exp 0.2983 t(sub 1)(exp 0.5228). Forty-five degree obliquity is d(sub c) = 0.8591 v(exp 0.0428) t(sub 1)(exp 0.2063). Sixty-five degree obliquity is d(sub c) = 0.2824 v(exp 0.1986) t(sub 1)(exp -0.3874). Plots of these curves are provided. A sensitivity study on the effects of using these new equations in the probability of no critical flaw analysis indicated a negligible increase in the performance of the dual wall protection system for SSF over the current baseline. The magnitude of the increase was 0.17 percent over 25 years on the MB-7 configuration run with the Bumper II program code.
Multivariate phenotype association analysis by marker-set kernel machine regression.
Maity, Arnab; Sullivan, Patrick F; Tzeng, Jun-Ying
2012-11-01
Genetic studies of complex diseases often collect multiple phenotypes relevant to the disorders. As these phenotypes can be correlated and share common genetic mechanisms, jointly analyzing these traits may bring more power to detect genes influencing individual or multiple phenotypes. Given the advancement brought by the multivariate phenotype approaches and the multimarker kernel machine regression, we construct a multivariate regression based on kernel machine to facilitate the joint evaluation of multimarker effects on multiple phenotypes. The kernel machine serves as a powerful dimension-reduction tool to capture complex effects among markers. The multivariate framework incorporates the potentially correlated multidimensional phenotypic information and accommodates common or different environmental covariates for each trait. We derive the multivariate kernel machine test based on a score-like statistic, and conduct simulations to evaluate the validity and efficacy of the method. We also study the performance of the commonly adapted strategies for kernel machine analysis on multiple phenotypes, including the multiple univariate kernel machine tests with original phenotypes or with their principal components. Our results suggest that none of these approaches has the uniformly best power, and the optimal test depends on the magnitude of the phenotype correlation and the effect patterns. However, the multivariate test retains to be a reasonable approach when the multiple phenotypes have none or mild correlations, and gives the best power once the correlation becomes stronger or when there exist genes that affect more than one phenotype. We illustrate the utility of the multivariate kernel machine method through the Clinical Antipsychotic Trails of Intervention Effectiveness antibody study. © 2012 Wiley Periodicals, Inc.
Andrianov, B V; Goryacheva, I I; Vlasov, S V; Gorelova, T V; Harutyunova, M V; Harutyunova, K V; Mayilyan, K R; Zakharov, I A
2015-03-01
Black flies (Diptera, Simuliidae) are well known for their medical, environmental, and veterinary importance. The simuliid fauna of Armenia includes 53 species. A number of dominant species are of ecological importance. Complex analysis, which involved morphometric, cytogenetic, and molecular genetic approaches, was conducted to characterize the species status of black flies inhabiting the territory of Armenia. It was shown that the predominant simuliid species, Simulium paraequinum and Simulium kiritshenkoi, belong to a group of species with minimal variability of the cox1 gene. The recently discovered species, Simulium noellery and Simulium [B.] erythrocephalum, which are new to Armenia, can be considered as potentially invasive, which is supported by the low level of variability of the cox1 gene.
Functional regression method for whole genome eQTL epistasis analysis with sequencing data.
Xu, Kelin; Jin, Li; Xiong, Momiao
2017-05-18
Epistasis plays an essential rule in understanding the regulation mechanisms and is an essential component of the genetic architecture of the gene expressions. However, interaction analysis of gene expressions remains fundamentally unexplored due to great computational challenges and data availability. Due to variation in splicing, transcription start sites, polyadenylation sites, post-transcriptional RNA editing across the entire gene, and transcription rates of the cells, RNA-seq measurements generate large expression variability and collectively create the observed position level read count curves. A single number for measuring gene expression which is widely used for microarray measured gene expression analysis is highly unlikely to sufficiently account for large expression variation across the gene. Simultaneously analyzing epistatic architecture using the RNA-seq and whole genome sequencing (WGS) data poses enormous challenges. We develop a nonlinear functional regression model (FRGM) with functional responses where the position-level read counts within a gene are taken as a function of genomic position, and functional predictors where genotype profiles are viewed as a function of genomic position, for epistasis analysis with RNA-seq data. Instead of testing the interaction of all possible pair-wises SNPs, the FRGM takes a gene as a basic unit for epistasis analysis, which tests for the interaction of all possible pairs of genes and use all the information that can be accessed to collectively test interaction between all possible pairs of SNPs within two genome regions. By large-scale simulations, we demonstrate that the proposed FRGM for epistasis analysis can achieve the correct type 1 error and has higher power to detect the interactions between genes than the existing methods. The proposed methods are applied to the RNA-seq and WGS data from the 1000 Genome Project. The numbers of pairs of significantly interacting genes after Bonferroni correction
Coelho, Lúcia H G; Gutz, Ivano G R
2006-03-15
A chemometric method for analysis of conductometric titration data was introduced to extend its applicability to lower concentrations and more complex acid-base systems. Auxiliary pH measurements were made during the titration to assist the calculation of the distribution of protonable species on base of known or guessed equilibrium constants. Conductivity values of each ionized or ionizable species possibly present in the sample were introduced in a general equation where the only unknown parameters were the total concentrations of (conjugated) bases and of strong electrolytes not involved in acid-base equilibria. All these concentrations were adjusted by a multiparametric nonlinear regression (NLR) method, based on the Levenberg-Marquardt algorithm. This first conductometric titration method with NLR analysis (CT-NLR) was successfully applied to simulated conductometric titration data and to synthetic samples with multiple components at concentrations as low as those found in rainwater (approximately 10 micromol L(-1)). It was possible to resolve and quantify mixtures containing a strong acid, formic acid, acetic acid, ammonium ion, bicarbonate and inert electrolyte with accuracy of 5% or better.
Generalized multilevel function-on-scalar regression and principal component analysis.
Goldsmith, Jeff; Zipunnikov, Vadim; Schrack, Jennifer
2015-06-01
This manuscript considers regression models for generalized, multilevel functional responses: functions are generalized in that they follow an exponential family distribution and multilevel in that they are clustered within groups or subjects. This data structure is increasingly common across scientific domains and is exemplified by our motivating example, in which binary curves indicating physical activity or inactivity are observed for nearly 600 subjects over 5 days. We use a generalized linear model to incorporate scalar covariates into the mean structure, and decompose subject-specific and subject-day-specific deviations using multilevel functional principal components analysis. Thus, functional fixed effects are estimated while accounting for within-function and within-subject correlations, and major directions of variability within and between subjects are identified. Fixed effect coefficient functions and principal component basis functions are estimated using penalized splines; model parameters are estimated in a Bayesian framework using Stan, a programming language that implements a Hamiltonian Monte Carlo sampler. Simulations designed to mimic the application have good estimation and inferential properties with reasonable computation times for moderate datasets, in both cross-sectional and multilevel scenarios; code is publicly available. In the application we identify effects of age and BMI on the time-specific change in probability of being active over a 24-hour period; in addition, the principal components analysis identifies the patterns of activity that distinguish subjects and days within subjects.
A regressive model analysis of congenital sensorineural deafness in German Dalmatian dogs.
Juraschko, Kathrin; Meyer-Lindenberg, Andrea; Nolte, Ingo; Distl, Ottmar
2003-08-01
The objective of the present study was to analyze the mode of inheritance for congenital sensorineural deafness (CSD) in German Dalmatian dogs by consideration of association between phenotypic breed characteristics and CSD. Segregation analysis with regressive logistic models was employed to test for different mechanisms of genetic transmission. Data were obtained from all three Dalmatian kennel clubs associated with the German Association for Dog Breeding and Husbandry (VDH). CSD was tested by veterinary practitioners using standardized protocols for Brainstem Auditory-Evoked Response (BAER). The sample included 1899 Dalmatian dogs from 354 litters in 169 different kennels. BAER testing results were from the years 1986 to 1999. Pedigree information was available for up to seven generations. The segregation analysis showed that a mixed monogenic-polygenic model including eye color as covariate among all other tested models best explained the segregation of affected animals in the pedigrees. The recessive major gene segregated in dogs with blue and brown eye color as well as in dogs with and without pigmented coat patches. Models which took into account the occurrence of patches, percentage of puppies tested per litter, or inbreeding coefficient gave no better adjustment to the most general (saturated) model. A procedure for the simultaneous prediction of breeding values and the estimation of genotype probabilities for CSD is expected to improve breeding programs significantly.
NASA Astrophysics Data System (ADS)
Rajab, Jasim Mohammed; Jafri, Mohd. Zubir Mat; Lim, Hwee San; Abdullah, Khiruddin
2012-10-01
This study encompasses air surface temperature (AST) modeling in the lower atmosphere. Data of four atmosphere pollutant gases (CO, O3, CH4, and H2O) dataset, retrieved from the National Aeronautics and Space Administration Atmospheric Infrared Sounder (AIRS), from 2003 to 2008 was employed to develop a model to predict AST value in the Malaysian peninsula using the multiple regression method. For the entire period, the pollutants were highly correlated (R=0.821) with predicted AST. Comparisons among five stations in 2009 showed close agreement between the predicted AST and the observed AST from AIRS, especially in the southwest monsoon (SWM) season, within 1.3 K, and for in situ data, within 1 to 2 K. The validation results of AST with AST from AIRS showed high correlation coefficient (R=0.845 to 0.918), indicating the model's efficiency and accuracy. Statistical analysis in terms of β showed that H2O (0.565 to 1.746) tended to contribute significantly to high AST values during the northeast monsoon season. Generally, these results clearly indicate the advantage of using the satellite AIRS data and a correlation analysis study to investigate the impact of atmospheric greenhouse gases on AST over the Malaysian peninsula. A model was developed that is capable of retrieving the Malaysian peninsulan AST in all weather conditions, with total uncertainties ranging between 1 and 2 K.
The value of a statistical life: a meta-analysis with a mixed effects regression model.
Bellavance, François; Dionne, Georges; Lebeau, Martin
2009-03-01
The value of a statistical life (VSL) is a very controversial topic, but one which is essential to the optimization of governmental decisions. We see a great variability in the values obtained from different studies. The source of this variability needs to be understood, in order to offer public decision-makers better guidance in choosing a value and to set clearer guidelines for future research on the topic. This article presents a meta-analysis based on 39 observations obtained from 37 studies (from nine different countries) which all use a hedonic wage method to calculate the VSL. Our meta-analysis is innovative in that it is the first to use the mixed effects regression model [Raudenbush, S.W., 1994. Random effects models. In: Cooper, H., Hedges, L.V. (Eds.), The Handbook of Research Synthesis. Russel Sage Foundation, New York] to analyze studies on the value of a statistical life. We conclude that the variability found in the values studied stems in large part from differences in methodologies.
Machine learning of swimming data via wisdom of crowd and regression analysis.
Xie, Jiang; Xu, Junfu; Nie, Celine; Nie, Qing
2017-04-01
Every performance, in an officially sanctioned meet, by a registered USA swimmer is recorded into an online database with times dating back to 1980. For the first time, statistical analysis and machine learning methods are systematically applied to 4,022,631 swim records. In this study, we investigate performance features for all strokes as a function of age and gender. The variances in performance of males and females for different ages and strokes were studied, and the correlations of performances for different ages were estimated using the Pearson correlation. Regression analysis show the performance trends for both males and females at different ages and suggest critical ages for peak training. Moreover, we assess twelve popular machine learning methods to predict or classify swimmer performance. Each method exhibited different strengths or weaknesses in different cases, indicating no one method could predict well for all strokes. To address this problem, we propose a new method by combining multiple inference methods to derive Wisdom of Crowd Classifier (WoCC). Our simulation experiments demonstrate that the WoCC is a consistent method with better overall prediction accuracy. Our study reveals several new age-dependent trends in swimming and provides an accurate method for classifying and predicting swimming times.
VanEngelsdorp, Dennis; Speybroeck, Niko; Evans, Jay D; Nguyen, Bach Kim; Mullin, Chris; Frazier, Maryann; Frazier, Jim; Cox-Foster, Diana; Chen, Yanping; Tarpy, David R; Haubruge, Eric; Pettis, Jeffrey S; Saegerman, Claude
2010-10-01
Colony collapse disorder (CCD), a syndrome whose defining trait is the rapid loss of adult worker honey bees, Apis mellifera L., is thought to be responsible for a minority of the large overwintering losses experienced by U.S. beekeepers since the winter 2006-2007. Using the same data set developed to perform a monofactorial analysis (PloS ONE 4: e6481, 2009), we conducted a classification and regression tree (CART) analysis in an attempt to better understand the relative importance and interrelations among different risk variables in explaining CCD. Fifty-five exploratory variables were used to construct two CART models: one model with and one model without a cost of misclassifying a CCD-diagnosed colony as a non-CCD colony. The resulting model tree that permitted for misclassification had a sensitivity and specificity of 85 and 74%, respectively. Although factors measuring colony stress (e.g., adult bee physiological measures, such as fluctuating asymmetry or mass of head) were important discriminating values, six of the 19 variables having the greatest discriminatory value were pesticide levels in different hive matrices. Notably, coumaphos levels in brood (a miticide commonly used by beekeepers) had the highest discriminatory value and were highest in control (healthy) colonies. Our CART analysis provides evidence that CCD is probably the result of several factors acting in concert, making afflicted colonies more susceptible to disease. This analysis highlights several areas that warrant further attention, including the effect of sublethal pesticide exposure on pathogen prevalence and the role of variability in bee tolerance to pesticides on colony survivorship.
Menon, Prashanthi; Podolsky, Irina; Feig, Jonathan E.; Aderem, Alan; Fisher, Edward A.; Gold, Elizabeth S.
2014-01-01
We report the first systems biology investigation of regulators controlling arterial plaque macrophage transcriptional changes in response to lipid lowering in vivo in two distinct mouse models of atherosclerosis regression. Transcriptome measurements from plaque macrophages from the Reversa mouse were integrated with measurements from an aortic transplant-based mouse model of plaque regression. Functional relevance of the genes detected as differentially expressed in plaque macrophages in response to lipid lowering in vivo was assessed through analysis of gene functional annotations, overlap with in vitro foam cell studies, and overlap of associated eQTLs with human atherosclerosis/CAD risk SNPs. To identify transcription factors that control plaque macrophage responses to lipid lowering in vivo, we used an integrative strategy – leveraging macrophage epigenomic measurements – to detect enrichment of transcription factor binding sites upstream of genes that are differentially expressed in plaque macrophages during regression. The integrated analysis uncovered eight transcription factor binding site elements that were statistically overrepresented within the 5′ regulatory regions of genes that were upregulated in plaque macrophages in the Reversa model under maximal regression conditions and within the 5′ regulatory regions of genes that were upregulated in the aortic transplant model during regression. Of these, the TCF/LEF binding site was present in promoters of upregulated genes related to cell motility, suggesting that the canonical Wnt signaling pathway may be activated in plaque macrophages during regression. We validated this network-based prediction by demonstrating that β-catenin expression is higher in regressing (vs. control group) plaques in both regression models, and we further demonstrated that stimulation of canonical Wnt signaling increases macrophage migration in vitro. These results suggest involvement of canonical Wnt signaling in
Regression Analysis of Combined Gene Expression Regulation in Acute Myeloid Leukemia
Li, Yue; Liang, Minggao; Zhang, Zhaolei
2014-01-01
Gene expression is a combinatorial function of genetic/epigenetic factors such as copy number variation (CNV), DNA methylation (DM), transcription factors (TF) occupancy, and microRNA (miRNA) post-transcriptional regulation. At the maturity of microarray/sequencing technologies, large amounts of data measuring the genome-wide signals of those factors became available from Encyclopedia of DNA Elements (ENCODE) and The Cancer Genome Atlas (TCGA). However, there is a lack of an integrative model to take full advantage of these rich yet heterogeneous data. To this end, we developed RACER (Regression Analysis of Combined Expression Regulation), which fits the mRNA expression as response using as explanatory variables, the TF data from ENCODE, and CNV, DM, miRNA expression signals from TCGA. Briefly, RACER first infers the sample-specific regulatory activities by TFs and miRNAs, which are then used as inputs to infer specific TF/miRNA-gene interactions. Such a two-stage regression framework circumvents a common difficulty in integrating ENCODE data measured in generic cell-line with the sample-specific TCGA measurements. As a case study, we integrated Acute Myeloid Leukemia (AML) data from TCGA and the related TF binding data measured in K562 from ENCODE. As a proof-of-concept, we first verified our model formalism by 10-fold cross-validation on predicting gene expression. We next evaluated RACER on recovering known regulatory interactions, and demonstrated its superior statistical power over existing methods in detecting known miRNA/TF targets. Additionally, we developed a feature selection procedure, which identified 18 regulators, whose activities clustered consistently with cytogenetic risk groups. One of the selected regulators is miR-548p, whose inferred targets were significantly enriched for leukemia-related pathway, implicating its novel role in AML pathogenesis. Moreover, survival analysis using the inferred activities identified C-Fos as a potential AML
Modeling age-of-onset: Cox model with latent major gene effects
Li, H.; Thompson, E.A.
1994-09-01
Analysis of age-of-onset is a key factor in the segregation and linkage analysis of complex genetic traits, but is complicated by the censoring of unaffected individuals. Most previous work has used parametric distributional assumptions, but it is hard to characterize the distribution of age-of-onset by a single distribution. Other approaches discretize age-of-onset and use logistic regression to model incidence; this approach does not use the information fully. Frailty models have been used for age-of-oset in the biostatistics literature, but these models do not lend themselves to modeling the correlations due to genetic effects which segregate within a family. Here, we propose use of the Cox model with latent major gene effects; conditional on the major genotypes, Cox`s proportional hazards model is used for age-of-onset for each individual. This is a semiparametric model; we do not specify the baseline hazard function. Likelihood analysis of such models is restricted by the difficulty in evaluating of maximizing the likelihood, especially when data are available for some of the members of an extended pedigree. Markov chain Monte Carlo permits genotypic configurations to be realized from the posterior distributions given a current model and the observed data. Hence methods for likelihood analysis can be developed: Monte Carlo EM is used for estimation of the parameters and their variance-covariance matrix. Markers and observed covariates are easily incorporated into this analysis. We present the model, methods for likelihood analysis and the results of a simulation study. The results are comparable with those based on a Cox model with known genotypic dependence in a pedigree. An early-onset Alzheimer`s pedigree and some breast cancer pedigrees have been used as real data examples. Some possible extensions are also discussed.
ERIC Educational Resources Information Center
Kaplan, David
2005-01-01
This article considers the problem of estimating dynamic linear regression models when the data are generated from finite mixture probability density function where the mixture components are characterized by different dynamic regression model parameters. Specifically, conventional linear models assume that the data are generated by a single…
ERIC Educational Resources Information Center
Hecht, Jeffrey B.
Previous research has demonstrated particular inadequacies in conventional methods used to identify outlier cases in bivariate regression models. Only through a combination of methods can one detect all of the deviant points potentially overinfluencing a regression model's parameters. This paper investigates whether a range of data points might…
Beyond Multiple Regression: Using Commonality Analysis to Better Understand R[superscript 2] Results
ERIC Educational Resources Information Center
Warne, Russell T.
2011-01-01
Multiple regression is one of the most common statistical methods used in quantitative educational research. Despite the versatility and easy interpretability of multiple regression, it has some shortcomings in the detection of suppressor variables and for somewhat arbitrarily assigning values to the structure coefficients of correlated…
CATEGORICAL REGRESSION ANALYSIS OF ACUTE INHALATION TOXICITY DATA FOR HYDROGEN SULFIDE
Categorical regression is one of the tools offered by the U.S. EPA for derivation of acute reference exposures (AREs), which are dose-response assessments for acute exposures to inhaled chemicals. Categorical regression is used as a meta-analytical technique to calculate probabi...
Greensmith, David J.
2014-01-01
Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow. PMID:24125908
Bode, Manuela; Woellhaf, Michael W.; Bohnert, Maria; van der Laan, Martin; Sommer, Frederik; Jung, Martin; Zimmermann, Richard; Schroda, Michael; Herrmann, Johannes M.
2015-01-01
Members of the twin Cx9C protein family constitute the largest group of proteins in the intermembrane space (IMS) of mitochondria. Despite their conserved nature and their essential role in the biogenesis of the respiratory chain, the molecular function of twin Cx9C proteins is largely unknown. We performed a SILAC-based quantitative proteomic analysis to identify interaction partners of the conserved twin Cx9C protein Cox19. We found that Cox19 interacts in a dynamic manner with Cox11, a copper transfer protein that facilitates metalation of the Cu(B) center of subunit 1 of cytochrome c oxidase. The interaction with Cox11 is critical for the stable accumulation of Cox19 in mitochondria. Cox19 consists of a helical hairpin structure that forms a hydrophobic surface characterized by two highly conserved tyrosine-leucine dipeptides. These residues are essential for Cox19 function and its specific binding to a cysteine-containing sequence in Cox11. Our observations suggest that an oxidative modification of this cysteine residue of Cox11 stimulates Cox19 binding, pointing to a redox-regulated interplay of Cox19 and Cox11 that is critical for copper transfer in the IMS and thus for biogenesis of cytochrome c oxidase. PMID:25926683
Comparative analysis of regression and artificial neural network models for wind speed prediction
NASA Astrophysics Data System (ADS)
Bilgili, Mehmet; Sahin, Besir
2010-11-01
In this study, wind speed was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. A three-layer feedforward artificial neural network structure was constructed and a backpropagation algorithm was used for the training of ANNs. To get a successful simulation, firstly, the correlation coefficients between all of the meteorological variables (wind speed, ambient temperature, atmospheric pressure, relative humidity and rainfall) were calculated taking two variables in turn for each calculation. All independent variables were added to the simple regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and also used in the input layer of the ANN. The results obtained by all methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods.
Galling, Britta; Roldán, Alexandra; Hagi, Katsuhiko; Rietschel, Liz; Walyzada, Frozan; Zheng, Wei; Cao, Xiao‐Lan; Xiang, Yu‐Tao; Zink, Mathias; Kane, John M.; Nielsen, Jimmi; Leucht, Stefan; Correll, Christoph U.
2017-01-01
Antipsychotic polypharmacy in schizophrenia is much debated, since it is common and costly with unclear evidence for its efficacy and safety. We conducted a systematic literature search and a random effects meta‐analysis of randomized trials comparing augmentation with a second antipsychotic vs. continued antipsychotic monotherapy in schizophrenia. Co‐primary outcomes were total symptom reduction and study‐defined response. Antipsychotic augmentation was superior to monotherapy regarding total symptom reduction (16 studies, N=694, standardized mean difference, SMD=–0.53, 95% CI: −0.87 to −0.19, p=0.002). However, superiority was only apparent in open‐label and low‐quality trials (both p<0.001), but not in double‐blind and high‐quality ones (p=0.120 and 0.226, respectively). Study‐defined response was similar between antipsychotic augmentation and monotherapy (14 studies, N=938, risk ratio = 1.19, 95% CI: 0.99 to 1.42, p=0.061), being clearly non‐significant in double‐blind and high‐quality studies (both p=0.990). Findings were replicated in clozapine and non‐clozapine augmentation studies. No differences emerged regarding all‐cause/specific‐cause discontinuation, global clinical impression, as well as positive, general and depressive symptoms. Negative symptoms improved more with augmentation treatment (18 studies, N=931, SMD=–0.38, 95% CI: −0.63 to −0.13, p<0.003), but only in studies augmenting with aripiprazole (8 studies, N=532, SMD=–0.41, 95% CI: −0.79 to −0.03, p=0.036). Few adverse effect differences emerged: D2 antagonist augmentation was associated with less insomnia (p=0.028), but more prolactin elevation (p=0.015), while aripiprazole augmentation was associated with reduced prolactin levels (p<0.001) and body weight (p=0.030). These data suggest that the common practice of antipsychotic augmentation in schizophrenia lacks double‐blind/high‐quality evidence for efficacy, except for negative symptom
A New Global Regression Analysis Method for the Prediction of Wind Tunnel Model Weight Corrections
NASA Technical Reports Server (NTRS)
Ulbrich, Norbert Manfred; Bridge, Thomas M.; Amaya, Max A.
2014-01-01
A new global regression analysis method is discussed that predicts wind tunnel model weight corrections for strain-gage balance loads during a wind tunnel test. The method determines corrections by combining "wind-on" model attitude measurements with least squares estimates of the model weight and center of gravity coordinates that are obtained from "wind-off" data points. The method treats the least squares fit of the model weight separate from the fit of the center of gravity coordinates. Therefore, it performs two fits of "wind- off" data points and uses the least squares estimator of the model weight as an input for the fit of the center of gravity coordinates. Explicit equations for the least squares estimators of the weight and center of gravity coordinates are derived that simplify the implementation of the method in the data system software of a wind tunnel. In addition, recommendations for sets of "wind-off" data points are made that take typical model support system constraints into account. Explicit equations of the confidence intervals on the model weight and center of gravity coordinates and two different error analyses of the model weight prediction are also discussed in the appendices of the paper.
Cronk, Ryan; Bartram, Jamie
2017-10-03
Sufficient, safe, and continuously available water services are important for human development and health yet many water systems in low- and middle-income countries are nonfunctional. Monitoring data were analyzed using regression and Bayesian networks (BNs) to explore factors influencing the functionality of 82 503 water systems in Nigeria and Tanzania. Functionality varied by system type. In Tanzania, Nira handpumps were more functional than Afridev and India Mark II handpumps. Higher functionality was associated with fee collection in Nigeria. In Tanzania, functionality was higher if fees were collected monthly rather than in response to system breakdown. Systems in Nigeria were more likely to be functional if they were used for both human and livestock consumption. In Tanzania, systems managed by private operators were more functional than community-managed systems. The BNs found strong dependencies between functionality and system type and administrative unit (e.g., district). The BNs predicted functionality increased from 68% to 89% in Nigeria and from 53% to 68% in Tanzania when best observed conditions were in place. Improvements to water system monitoring and analysis of monitoring data with different modeling techniques may be useful for identifying water service improvement opportunities and informing evidence-based decision-making for better management, policy, programming, and practice.
Fernández-Fernández, Mario; Rodríguez-González, Pablo; García Alonso, J Ignacio
2016-10-01
We have developed a novel, rapid and easy calculation procedure for Mass Isotopomer Distribution Analysis based on multiple linear regression which allows the simultaneous calculation of the precursor pool enrichment and the fraction of newly synthesized labelled proteins (fractional synthesis) using linear algebra. To test this approach, we used the peptide RGGGLK as a model tryptic peptide containing three subunits of glycine. We selected glycine labelled in two (13) C atoms ((13) C2 -glycine) as labelled amino acid to demonstrate that spectral overlap is not a problem in the proposed methodology. The developed methodology was tested first in vitro by changing the precursor pool enrichment from 10 to 40% of (13) C2 -glycine. Secondly, a simulated in vivo synthesis of proteins was designed by combining the natural abundance RGGGLK peptide and 10 or 20% (13) C2 -glycine at 1 : 1, 1 : 3 and 3 : 1 ratios. Precursor pool enrichments and fractional synthesis values were calculated with satisfactory precision and accuracy using a simple spreadsheet. This novel approach can provide a relatively rapid and easy means to measure protein turnover based on stable isotope tracers. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.
Li, Wentian; Sun, Fengzhu; Grosse, Ivo
2004-01-01
One important issue commonly encountered in the analysis of microarray data is to decide which and how many genes should be selected for further studies. For discriminant microarray data analyses based on statistical models, such as the logistic regression models, gene selection can be accomplished by a comparison of the maximum likelihood of the model given the real data, L(D|M), and the expected maximum likelihood of the model given an ensemble of surrogate data with randomly permuted label, L(D(0)|M). Typically, the computational burden for obtaining L(D(0)M) is immense, often exceeding the limits of available computing resources by orders of magnitude. Here, we propose an approach that circumvents such heavy computations by mapping the simulation problem to an extreme-value problem. We present the derivation of an asymptotic distribution of the extreme-value as well as its mean, median, and variance. Using this distribution, we propose two gene selection criteria, and we apply them to two microarray datasets and three classification tasks for illustration.
NASA Astrophysics Data System (ADS)
Elnasir, Selma; Shamsuddin, Siti Mariyam; Farokhi, Sajad
2015-01-01
Palm vein recognition (PVR) is a promising new biometric that has been applied successfully as a method of access control by many organizations, which has even further potential in the field of forensics. The palm vein pattern has highly discriminative features that are difficult to forge because of its subcutaneous position in the palm. Despite considerable progress and a few practical issues, providing accurate palm vein readings has remained an unsolved issue in biometrics. We propose a robust and more accurate PVR method based on the combination of wavelet scattering (WS) with spectral regression kernel discriminant analysis (SRKDA). As the dimension of WS generated features is quite large, SRKDA is required to reduce the extracted features to enhance the discrimination. The results based on two public databases-PolyU Hyper Spectral Palmprint public database and PolyU Multi Spectral Palmprint-show the high performance of the proposed scheme in comparison with state-of-the-art methods. The proposed approach scored a 99.44% identification rate and a 99.90% verification rate [equal error rate (EER)=0.1%] for the hyperspectral database and a 99.97% identification rate and a 99.98% verification rate (EER=0.019%) for the multispectral database.
Brooks, S P; Suelter, C H
1986-09-01
An IBM computer program, WILMAN4, is described which calculates the estimates, Km, V and Km/V from initial velocity measurements according to one of four statistical methods. Three of these methods involve linear regression analysis using weights given by assuming: (i) constant absolute error (G.N. Wilkinson, 1961, Biochem J., 80, 324-332), (ii) constant relative error (G. Johansen and R. Lumry, 1961, C.R. Trav. Lab. Carlsberg, 32, 185-214) and (iii) an error function in between the above two cases. (A. Cornish-Bowden, 1976, Principles of Enzyme Kinetics, Butterworths Inc, Boston, Mass., pp. 168-193). The fourth method is a non-parametric procedure derived by Eisenthal and Cornish-Bowden (Biochim. Biophys. Acta, 532 (1974) 268-272). Residuals are obtained by subtracting the experimental and the calculated velocities. Outliers, or residuals which are greater than two experimental standard deviations, can be identified and removed from the data set. If the sequence of positive and negative signs of the residuals is random as determined by a statistical probability calculation, the data set is assumed to obey the Michaelis-Menten equation.
Multiple regression analysis in modeling of columnar ozone in Peninsular Malaysia.
Tan, K C; Lim, H S; Mat Jafri, M Z
2014-06-01
This study aimed to predict monthly columnar ozone (O3) in Peninsular Malaysia by using data on the concentration of environmental pollutants. Data (2003-2008) on five atmospheric pollutant gases (CO2, O3, CH4, NO2, and H2O vapor) retrieved from the satellite Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) were employed to develop a model that predicts columnar ozone through multiple linear regression. In the entire period, the pollutants were highly correlated (R = 0.811 for the southwest monsoon, R = 0.803 for the northeast monsoon) with predicted columnar ozone. The results of the validation of columnar ozone with column ozone from SCIAMACHY showed a high correlation coefficient (R = 0.752-0.802), indicating the model's accuracy and efficiency. Statistical analysis was utilized to determine the effects of each atmospheric pollutant on columnar ozone. A model that can retrieve columnar ozone in Peninsular Malaysia was developed to provide air quality information. These results are encouraging and accurate and can be used in early warning of the population to comply with air quality standards.
Uchimoto, Takeaki; Iwao, Yasunori; Hattori, Hiroaki; Noguchi, Shuji; Itai, Shigeru
2013-01-01
The interaction of the effects of the triglycerin full behenate (TR-FB) concentration and the mixing time on lubrication and tablet properties were analyzed under a two-factor central composite design, and compared with those of magnesium stearate (Mg-St). Various amounts of lubricant (0.07-3.0%) were added to granules and mixed for 1-30 min. A multiple linear regression analysis was performed to identify the effect of the mixing conditions on each physicochemical property. The mixing conditions did not significantly affect the lubrication properties of TR-FB. For tablet properties, tensile strength decreased and disintegration time increased when the lubricant concentration and the mixing time were increased for Mg-St. The direct interaction of the Mg-St concentration and the mixing time had a significant negative effect on the disintegration time. In contrast, any mixing conditions of TR-FB did not affect the tablet properties. In addition, the range of mixing conditions which satisfied the lubrication and tablet property criteria was broader for TR-FB than that for Mg-St, suggesting that TR-FB allows tablets with high quality attributes to be produced consistently. Therefore, TR-FB is a potential lubricant alternative to Mg-St.
Dai, Wensheng
2014-01-01
Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting. PMID:25165740
Polanczyk, Guilherme V; Willcutt, Erik G; Salum, Giovanni A; Kieling, Christian; Rohde, Luis A
2014-01-01
Background: Previous studies have identified significant variability in attention-deficit / hyperactivity disorder (ADHD) prevalence estimates worldwide, largely explained by methodological procedures. However, increasing rates of ADHD diagnosis and treatment throughout the past few decades have fuelled concerns about whether the true prevalence of the disorder has increased over time. Methods: We updated the two most comprehensive systematic reviews on ADHD prevalence available in the literature. Meta-regression analyses were conducted to test the effect of year of study in the context of both methodological variables that determined variability in ADHD prevalence (diagnostic criteria, impairment criterion and source of information), and the geographical location of studies. Results: We identified 154 original studies and included 135 in the multivariate analysis. Methodological procedures investigated were significantly associated with heterogeneity of studies. Geographical location and year of study were not associated with variability in ADHD prevalence estimates. Conclusions: Confirming previous findings, variability in ADHD prevalence estimates is mostly explained by methodological characteristics of the studies. In the past three decades, there has been no evidence to suggest an increase in the number of children in the community who meet criteria for ADHD when standardized diagnostic procedures are followed. PMID:24464188
Tahsin, Subrina; Chang, Ni-Bin
2016-02-01
Stormwater wet detention ponds have been a commonly employed best management practice for stormwater management throughout the world for many years. In the past, the trophic state index values have been used to evaluate seasonal changes in water quality and rank lakes within a region or between several regions; yet, to date, there is no similar index for stormwater wet detention ponds. This study aimed to develop a new multivariate trophic state index (MTSI) suitable for conducting a rapid eutrophication assessment of stormwater wet detention ponds under uncertainty with respect to three typical physical and chemical properties. Six stormwater wet detention ponds in Florida were selected for demonstration of the new MTSI with respect to total phosphorus (TP), total nitrogen (TN), and Secchi disk depth (SDD) as cognitive assessment metrics to sense eutrophication potential collectively and inform the environmental impact holistically. Due to the involvement of multiple endogenous variables (i.e., TN, TP, and SDD) for the eutrophication assessment simultaneously under uncertainty, fuzzy synthetic evaluation was applied to first standardize and synchronize the sources of uncertainty in the decision analysis. The ordered probit regression model was then formulated for assessment based on the concept of MTSI with the inputs from the fuzzy synthetic evaluation. It is indicative that the severe eutrophication condition is present during fall, which might be due to frequent heavy summer storm events contributing to high-nutrient inputs in these six ponds.
A systematic review and meta-regression analysis of mivacurium for tracheal intubation.
Vanlinthout, L E H; Mesfin, S H; Hens, N; Vanacker, B F; Robertson, E N; Booij, L H D J
2014-12-01
We systematically reviewed factors associated with intubation conditions in randomised controlled trials of mivacurium, using random-effects meta-regression analysis. We included 29 studies of 1050 healthy participants. Four factors explained 72.9% of the variation in the probability of excellent intubation conditions: mivacurium dose, 24.4%; opioid use, 29.9%; time to intubation and age together, 18.6%. The odds ratio (95% CI) for excellent intubation was 3.14 (1.65-5.73) for doubling the mivacurium dose, 5.99 (2.14-15.18) for adding opioids to the intubation sequence, and 6.55 (6.01-7.74) for increasing the delay between mivacurium injection and airway insertion from 1 to 2 min in subjects aged 25 years and 2.17 (2.01-2.69) for subjects aged 70 years, p < 0.001 for all. We conclude that good conditions for tracheal intubation are more likely by delaying laryngoscopy after injecting a higher dose of mivacurium with an opioid, particularly in older people.
Trends in Suicide Mortality Rates for Turkey from 1987 to 2011: A Joinpoint Regression Analysis.
Dogan, Nurhan; Toprak, Dilek
2015-06-01
Suicide is among the top 20 leading causes of death globally in all age groups and it is still a significant social and public health problem. Data on suicide deaths in 1987-2011 were extracted from the Turkish Statistical Institute mortality dataset based on ICD-9 and ICD-10 codes. The temporal trend in age-standardized suicide rates was tested for age, gender and methods using Joinpoint Regression Analysis. The average of age-standardized suicide rates of the period 1987-2011 were 3.08 per 100,000 people, 3.95 for male and 2.21 for female. Significant increases were observed in males in all age groups, but no significant changes were observed in females over the age of 45. The most common methods of suicide among people who live in Turkey were hanging, poisoning, firearms and jumping. High-risk groups could benefit from targeted strategies of suicide prevention. To understand the important inﬂuences on suicide risk in different age groups, future studies must investigate the experiences of older and younger individuals separately.
Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa
2015-11-03
Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.
Effect of acute hypoxia on cognition: A systematic review and meta-regression analysis.
McMorris, Terry; Hale, Beverley J; Barwood, Martin; Costello, Joseph; Corbett, Jo
2017-03-01
A systematic meta-regression analysis of the effects of acute hypoxia on the performance of central executive and non-executive tasks, and the effects of the moderating variables, arterial partial pressure of oxygen (PaO2) and hypobaric versus normobaric hypoxia, was undertaken. Studies were included if they were performed on healthy humans; within-subject design was used; data were reported giving the PaO2 or that allowed the PaO2 to be estimated (e.g. arterial oxygen saturation and/or altitude); and the duration of being in a hypoxic state prior to cognitive testing was ≤6days. Twenty-two experiments met the criteria for inclusion and demonstrated a moderate, negative mean effect size (g=-0.49, 95% CI -0.64 to -0.34, p<0.001). There were no significant differences between central executive and non-executive, perception/attention and short-term memory, tasks. Low (35-60mmHg) PaO2 was the key predictor of cognitive performance (R(2)=0.45, p<0.001) and this was independent of whether the exposure was in hypobaric hypoxic or normobaric hypoxic conditions.
Power Law Regression Analysis of Heat Flux Width in Type I ELMs
NASA Astrophysics Data System (ADS)
Stephens, C. D.; Makowski, M. A.; Leonard, A. W.; Osborne, T. H.
2014-10-01
In this project, a database of Type I ELM characteristics has been assembled and will be used to investigate possible dependencies of the heat flux width on physics and engineering parameters. At the edge near the divertor, high impulsive heat loads are imparted onto the surface. The impact of these ELMs can cause a reduction in divertor lifetime if the heat flux is great enough due to material erosion. A program will be used to analyze data, extract relevant, measurable quantities, and record the quantities in the table. Care is taken to accurately capture the complex space/time structure of the ELM. Then correlations between discharge and equilibrium parameters will be investigated. Power law regression analysis will be used to help determine the dependence of the heat flux width on these various measurable quantities and parameters. This will enable us to better understand the physics of heat flux at the edge. Work supported in part by the National Undergraduate Fellowship Program in Plasma Physics and Fusion Energy Sciences and the US DOE under DE-FG02-04ER54761, DE-AC52-07NA27344, DE-FC02-04ER54698.
Tanboğa, Ibrahim Halil; Kurt, Mustafa; Işik, Turgay; Kaya, Ahmet; Ekinci, Mehmet; Aksakal, Enbiya; Sevimli, Serdar; Cayli, Murat
2012-03-01
We aimed to assess the use and reporting-quality of multivariate logistic regression analysis (MVLRA) in articles published in two Turkish cardiology journals. We reviewed all original articles published in two Turkish cardiology journals (The Anatolian Journal of Cardiology and Archives of the Turkish Society of Cardiology) between January 2010 and August 2011. The articles that used MVLRA were analyzed comprehensively based on 10 predefined criteria. A total of 212 articles were reviewed, of which MVLRA was used in 33 (15.6%). Twenty-nine articles (87.9%) properly included the main components of the MVRLA, namely, odds ratios, 95% confidence intervals, and p values. However, none of the articles reported MVRLA-related data such as the modeling type, validation, goodness-fit, multicollinearity and interaction tests. There were severe reporting flaws and faults as to the ratio of the total number of events or sample size to the number of independent variables included into the MVLRA model, the use of fitness procedures, and how the independent variables were selected. Our results indicate that MVLRA has become a standard statistical method in the Turkish cardiology literature. However, overall reporting of MVLRA data still has seriously inadequate and inaccurate aspects.
Ponsoda, Vicente; Martínez, Kenia; Pineda-Pardo, José A; Abad, Francisco J; Olea, Julio; Román, Francisco J; Barbey, Aron K; Colom, Roberto
2017-02-01
Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp 38:803-816, 2017. © 2016 Wiley Periodicals, Inc.
Dai, Wensheng; Wu, Jui-Yu; Lu, Chi-Jie
2014-01-01
Sales forecasting is one of the most important issues in managing information technology (IT) chain store sales since an IT chain store has many branches. Integrating feature extraction method and prediction tool, such as support vector regression (SVR), is a useful method for constructing an effective sales forecasting scheme. Independent component analysis (ICA) is a novel feature extraction technique and has been widely applied to deal with various forecasting problems. But, up to now, only the basic ICA method (i.e., temporal ICA model) was applied to sale forecasting problem. In this paper, we utilize three different ICA methods including spatial ICA (sICA), temporal ICA (tICA), and spatiotemporal ICA (stICA) to extract features from the sales data and compare their performance in sales forecasting of IT chain store. Experimental results from a real sales data show that the sales forecasting scheme by integrating stICA and SVR outperforms the comparison models in terms of forecasting error. The stICA is a promising tool for extracting effective features from branch sales data and the extracted features can improve the prediction performance of SVR for sales forecasting.
Psychosocial variables and time to injury onset: a hurdle regression analysis model.
Sibold, Jeremy; Zizzi, Samuel
2012-01-01
Psychological variables have been shown to be related to athletic injury and time missed from participation in sport. We are unaware of any empirical examination of the influence of psychological variables on time to onset of injury. To examine the influence of orthopaedic and psychosocial variables on time to injury in college athletes. One hundred seventy-seven (men 5 116, women 5 61; age 5 19.45 6 1.39 years) National Collegiate Athletic Association Division II athletes. Hurdle regression analysis (HRA) was used to determine the influence of predictor variables on days to first injury. Worry (z = 2.98, P = .003), concentration disruption (z = -3.95, P < .001), and negative life-event stress (z = 5.02, P < .001) were robust predictors of days to injury. Orthopaedic risk score was not a predictor (z = 1.28, P = .20). These findings support previous research on the stress-injury relationship, and our group is the first to use HRA in athletic injury data. These data support the addition of psychological screening as part of preseason health examinations for collegiate athletes.
Psychosocial Variables and Time to Injury Onset: A Hurdle Regression Analysis Model
Sibold, Jeremy; Zizzi, Samuel
2012-01-01
Context: Psychological variables have been shown to be related to athletic injury and time missed from participation in sport. We are unaware of any empirical examination of the influence of psychological variables on time to onset of injury. Objective: To examine the influence of orthopaedic and psychosocial variables on time to injury in college athletes. Patients or Other Participants: One hundred seventy-seven (men = 116, women = 61; age = 19.45 ± 1.39 years) National Collegiate Athletic Association Division II athletes. Main Outcome Measure(s): Hurdle regression analysis (HRA) was used to determine the influence of predictor variables on days to first injury. Results: Worry (z = 2.98, P = .003), concentration disruption (z = −3.95, P < .001), and negative life-event stress (z = 5.02, P < .001) were robust predictors of days to injury. Orthopaedic risk score was not a predictor (z = 1.28, P = .20). Conclusions: These findings support previous research on the stress-injury relationship, and our group is the first to use HRA in athletic injury data. These data support the addition of psychological screening as part of preseason health examinations for collegiate athletes. PMID:23068591
Predicting pesticide removal efficacy of vegetated filter strips: A meta-regression analysis.
Chen, Huajin; Grieneisen, Michael L; Zhang, Minghua
2016-04-01
Vegetated Filter Strips (VFS's) are widely used for alleviating agricultural pesticide loadings to surface water bodies. However, effective tools are lacking to quantify the performance of VFS's in reducing off-site pesticide transport. In this study, we applied meta-regression to develop a model for predicting VFS pesticide retention efficiency based on hydrologic responses of VFS's, incoming pollutant characteristics and the interaction within and between these two factor groups (R(2)=0.83). In cross-validation analysis, our model (Q(2)=0.81) outperformed the existing pesticide retention module of VFSMOD (Q(2)=0.72) by explicitly accounting for interaction effect and the categorical effect of pesticide adsorption properties. Based on the 181 data points studied, infiltration had a leading, positive influence on pesticide retention, followed by sedimentation and interaction between the two. Interaction between infiltration and pesticide adsorption properties was also prominent, as the influence of infiltration was significantly lower for strongly adsorbed pesticides. In addition, the clay content of incoming sediment was negatively associated with pesticide retention. Our model is not only valuable in predicting VFS performance, but also provides a quantitative characterization of the interacting VFS processes, thereby facilitating a deeper understanding of the underlying mechanisms.
Tipayamongkholgul, Mathuros; Lisakulruk, Sunisa
2011-05-01
Focusing on the socio-geographical factors that influence local vulnerability to dengue at the village level, spatial regression methods were applied to analyse, over a 5-year period, the village-specific, cumulative incidence of all reported dengue cases among 437 villages in Prachuap Khiri Khan, a semi-urban province of Thailand. The K-order nearest neighbour method was used to define the range of neighbourhoods. Analysis showed a significant neighbourhood effect (ρ = 0.405, P <0.001), which implies that villages with geographical proximity shared a similar level of vulnerability to dengue. The two independent social factors, associated with a higher incidence of dengue, were a shorter distance to the nearest urban area (β = -0.133, P <0.05) and a smaller average family size (β = -0.102, P <0.05). These results indicate that the trend of increasing dengue occurrence in rural Thailand arose in areas under stronger urban influence rather than in remote rural areas.
Wong, Y Joel; Owen, Jesse; Shea, Munyi
2012-01-01
How are specific dimensions of masculinity related to psychological distress in specific groups of men? To address this question, the authors used latent class regression to assess the optimal number of latent classes that explained differential relationships between conformity to masculine norms and psychological distress in a racially diverse sample of 223 men. The authors identified a 2-class solution. Both latent classes demonstrated very different associations between conformity to masculine norms and psychological distress. In Class 1 (labeled risk avoiders; n = 133), conformity to the masculine norm of risk-taking was negatively related to psychological distress. In Class 2 (labeled detached risk-takers; n = 90), conformity to the masculine norms of playboy, self-reliance, and risk-taking was positively related to psychological distress, whereas conformity to the masculine norm of violence was negatively related to psychological distress. A post hoc analysis revealed that younger men and Asian American men (compared with Latino and White American men) had significantly greater odds of being in Class 2 versus Class 1. The implications of these findings for future research and clinical practice are examined. (c) 2012 APA, all rights reserved.
Wagner, Philippe; Ghith, Nermin; Leckie, George
2016-01-01
Background and Aim Many multilevel logistic regression analyses of “neighbourhood and health” focus on interpreting measures of associations (e.g., odds ratio, OR). In contrast, multilevel analysis of variance is rarely considered. We propose an original stepwise analytical approach that distinguishes between “specific” (measures of association) and “general” (measures of variance) contextual effects. Performing two empirical examples we illustrate the methodology, interpret the results and discuss the implications of this kind of analysis in public health. Methods We analyse 43,291 individuals residing in 218 neighbourhoods in the city of Malmö, Sweden in 2006. We study two individual outcomes (psychotropic drug use and choice of private vs. public general practitioner, GP) for which the relative importance of neighbourhood as a source of individual variation differs substantially. In Step 1 of the analysis, we evaluate the OR and the area under the receiver operating characteristic (AUC) curve for individual-level covariates (i.e., age, sex and individual low income). In Step 2, we assess general contextual effects using the AUC. Finally, in Step 3 the OR for a specific neighbourhood characteristic (i.e., neighbourhood income) is interpreted jointly with the proportional change in variance (i.e., PCV) and the proportion of ORs in the opposite direction (POOR) statistics. Results For both outcomes, information on individual characteristics (Step 1) provide a low discriminatory accuracy (AUC = 0.616 for psychotropic drugs; = 0.600 for choosing a private GP). Accounting for neighbourhood of residence (Step 2) only improved the AUC for choosing a private GP (+0.295 units). High neighbourhood income (Step 3) was strongly associated to choosing a private GP (OR = 3.50) but the PCV was only 11% and the POOR 33%. Conclusion Applying an innovative stepwise multilevel analysis, we observed that, in Malmö, the neighbourhood context per se had a negligible
Choi, Seung Hoan; Labadorf, Adam T; Myers, Richard H; Lunetta, Kathryn L; Dupuis, Josée; DeStefano, Anita L
2017-02-06
Next generation sequencing provides a count of RNA molecules in the form of short reads, yielding discrete, often highly non-normally distributed gene expression measurements. Although Negative Binomial (NB) regression has been generally accepted in the analysis of RNA sequencing (RNA-Seq) data, its appropriateness has not been exhaustively evaluated. We explore logistic regression as an alternative method for RNA-Seq studies designed to compare cases and controls, where disease status is modeled as a function of RNA-Seq reads using simulated and Huntington disease data. We evaluate the effect of adjusting for covariates that have an unknown relationship with gene expression. Finally, we incorporate the data adaptive method in order to compare false positive rates. When the sample size is small or the expression levels of a gene are highly dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes logistic (BL) regressions are conservative. Firth's logistic (FL) regression performs well or is slightly conservative. Large sample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha levels of 0.05 and 0.01. However, Type-I error rates are controlled after applying the data adaptive method. The NB, BL, and FL regressions gain increased power with large sample size, large log2 fold-change, and low dispersion. The FL regression has comparable power to NB regression. We conclude that implementing the data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis. Firth's logistic regression provides a concise statistical inference process and reduces spurious associations from inaccurately estimated dispersion parameters in the negative binomial framework.
Saadah, Nicholas H; van Hout, Fabienne M A; Schipperus, Martin R; le Cessie, Saskia; Middelburg, Rutger A; Wiersum-Osselton, Johanna C; van der Bom, Johanna G
2017-09-01
We estimated rates for common plasma-associated transfusion reactions and compared reported rates for various plasma types. We performed a systematic review and meta-analysis of peer-reviewed articles that reported plasma transfusion reaction rates. Random-effects pooled rates were calculated and compared between plasma types. Meta-regression was used to compare various plasma types with regard to their reported plasma transfusion reaction rates. Forty-eight studies reported transfusion reaction rates for fresh-frozen plasma (FFP; mixed-sex and male-only), amotosalen INTERCEPT FFP, methylene blue-treated FFP, and solvent/detergent-treated pooled plasma. Random-effects pooled average rates for FFP were: allergic reactions, 92/10(5) units transfused (95% confidence interval [CI], 46-184/10(5) units transfused); febrile nonhemolytic transfusion reactions (FNHTRs), 12/10(5) units transfused (95% CI, 7-22/10(5) units transfused); transfusion-associated circulatory overload (TACO), 6/10(5) units transfused (95% CI, 1-30/10(5) units transfused); transfusion-related acute lung injury (TRALI), 1.8/10(5) units transfused (95% CI, 1.2-2.7/10(5) units transfused); and anaphylactic reactions, 0.8/10(5) units transfused (95% CI, 0-45.7/10(5) units transfused). Risk differences between plasma types were not significant for allergic reactions, TACO, or anaphylactic reactions. Methylene blue-treated FFP led to fewer FNHTRs than FFP (risk difference = -15.3 FNHTRs/10(5) units transfused; 95% CI, -24.7 to -7.1 reactions/10(5) units transfused); and male-only FFP led to fewer cases of TRALI than mixed-sex FFP (risk difference = -0.74 TRALI/10(5) units transfused; 95% CI, -2.42 to -0.42 injuries/10(5) units transfused). Meta-regression demonstrates that the rate of FNHTRs is lower for methylene blue-treated compared with FFP, and the rate of TRALI is lower for male-only than for mixed-sex FFP; whereas no significant differences are observed between plasma types for allergic
Eckert, Laurent; Lançon, Christophe
2006-01-01
Background Data comparing duloxetine with existing antidepressant treatments is limited. A comparison of duloxetine with fluoxetine has been performed but no comparison with venlafaxine, the other antidepressant in the same therapeutic class with a significant market share, has been undertaken. In the absence of relevant data to assess the place that duloxetine should occupy in the therapeutic arsenal, indirect comparisons are the most rigorous way to go. We conducted a systematic review of the efficacy of duloxetine, fluoxetine and venlafaxine versus placebo in the treatment of Major Depressive Disorder (MDD), and performed indirect comparisons through meta-regressions. Methods The bibliography of the Agency for Health Care Policy and Research and the CENTRAL, Medline, and Embase databases were interrogated using advanced search strategies based on a combination of text and index terms. The search focused on randomized placebo-controlled clinical trials involving adult patients treated for acute phase Major Depressive Disorder. All outcomes were derived to take account for varying placebo responses throughout studies. Primary outcome was treatment efficacy as measured by Hedge's g effect size. Secondary outcomes were response and dropout rates as measured by log odds ratios. Meta-regressions were run to indirectly compare the drugs. Sensitivity analysis, assessing the influence of individual studies over the results, and the influence of patients' characteristics were run. Results 22 studies involving fluoxetine, 9 involving duloxetine and 8 involving venlafaxine were selected. Using indirect comparison methodology, estimated effect sizes for efficacy compared with duloxetine were 0.11 [-0.14;0.36] for fluoxetine and 0.22 [0.06;0.38] for venlafaxine. Response log odds ratios were -0.21 [-0.44;0.03], 0.70 [0.26;1.14]. Dropout log odds ratios were -0.02 [-0.33;0.29], 0.21 [-0.13;0.55]. Sensitivity analyses showed that results were consistent. Conclusion Fluoxetine
NASA Astrophysics Data System (ADS)
Gizaw, Mesgana Seyoum; Gan, Thian Yew
2016-07-01
Regional Flood Frequency Analysis (RFFA) is a statistical method widely used to estimate flood quantiles of catchments with limited streamflow data. In addition, to estimate the flood quantile of ungauged sites, there could be only a limited number of stations with complete dataset are available from hydrologically similar, surrounding catchments. Besides traditional regression based RFFA methods, recent applications of machine learning algorithms such as the artificial neural network (ANN) have shown encouraging results in regional flood quantile estimations. Another novel machine learning technique that is becoming widely applicable in the hydrologic community is the Support Vector Regression (SVR). In this study, an RFFA model based on SVR was developed to estimate regional flood quantiles for two study areas, one with 26 catchments located in southeastern British Columbia (BC) and another with 23 catchments located in southern Ontario (ON), Canada. The SVR-RFFA model for both study sites was developed from 13 sets of physiographic and climatic predictors for the historical period. The Ef (Nash Sutcliffe coefficient) and R2 of the SVR-RFFA model was about 0.7 when estimating flood quantiles of 10, 25, 50 and 100 year return periods which indicate satisfactory model performance in both study areas. In addition, the SVR-RFFA model also performed well based on other goodness-of-fit statistics such as BIAS (mean bias) and BIASr (relative BIAS). If the amount of data available for training RFFA models is limited, the SVR-RFFA model was found to perform better than an ANN based RFFA model, and with significantly lower median CV (coefficient of variation) of the estimated flood quantiles. The SVR-RFFA model was then used to project changes in flood quantiles over the two study areas under the impact of climate change using the RCP4.5 and RCP8.5 climate projections of five Coupled Model Intercomparison Project (CMIP5) GCMs (Global Climate Models) for the 2041
NASA Astrophysics Data System (ADS)
Pradhan, Biswajeet
Recently, in 2006 and 2007 heavy monsoons rainfall have triggered floods along Malaysia's east coast as well as in southern state of Johor. The hardest hit areas are along the east coast of peninsular Malaysia in the states of Kelantan, Terengganu and Pahang. The city of Johor was particularly hard hit in southern side. The flood cost nearly billion ringgit of property and many lives. The extent of damage could have been reduced or minimized if an early warning system would have been in place. This paper deals with flood susceptibility analysis using logistic regression model. We have evaluated the flood susceptibility and the effect of flood-related factors along the Kelantan river basin using the Geographic Information System (GIS) and remote sensing data. Previous flooded areas were extracted from archived radarsat images using image processing tools. Flood susceptibility mapping was conducted in the study area along the Kelantan River using radarsat imagery and then enlarged to 1:25,000 scales. Topographical, hydrological, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence flood occurrence were: topographic slope, topographic aspect, topographic curvature, DEM and distance from river drainage, all from the topographic database; flow direction, flow accumulation, extracted from hydrological database; geology and distance from lineament, taken from the geologic database; land use from SPOT satellite images; soil texture from soil database; and the vegetation index value from SPOT satellite images. Flood susceptible areas were analyzed and mapped using the probability-logistic regression model. Results indicate that flood prone areas can be performed at 1:25,000 which is comparable to some conventional flood hazard map scales. The flood prone areas delineated on these maps correspond to areas that would be inundated by significant flooding
A Bayesian ridge regression analysis of congestion's impact on urban expressway safety.
Shi, Qi; Abdel-Aty, Mohamed; Lee, Jaeyoung
2016-03-01
With the rapid growth of traffic in urban areas, concerns about congestion and traffic safety have been heightened. This study leveraged both Automatic Vehicle Identification (AVI) system and Microwave Vehicle Detection System (MVDS) installed on an expressway in Central Florida to explore how congestion impacts the crash occurrence in urban areas. Multiple congestion measures from the two systems were developed. To ensure more precise estimates of the congestion's effects, the traffic data were aggregated into peak and non-peak hours. Multicollinearity among traffic parameters was examined. The results showed the presence of multicollinearity especially during peak hours. As a response, ridge regression was introduced to cope with this issue. Poisson models with uncorrelated random effects, correlated random effects, and both correlated random effects and random parameters were constructed within the Bayesian framework. It was proven that correlated random effects could significantly enhance model performance. The random parameters model has similar goodness-of-fit compared with the model with only correlated random effects. However, by accounting for the unobserved heterogeneity, more variables were found to be significantly related to crash frequency. The models indicated that congestion increased crash frequency during peak hours while during non-peak hours it was not a major crash contributing factor. Using the random parameter model, the three congestion measures were compared. It was found that all congestion indicators had similar effects while Congestion Index (CI) derived from MVDS data was a better congestion indicator for safety analysis. Also, analyses showed that the segments with higher congestion intensity could not only increase property damage only (PDO) crashes, but also more severe crashes. In addition, the issues regarding the necessity to incorporate specific congestion indicator for congestion's effects on safety and to take care of the
Byers, John A
2013-08-01
Dose-response curves of the effects of semiochemicals on neurophysiology and behavior are reported in many articles in insect chemical ecology. Most curves are shown in figures representing points connected by straight lines, in which the x-axis has order of magnitude increases in dosage vs. responses on the y-axis. The lack of regression curves indicates that the nature of the dose-response relationship is not well understood. Thus, a computer model was developed to simulate a flux of various numbers of pheromone molecules (10(3) to 5 × 10(6)) passing by 10(4) receptors distributed among 10(6) positions along an insect antenna. Each receptor was depolarized by at least one strike by a molecule, and subsequent strikes had no additional effect. The simulations showed that with an increase in pheromone release rate, the antennal response would increase in a convex fashion and not in a logarithmic relation as suggested previously. Non-linear regression showed that a family of kinetic formation functions fit the simulated data nearly perfectly (R(2) >0.999). This is reasonable because olfactory receptors have proteins that bind to the pheromone molecule and are expected to exhibit enzyme kinetics. Over 90 dose-response relationships reported in the literature of electroantennographic and behavioral bioassays in the laboratory and field were analyzed by the logarithmic and kinetic formation functions. This analysis showed that in 95% of the cases, the kinetic functions explained the relationships better than the logarithmic (mean of about 20% better). The kinetic curves become sigmoid when graphed on a log scale on the x-axis. Dose-catch relationships in the field are similar to dose-EAR (effective attraction radius, in which a spherical radius indicates the trapping effect of a lure) and the circular EARc in two dimensions used in mass trapping models. The use of kinetic formation functions for dose-response curves of attractants, and kinetic decay curves for
López-Campos, Jose Luis; Ruiz-Ramos, Miguel; Soriano, Joan B
2014-01-01
Findings from studies done over the past 20 years suggest that mortality from chronic obstructive pulmonary disease (COPD) is decreasing worldwide, but little information is available for trends in Europe. We aimed to describe COPD mortality trends by sex and calendar year for the period of 1994 to 2010. We extracted data for COPD deaths between 1994 and 2010 in the 27 countries in the European Union (EU) from the statistical office of the EU (Eurostat), using the International Classification of Diseases 10 (ICD-10) codes J40-J44 and J47. We estimated age-standardised mortality rates (ASR), and analysed data using joinpoint regression, for women and men in the EU overall and by individual country for each year. We used the standard European population as the reference and present our findings as deaths per 100,000 person-years. We compared findings for each country with the EU average by calculating standardised rate ratios (SRR) and 95% CIs. Between 1994 and 2010, there were 2,348,184 recorded COPD deaths in the EU. COPD mortality was higher in men than in women throughout the study period in all EU countries. In the EU overall, deaths per 100,000 population decreased in men almost linearly from 90·07 in 1994 to 61·33 in 2010, and in women from 26·99 in 1994 to 25·15 in 2010, representing a narrowing in gender gap over the study period. Several countries had a higher SRR mortality than the EU average-eg, Ireland, Hungary, and Belgium for men and Denmark, the UK, and the Netherlands for women. Our joinpoint regression analysis identified no statistically significant changes in the trend for the whole EU, but several countries had changing trends over the study period. In men, we recorded a 2·56% constant and statistically significant decrease in ASRs in the EU. Five countries had an increase in ASR. Overall, in women, we recorded a 0·76% statistically significant decrease in ASRs. 14 countries had an increase in ASR. Our findings indicate a downward trend in
Pineda, Silvia; Real, Francisco X; Kogevinas, Manolis; Carrato, Alfredo; Chanock, Stephen J; Malats, Núria; Van Steen, Kristel
2015-12-01
Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we propose a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. This was applied with penalized regression methods (LASSO and ENET) when exploring relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whose expression levels were significantly associated with both SNPs and CPGs. Importantly, 36 (75%) of them were replicated in an independent data set (TCGA) and the performance of the proposed method was checked with a simulation study. We further support our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexible and easy to implement when analyzing several types of omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complex genetic mechanisms involved in disease
Nakasone, Yutaka Ikeda, Osamu; Yamashita, Yasuyuki; Kudoh, Kouichi; Shigematsu, Yoshinori; Harada, Kazunori
2007-09-15
We applied multivariate analysis to the clinical findings in patients with acute gastrointestinal (GI) hemorrhage and compared the relationship between these findings and angiographic evidence of extravasation. Our study population consisted of 46 patients with acute GI bleeding. They were divided into two groups. In group 1 we retrospectively analyzed 41 angiograms obtained in 29 patients (age range, 25-91 years; average, 71 years). Their clinical findings including the shock index (SI), diastolic blood pressure, hemoglobin, platelet counts, and age, which were quantitatively analyzed. In group 2, consisting of 17 patients (age range, 21-78 years; average, 60 years), we prospectively applied statistical analysis by a logistics regression model to their clinical findings and then assessed 21 angiograms obtained in these patients to determine whether our model was useful for predicting the presence of angiographic evidence of extravasation. On 18 of 41 (43.9%) angiograms in group 1 there was evidence of extravasation; in 3 patients it was demonstrated only by selective angiography. Factors significantly associated with angiographic visualization of extravasation were the SI and patient age. For differentiation between cases with and cases without angiographic evidence of extravasation, the maximum cutoff point was between 0.51 and 0.0.53. Of the 21 angiograms obtained in group 2, 13 (61.9%) showed evidence of extravasation; in 1 patient it was demonstrated only on selective angiograms. We found that in 90% of the cases, the prospective application of our model correctly predicted the angiographically confirmed presence or absence of extravasation. We conclude that in patients with GI hemorrhage, angiographic visualization of extravasation is associated with the pre-embolization SI. Patients with a high SI value should undergo study to facilitate optimal treatment planning.
Integrative analysis of multiple diverse omics datasets by sparse group multitask regression
Lin, Dongdong; Zhang, Jigang; Li, Jingyao; He, Hao; Deng, Hong-Wen; Wang, Yu-Ping
2014-01-01
A variety of high throughput genome-wide assays enable the exploration of genetic risk factors underlying complex traits. Although these studies have remarkable impact on identifying susceptible biomarkers, they suffer from issues such as limited sample size and low reproducibility. Combining individual studies of different genetic levels/platforms has the promise to improve the power and consistency of biomarker identification. In this paper, we propose a novel integrative method, namely sparse group multitask regression, for integrating diverse omics datasets, platforms, and populations to identify risk genes/factors of complex diseases. This method combines multitask learning with sparse group regularization, which will: (1) treat the biomarker identification in each single study as a task and then combine them by multitask learning; (2) group variables from all studies for identifying significant genes; (3) enforce sparse constraint on groups of variables to overcome the “small sample, but large variables” problem. We introduce two sparse group penalties: sparse group lasso and sparse group ridge in our multitask model, and provide an effective algorithm for each model. In addition, we propose a significance test for the identification of potential risk genes. Two simulation studies are performed to evaluate the performance of our integrative method by comparing it with conventional meta-analysis method. The results show that our sparse group multitask method outperforms meta-analysis method significantly. In an application to our osteoporosis studies, 7 genes are identified as significant genes by our method and are found to have significant effects in other three independent studies for validation. The most significant gene SOD2 has been identified in our previous osteoporosis study involving the same expression dataset. Several other genes such as TREML2, HTR1E, and GLO1 are shown to be novel susceptible genes for osteoporosis, as confirmed from other
Arenja, Nisha; Breidthardt, Tobias; Socrates, Thenral; Schindler, Christian; Heinisch, Corinna; Tschung, Christopher; Potocki, Mihael; Gualandro, Danielle; Mueller, Christian
2011-10-09
Simple tools for risk stratification of patients with acute heart failure (AHF) are an unmet clinical need, particularly regarding long-term mortality. We prospectively enrolled 610 consecutive patients presenting to the emergency department with AHF. The diagnosis of AHF was adjudicated by two independent cardiologists. The classification and regression tree (CART) analysis was used to develop a simple risk algorithm. This was internally validated by cross-validation. One-year follow-up was complete in all patients (100%). A total of 201 patients (33%) died within 360 days. The CART analysis identified blood urea nitrogen (BUN) and age as the best single predictors of 1-year mortality and patients were categorised to three risk groups: high risk group (BUN >27.5 mg/dl and age >86 years), intermediate risk group (BUN >27.5 mg/dl and age ≤ 86 years) and low risk group (BUN ≤ 27.5 mg/dl). The Kaplan-Meier curves showed a significant increase in mortality in the high risk group compared with the lower risk groups (log-rank test p <0.001). The hazard ratio regarding 1-year mortality between patients identified as low and high risk was 2.0 (95% confidence interval, 1.7-2.4), with statistically significant differences between all risk groups (p <0.001). The likelihood-based 95%-confidence set for the age- and the urea-threshold is contained in the rectangular set defined by 25 mg/dl ≤ urea threshold ≤30.6 mg/dl and 76 years ≤ age threshold ≤96 years. These results suggest that AHF patients at low, intermediate and high risk for death within 360 days can be easily identified using patient's demographics and laboratory data obtained at presentation. Application of this simple risk stratification algorithm may help to improve the management of these patients.
Lees, Mackenzie C.; Merani, Shaheed; Tauh, Keerit; Khadaroo, Rachel G.
2015-01-01
Background Older adults (≥ 65 yr) are the fastest growing population and are presenting in increasing numbers for acute surgical care. Emergency surgery is frequently life threatening for older patients. Our objective was to identify predictors of mortality and poor outcome among elderly patients undergoing emergency general surgery. Methods We conducted a retrospective cohort study of patients aged 65–80 years undergoing emergency general surgery between 2009 and 2010 at a tertiary care centre. Demographics, comorbidities, in-hospital complications, mortality and disposition characteristics of patients were collected. Logistic regression analysis was used to identify covariate-adjusted predictors of in-hospital mortality and discharge of patients home. Results Our analysis included 257 patients with a mean age of 72 years; 52% were men. In-hospital mortality was 12%. Mortality was associated with patients who had higher American Society of Anesthesiologists (ASA) class (odds ratio [OR] 3.85, 95% confidence interval [CI] 1.43–10.33, p = 0.008) and in-hospital complications (OR 1.93, 95% CI 1.32–2.83, p = 0.001). Nearly two-thirds of patients discharged home were younger (OR 0.92, 95% CI 0.85–0.99, p = 0.036), had lower ASA class (OR 0.45, 95% CI 0.27–0.74, p = 0.002) and fewer in-hospital complications (OR 0.69, 95% CI 0.53–0.90, p = 0.007). Conclusion American Society of Anesthesiologists class and in-hospital complications are perioperative predictors of mortality and disposition in the older surgical population. Understanding the predictors of poor outcome and the importance of preventing in-hospital complications in older patients will have important clinical utility in terms of preoperative counselling, improving health care and discharging patients home. PMID:26204143
2009-01-01
Background The central nervous system is considered a sanctuary site for HIV-1 replication. Variables associated with HIV cerebrospinal fluid (CSF) viral load in the context of opportunistic CNS infections are poorly understood. Our objective was to evaluate the relation between: (1) CSF HIV-1 viral load and CSF cytological and biochemical characteristics (leukocyte count, protein concentration, cryptococcal antigen titer); (2) CSF HIV-1 viral load and HIV-1 plasma viral load; and (3) CSF leukocyte count and the peripheral blood CD4+ T lymphocyte count. Methods Our approach was to use a prospective collection and analysis of pre-treatment, paired CSF and plasma samples from antiretroviral-naive HIV-positive patients with cryptococcal meningitis and assisted at the Francisco J Muñiz Hospital, Buenos Aires, Argentina (period: 2004 to 2006). We measured HIV CSF and plasma levels by polymerase chain reaction using the Cobas Amplicor HIV-1 Monitor Test version 1.5 (Roche). Data were processed with Statistix 7.0 software (linear regression analysis). Results Samples from 34 patients were analyzed. CSF leukocyte count showed statistically significant correlation with CSF HIV-1 viral load (r = 0.4, 95% CI = 0.13-0.63, p = 0.01). No correlation was found with the plasma viral load, CSF protein concentration and cryptococcal antigen titer. A positive correlation was found between peripheral blood CD4+ T lymphocyte count and the CSF leukocyte count (r = 0.44, 95% CI = 0.125-0.674, p = 0.0123). Conclusion Our study suggests that CSF leukocyte count influences CSF HIV-1 viral load in patients with meningitis caused by Cryptococcus neoformans.
Julien, Rhona; Levy, Jonathan I; Adamkiewicz, Gary; Hauser, Russ; Spengler, John D; Canales, Robert A; Hynes, H Patricia
2008-10-01
Many units in public housing or other low-income urban dwellings may have elevated pesticide residues, given recurring infestation, but it would be logistically and economically infeasible to sample a large number of units to identify highly exposed households to design interventions. Within this study, our aim was to devise a low-cost approach to identify homes in public housing with high levels of pesticide residues, using information that would allow the housing authority and residents to determine optimal strategies to reduce household exposures. As part of the Healthy Public Housing Initiative, we collected environmental samples from 42 public housing apartments in Boston, MA, in 2002 and 2003 and gathered housing characteristics; for example, household demographics and self-reported pesticide use information, considering information available with and without a home visit. Focusing on five organophosphate and pyrethroid pesticides, we used classification and regression tree analysis (CART) to disaggregate the pesticide concentration data into homogenous subsamples according to housing characteristics, which allowed us to identify households and associated networks impacted by the mismanagement of pesticides. The CART analysis demonstrated reasonable sensitivity and specificity given more extensive household information but generally poor performance using only information available without a home visit. Apartments with high concentrations of cyfluthrin, a pyrethroid of interest given that it is a restricted use pesticide, were more likely to be associated with Hispanic residents who resided in their current apartment for more than 5 yr, consistent with documented pesticide usage patterns. We conclude that using CART as an exploratory technique to better understand the home characteristics associated with elevated pesticide levels may be a viable approach for risk management in large multiunit housing developments.
Telfer, Scott; Lange, Moritz J; Sudduth, Amanda S M
2017-08-24
The external knee adduction moment has been identified as a key biomarker in biomechanics research, with associations with this variable and degenerative diseases such as knee osteoarthritis. Heterogeneity in participant characteristics and the protocols used to measure this variable may however complicate its interpretation. Previous reviews have focused on interventions or did not control for potential moderator variables in their analysis. In this meta-regression analysis, we aimed to determine the influence of factors including the cohort type, footwear, and walking speed on the measurement of knee adduction moment. We performed a systematic review of the literature, identifying articles that used the Plug-in-Gait inverse dynamics model to calculate the knee adduction moment during level walking, and used a mixed effect model to determine the effect of the previously described factors on the measurement. Results for 861 individuals were described in 19 articles. Walking speed had the largest influence on knee adduction moment (p<0.001), and participants with medial knee osteoarthritis had an increased knee adduction moment (p=0.008) compared to healthy subjects. Footwear was found to have a significant overall effect (p=0.024). Participants tested barefoot or wearing their own shoes had lower adduction moments than those tested in footwear provided by the researchers. Overall, the moderators accounted for 60% of the heterogeneity in the results. These results support the hypothesis that an increased knee adduction moment is associated with medial compartment knee osteoarthritis, and that footwear choice can influence the results. Gait speed has the largest effect on knee adduction moment measurement and should be carefully controlled for in studies investigating this variable. Copyright © 2017 Elsevier B.V. All rights reserved.
Determinants for changing the treatment of COPD: a regression analysis from a clinical audit
López-Campos, Jose Luis; Abad Arranz, María; Calero Acuña, Carmen; Romero Valero, Fernando; Ayerbe García, Ruth; Hidalgo Molina, Antonio; Aguilar Perez-Grovas, Ricardo I; García Gil, Francisco; Casas Maldonado, Francisco; Caballero Ballesteros, Laura; Sánchez Palop, María; Pérez-Tejero, Dolores; Segado, Alejandro; Calvo Bonachera, Jose; Hernández Sierra, Bárbara; Doménech, Adolfo; Arroyo Varela, Macarena; González Vargas, Francisco; Cruz Rueda, Juan J
2016-01-01
Introduction This study is an analysis of a pilot COPD clinical audit that evaluated adherence to guidelines for patients with COPD in a stable disease phase during a routine visit in specialized secondary care outpatient clinics in order to identify the variables associated with the decision to step-up or step-down pharmacological treatment. Methods This study was a pilot clinical audit performed at hospital outpatient respiratory clinics in the region of Andalusia, Spain (eight provinces with over eight million inhabitants), in which 20% of centers in the area (catchment population 3,143,086 inhabitants) were invited to participate. Treatment changes were evaluated in terms of the number of prescribed medications and were classified as step-up, step-down, or no change. Three backward stepwise binominal multivariate logistic regression analyses were conducted to evaluate variables associated with stepping up, stepping down, and inhaled corticosteroids discontinuation. Results The present analysis evaluated 565 clinical records (91%) of the complete audit. Of those records, 366 (64.8%) cases saw no change in pharmacological treatment, while 99 patients (17.5%) had an increase in the number of drugs, 55 (9.7%) had a decrease in the number of drugs, and 45 (8.0%) noted a change to other medication for a similar therapeutic scheme. Exacerbations were the main factor in stepping up treatment, as were the symptoms themselves. In contrast, rather than symptoms, doctors used forced expiratory volume in 1 second and previous treatment with long-term antibiotics or inhaled corticosteroids as the key determinants to stepping down treatment. Conclusion The majority of doctors did not change the prescription. When changes were made, a number of related factors were noted. Future trials must evaluate whether these therapeutic changes impact clinically relevant outcomes at follow-up. PMID:27330285
Nakasone, Yutaka; Ikeda, Osamu; Yamashita, Yasuyuki; Kudoh, Kouichi; Shigematsu, Yoshinori; Harada, Kazunori
2007-01-01
We applied multivariate analysis to the clinical findings in patients with acute gastrointestinal (GI) hemorrhage and compared the relationship between these findings and angiographic evidence of extravasation. Our study population consisted of 46 patients with acute GI bleeding. They were divided into two groups. In group 1 we retrospectively analyzed 41 angiograms obtained in 29 patients (age range, 25-91 years; average, 71 years). Their clinical findings including the shock index (SI), diastolic blood pressure, hemoglobin, platelet counts, and age, which were quantitatively analyzed. In group 2, consisting of 17 patients (age range, 21-78 years; average, 60 years), we prospectively applied statistical analysis by a logistics regression model to their clinical findings and then assessed 21 angiograms obtained in these patients to determine whether our model was useful for predicting the presence of angiographic evidence of extravasation. On 18 of 41 (43.9%) angiograms in group 1 there was evidence of extravasation; in 3 patients it was demonstrated only by selective angiography. Factors significantly associated with angiographic visualization of extravasation were the SI and patient age. For differentiation between cases with and cases without angiographic evidence of extravasation, the maximum cutoff point was between 0.51 and 0.0.53. Of the 21 angiograms obtained in group 2, 13 (61.9%) showed evidence of extravasation; in 1 patient it was demonstrated only on selective angiograms. We found that in 90% of the cases, the prospective application of our model correctly predicted the angiographically confirmed presence or absence of extravasation. We conclude that in patients with GI hemorrhage, angiographic visualization of extravasation is associated with the pre-embolization SI. Patients with a high SI value should undergo study to facilitate optimal treatment planning.
Regression Analysis of Top of Descent Location for Idle-thrust Descents
NASA Technical Reports Server (NTRS)
Stell, Laurel; Bronsvoort, Jesper; McDonald, Greg
2013-01-01
In this paper, multiple regression analysis is used to model the top of descent (TOD) location of user-preferred descent trajectories computed by the flight management system (FMS) on over 1000 commercial flights into Melbourne, Australia. The independent variables cruise altitude, final altitude, cruise Mach, descent speed, wind, and engine type were also recorded or computed post-operations. Both first-order and second-order models are considered, where cross-validation, hypothesis testing, and additional analysis are used to compare models. This identifies the models that should give the smallest errors if used to predict TOD location for new data in the future. A model that is linear in TOD altitude, final altitude, descent speed, and wind gives an estimated standard deviation of 3.9 nmi for TOD location given the trajec- tory parameters, which means about 80% of predictions would have error less than 5 nmi in absolute value. This accuracy is better than demonstrated by other ground automation predictions using kinetic models. Furthermore, this approach would enable online learning of the model. Additional data or further knowl- edge of algorithms is necessary to conclude definitively that no second-order terms are appropriate. Possible applications of the linear model are described, including enabling arriving aircraft to fly optimized descents computed by the FMS even in congested airspace. In particular, a model for TOD location that is linear in the independent variables would enable decision support tool human-machine interfaces for which a kinetic approach would be computationally too slow.
Dinç, Erdal; Ozdemir, Abdil
2005-01-01
Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.
The development of a flyover noise prediction technique using multiple linear regression analysis
NASA Astrophysics Data System (ADS)
Rathgeber, R. K.
1981-04-01
At Cessna Aircraft Company, statistical analyses have been developed to define important trends in flyover noise data. Multiple regression techniques have provided the means to develop flyover noise prediction methods which have resulted in better accuracy than methods used in the past. Regression analyses have been conducted to determine the important relationship between propeller helical tip Mach number and the flyover noise level. Other variables have been included in the regression models either because the added variable contributed to reducing the remaining variation in the model or the variable appeared to be a strong causal agent of flyover noise.
WebDISCO: a web service for distributed cox model learning without patient-level data sharing.
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
WebDISCO: a web service for distributed cox model learning without patient-level data sharing
Lu, Chia-Lun; Wang, Shuang; Ji, Zhanglong; Wu, Yuan; Xiong, Li; Jiang, Xiaoqian; Ohno-Machado, Lucila
2015-01-01
Objective 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. Methods and materials 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. Results 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 10−15 to 10−12. The results confirm the mathematical derivation and show that the implementation of the distributed model can achieve the same results as the centralized implementation. Limitation 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. PMID:26159465