CUSUM-Logistic Regression analysis for the rapid detection of errors in clinical laboratory test results.

PubMed

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.

Predicting 30-day Hospital Readmission with Publicly Available Administrative Database. A Conditional Logistic Regression Modeling Approach.

PubMed

Zhu, K; Lou, Z; Zhou, J; Ballester, N; Kong, N; Parikh, P

2015-01-01

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of more than 10% over the standard classification models, which can be translated to correct labeling of additional 400 - 500 readmissions for heart failure patients in the state of California over a year. Lastly, several key predictor identified from the HCUP data include the disposition location from discharge, the number of chronic conditions, and the number of acute procedures. It would be beneficial to apply simple decision rules obtained from the decision tree in an ad-hoc manner to guide the cohort stratification. It could be potentially beneficial to explore the effect of pairwise interactions between influential predictors when building the logistic regression models for different data strata. Judicious use of the ad-hoc CLR models developed offers insights into future development of prediction models for hospital readmissions, which can lead to better intuition in identifying high-risk patients and developing effective post-discharge care strategies. Lastly, this paper is expected to raise the awareness of collecting data on additional markers and developing necessary database infrastructure for larger-scale exploratory studies on readmission risk prediction.

Regression trees for predicting mortality in patients with cardiovascular disease: What improvement is achieved by using ensemble-based methods?

PubMed Central

Austin, Peter C; Lee, Douglas S; Steyerberg, Ewout W; Tu, Jack V

2012-01-01

In biomedical research, the logistic regression model is the most commonly used method for predicting the probability of a binary outcome. While many clinical researchers have expressed an enthusiasm for regression trees, this method may have limited accuracy for predicting health outcomes. We aimed to evaluate the improvement that is achieved by using ensemble-based methods, including bootstrap aggregation (bagging) of regression trees, random forests, and boosted regression trees. We analyzed 30-day mortality in two large cohorts of patients hospitalized with either acute myocardial infarction (N = 16,230) or congestive heart failure (N = 15,848) in two distinct eras (1999–2001 and 2004–2005). We found that both the in-sample and out-of-sample prediction of ensemble methods offered substantial improvement in predicting cardiovascular mortality compared to conventional regression trees. However, conventional logistic regression models that incorporated restricted cubic smoothing splines had even better performance. We conclude that ensemble methods from the data mining and machine learning literature increase the predictive performance of regression trees, but may not lead to clear advantages over conventional logistic regression models for predicting short-term mortality in population-based samples of subjects with cardiovascular disease. PMID:22777999

Upgrade Summer Severe Weather Tool

NASA Technical Reports Server (NTRS)

Watson, Leela

2011-01-01

The goal of this task was to upgrade to the existing severe weather database by adding observations from the 2010 warm season, update the verification dataset with results from the 2010 warm season, use statistical logistic regression analysis on the database and develop a new forecast tool. The AMU analyzed 7 stability parameters that showed the possibility of providing guidance in forecasting severe weather, calculated verification statistics for the Total Threat Score (TTS), and calculated warm season verification statistics for the 2010 season. The AMU also performed statistical logistic regression analysis on the 22-year severe weather database. The results indicated that the logistic regression equation did not show an increase in skill over the previously developed TTS. The equation showed less accuracy than TTS at predicting severe weather, little ability to distinguish between severe and non-severe weather days, and worse standard categorical accuracy measures and skill scores over TTS.

Combining logistic regression with classification and regression tree to predict quality of care in a home health nursing data set.

PubMed

Guo, Huey-Ming; Shyu, Yea-Ing Lotus; Chang, Her-Kun

2006-01-01

In this article, the authors provide an overview of a research method to predict quality of care in home health nursing data set. The results of this study can be visualized through classification an regression tree (CART) graphs. The analysis was more effective, and the results were more informative since the home health nursing dataset was analyzed with a combination of the logistic regression and CART, these two techniques complete each other. And the results more informative that more patients' characters were related to quality of care in home care. The results contributed to home health nurse predict patient outcome in case management. Improved prediction is needed for interventions to be appropriately targeted for improved patient outcome and quality of care.

Improving power and robustness for detecting genetic association with extreme-value sampling design.

PubMed

Chen, Hua Yun; Li, Mingyao

2011-12-01

Extreme-value sampling design that samples subjects with extremely large or small quantitative trait values is commonly used in genetic association studies. Samples in such designs are often treated as "cases" and "controls" and analyzed using logistic regression. Such a case-control analysis ignores the potential dose-response relationship between the quantitative trait and the underlying trait locus and thus may lead to loss of power in detecting genetic association. An alternative approach to analyzing such data is to model the dose-response relationship by a linear regression model. However, parameter estimation from this model can be biased, which may lead to inflated type I errors. We propose a robust and efficient approach that takes into consideration of both the biased sampling design and the potential dose-response relationship. Extensive simulations demonstrate that the proposed method is more powerful than the traditional logistic regression analysis and is more robust than the linear regression analysis. We applied our method to the analysis of a candidate gene association study on high-density lipoprotein cholesterol (HDL-C) which includes study subjects with extremely high or low HDL-C levels. Using our method, we identified several SNPs showing a stronger evidence of association with HDL-C than the traditional case-control logistic regression analysis. Our results suggest that it is important to appropriately model the quantitative traits and to adjust for the biased sampling when dose-response relationship exists in extreme-value sampling designs. © 2011 Wiley Periodicals, Inc.

Independent Prognostic Factors for Acute Organophosphorus Pesticide Poisoning.

PubMed

Tang, Weidong; Ruan, Feng; Chen, Qi; Chen, Suping; Shao, Xuebo; Gao, Jianbo; Zhang, Mao

2016-07-01

Acute organophosphorus pesticide poisoning (AOPP) is becoming a significant problem and a potential cause of human mortality because of the abuse of organophosphate compounds. This study aims to determine the independent prognostic factors of AOPP by using multivariate logistic regression analysis. The clinical data for 71 subjects with AOPP admitted to our hospital were retrospectively analyzed. This information included the Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, admission blood cholinesterase levels, 6-h post-admission blood cholinesterase levels, cholinesterase activity, blood pH, and other factors. Univariate analysis and multivariate logistic regression analyses were conducted to identify all prognostic factors and independent prognostic factors, respectively. A receiver operating characteristic curve was plotted to analyze the testing power of independent prognostic factors. Twelve of 71 subjects died. Admission blood lactate levels, 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, blood pH, and APACHE II scores were identified as prognostic factors for AOPP according to the univariate analysis, whereas only 6-h post-admission blood lactate levels, post-admission 6-h lactate clearance rates, and blood pH were independent prognostic factors identified by multivariate logistic regression analysis. The receiver operating characteristic analysis suggested that post-admission 6-h lactate clearance rates were of moderate diagnostic value. High 6-h post-admission blood lactate levels, low blood pH, and low post-admission 6-h lactate clearance rates were independent prognostic factors identified by multivariate logistic regression analysis. Copyright © 2016 by Daedalus Enterprises.

Intermediate and advanced topics in multilevel logistic regression analysis

PubMed Central

Merlo, Juan

2017-01-01

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. PMID:28543517

Intermediate and advanced topics in multilevel logistic regression analysis.

PubMed

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.

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

Butler, W.J.; Kalasinski, L.A.

In this paper, a generalized logistic regression model for correlated observations is used to analyze epidemiologic data on the frequency of spontaneous abortion among a group of women office workers. The results are compared to those obtained from the use of the standard logistic regression model that assumes statistical independence among all the pregnancies contributed by one woman. In this example, the correlation among pregnancies from the same woman is fairly small and did not have a substantial impact on the magnitude of estimates of parameters of the model. This is due at least partly to the small average numbermore » of pregnancies contributed by each woman.« less

Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis.

PubMed

Armstrong, Ben G; Gasparrini, Antonio; Tobias, Aurelio

2014-11-24

The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case-control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters. The conditional Poisson model avoids estimating stratum parameters by conditioning on the total event count in each stratum, thus simplifying the computing and increasing the number of strata for which fitting is feasible compared with the standard unconditional Poisson model. Unlike the conditional logistic model, the conditional Poisson model does not require expanding the data, and can adjust for overdispersion and auto-correlation. It is available in Stata, R, and other packages. By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models. Allowing for overdispersion or autocorrelation was possible with the conditional Poisson model but when not required this model gave identical estimates to those from conditional logistic regression. Conditional Poisson regression models provide an alternative to case crossover analysis of stratified time series data with some advantages. The conditional Poisson model can also be used in other contexts in which primary control for confounding is by fine stratification.

The use of generalized estimating equations in the analysis of motor vehicle crash data.

PubMed

Hutchings, Caroline B; Knight, Stacey; Reading, James C

2003-01-01

The purpose of this study was to determine if it is necessary to use generalized estimating equations (GEEs) in the analysis of seat belt effectiveness in preventing injuries in motor vehicle crashes. The 1992 Utah crash dataset was used, excluding crash participants where seat belt use was not appropriate (n=93,633). The model used in the 1996 Report to Congress [Report to congress on benefits of safety belts and motorcycle helmets, based on data from the Crash Outcome Data Evaluation System (CODES). National Center for Statistics and Analysis, NHTSA, Washington, DC, February 1996] was analyzed for all occupants with logistic regression, one level of nesting (occupants within crashes), and two levels of nesting (occupants within vehicles within crashes) to compare the use of GEEs with logistic regression. When using one level of nesting compared to logistic regression, 13 of 16 variance estimates changed more than 10%, and eight of 16 parameter estimates changed more than 10%. In addition, three of the independent variables changed from significant to insignificant (alpha=0.05). With the use of two levels of nesting, two of 16 variance estimates and three of 16 parameter estimates changed more than 10% from the variance and parameter estimates in one level of nesting. One of the independent variables changed from insignificant to significant (alpha=0.05) in the two levels of nesting model; therefore, only two of the independent variables changed from significant to insignificant when the logistic regression model was compared to the two levels of nesting model. The odds ratio of seat belt effectiveness in preventing injuries was 12% lower when a one-level nested model was used. Based on these results, we stress the need to use a nested model and GEEs when analyzing motor vehicle crash data.

Study of relationship between clinical factors and velopharyngeal closure in cleft palate patients

PubMed Central

Chen, Qi; Zheng, Qian; Shi, Bing; Yin, Heng; Meng, Tian; Zheng, Guang-ning

2011-01-01

BACKGROUND: This study was carried out to analyze the relationship between clinical factors and velopharyngeal closure (VPC) in cleft palate patients. METHODS: Chi-square test was used to compare the postoperative velopharyngeal closure rate. Logistic regression model was used to analyze independent variables associated with velopharyngeal closure. RESULTS: Difference of postoperative VPC rate in different cleft types, operative ages and surgical techniques was significant (P=0.000). Results of logistic regression analysis suggested that when operative age was beyond deciduous dentition stage, or cleft palate type was complete, or just had undergone a simple palatoplasty without levator veli palatini retropositioning, patients would suffer a higher velopharyngeal insufficiency rate after primary palatal repair. CONCLUSIONS: Cleft type, operative age and surgical technique were the contributing factors influencing VPC rate after primary palatal repair of cleft palate patients. PMID:22279464

Multivariate logistic regression analysis of postoperative complications and risk model establishment of gastrectomy for gastric cancer: A single-center cohort report.

PubMed

Zhou, Jinzhe; Zhou, Yanbing; Cao, Shougen; Li, Shikuan; Wang, Hao; Niu, Zhaojian; Chen, Dong; Wang, Dongsheng; Lv, Liang; Zhang, Jian; Li, Yu; Jiao, Xuelong; Tan, Xiaojie; Zhang, Jianli; Wang, Haibo; Zhang, Bingyuan; Lu, Yun; Sun, Zhenqing

2016-01-01

Reporting of surgical complications is common, but few provide information about the severity and estimate risk factors of complications. If have, but lack of specificity. We retrospectively analyzed data on 2795 gastric cancer patients underwent surgical procedure at the Affiliated Hospital of Qingdao University between June 2007 and June 2012, established multivariate logistic regression model to predictive risk factors related to the postoperative complications according to the Clavien-Dindo classification system. Twenty-four out of 86 variables were identified statistically significant in univariate logistic regression analysis, 11 significant variables entered multivariate analysis were employed to produce the risk model. Liver cirrhosis, diabetes mellitus, Child classification, invasion of neighboring organs, combined resection, introperative transfusion, Billroth II anastomosis of reconstruction, malnutrition, surgical volume of surgeons, operating time and age were independent risk factors for postoperative complications after gastrectomy. Based on logistic regression equation, p=Exp∑BiXi / (1+Exp∑BiXi), multivariate logistic regression predictive model that calculated the risk of postoperative morbidity was developed, p = 1/(1 + e((4.810-1.287X1-0.504X2-0.500X3-0.474X4-0.405X5-0.318X6-0.316X7-0.305X8-0.278X9-0.255X10-0.138X11))). The accuracy, sensitivity and specificity of the model to predict the postoperative complications were 86.7%, 76.2% and 88.6%, respectively. This risk model based on Clavien-Dindo grading severity of complications system and logistic regression analysis can predict severe morbidity specific to an individual patient's risk factors, estimate patients' risks and benefits of gastric surgery as an accurate decision-making tool and may serve as a template for the development of risk models for other surgical groups.

Dental health services utilization and associated factors in children 6 to 12 years old in a low-income country.

PubMed

Medina-Solis, Carlo Eduardo; Maupomé, Gerardo; del Socorro, Herrera Miriam; Pérez-Núñez, Ricardo; Avila-Burgos, Leticia; Lamadrid-Figueroa, Hector

2008-01-01

To determine the factors associated with the dental health services utilization among children ages 6 to 12 in León, Nicaragua. A cross-sectional study was carried out in 1,400 schoolchildren. Using a questionnaire, we determined information related to utilization and independent variables in the previous year. Oral health needs were established by means of a dental examination. To identify the independent variables associated with dental health services utilization, two types of multivariate regression models were used, according to the measurement scale of the outcome variable: a) frequency of utilization as (0) none, (1) one, and (2) two or more, analyzed with the ordered logistic regression and b) the type of service utilized as (0) none, (1) preventive services, (2) curative services, and (3) both services, analyzed with the multinomial logistic regression. The proportion of children who received at least one dental service in the 12 months prior to the study was 27.7 percent. The variables associated with utilization in the two models were older age, female sex, more frequent toothbrushing, positive attitude of the mother toward the child's oral health, higher socioeconomic level, and higher oral health needs. Various predisposing, enabling, and oral health needs variables were associated with higher dental health services utilization. As in prior reports elsewhere, these results from Nicaragua confirmed that utilization inequalities exist between socioeconomic groups. The multinomial logistic regression model evidenced the association of different variables depending on the type of service used.

Estimating a Logistic Discrimination Functions When One of the Training Samples Is Subject to Misclassification: A Maximum Likelihood Approach.

PubMed

Nagelkerke, Nico; Fidler, Vaclav

2015-01-01

The problem of discrimination and classification is central to much of epidemiology. Here we consider the estimation of a logistic regression/discrimination function from training samples, when one of the training samples is subject to misclassification or mislabeling, e.g. diseased individuals are incorrectly classified/labeled as healthy controls. We show that this leads to zero-inflated binomial model with a defective logistic regression or discrimination function, whose parameters can be estimated using standard statistical methods such as maximum likelihood. These parameters can be used to estimate the probability of true group membership among those, possibly erroneously, classified as controls. Two examples are analyzed and discussed. A simulation study explores properties of the maximum likelihood parameter estimates and the estimates of the number of mislabeled observations.

Exploring students' patterns of reasoning

NASA Astrophysics Data System (ADS)

Matloob Haghanikar, Mojgan

As part of a collaborative study of the science preparation of elementary school teachers, we investigated the quality of students' reasoning and explored the relationship between sophistication of reasoning and the degree to which the courses were considered inquiry oriented. To probe students' reasoning, we developed open-ended written content questions with the distinguishing feature of applying recently learned concepts in a new context. We devised a protocol for developing written content questions that provided a common structure for probing and classifying students' sophistication level of reasoning. In designing our protocol, we considered several distinct criteria, and classified students' responses based on their performance for each criterion. First, we classified concepts into three types: Descriptive, Hypothetical, and Theoretical and categorized the abstraction levels of the responses in terms of the types of concepts and the inter-relationship between the concepts. Second, we devised a rubric based on Bloom's revised taxonomy with seven traits (both knowledge types and cognitive processes) and a defined set of criteria to evaluate each trait. Along with analyzing students' reasoning, we visited universities and observed the courses in which the students were enrolled. We used the Reformed Teaching Observation Protocol (RTOP) to rank the courses with respect to characteristics that are valued for the inquiry courses. We conducted logistic regression for a sample of 18courses with about 900 students and reported the results for performing logistic regression to estimate the relationship between traits of reasoning and RTOP score. In addition, we analyzed conceptual structure of students' responses, based on conceptual classification schemes, and clustered students' responses into six categories. We derived regression model, to estimate the relationship between the sophistication of the categories of conceptual structure and RTOP scores. However, the outcome variable with six categories required a more complicated regression model, known as multinomial logistic regression, generalized from binary logistic regression. With the large amount of collected data, we found that the likelihood of the higher cognitive processes were in favor of classes with higher measures on inquiry. However, the usage of more abstract concepts with higher order conceptual structures was less prevalent in higher RTOP courses.

The effect of high leverage points on the logistic ridge regression estimator having multicollinearity

NASA Astrophysics Data System (ADS)

Ariffin, Syaiba Balqish; Midi, Habshah

2014-06-01

This article is concerned with the performance of logistic ridge regression estimation technique in the presence of multicollinearity and high leverage points. In logistic regression, multicollinearity exists among predictors and in the information matrix. The maximum likelihood estimator suffers a huge setback in the presence of multicollinearity which cause regression estimates to have unduly large standard errors. To remedy this problem, a logistic ridge regression estimator is put forward. It is evident that the logistic ridge regression estimator outperforms the maximum likelihood approach for handling multicollinearity. The effect of high leverage points are then investigated on the performance of the logistic ridge regression estimator through real data set and simulation study. The findings signify that logistic ridge regression estimator fails to provide better parameter estimates in the presence of both high leverage points and multicollinearity.

Propensity of University Students in the Region of Antofagasta, Chile to Create Enterprise

ERIC Educational Resources Information Center

Romani, Gianni; Didonet, Simone; Contuliano, Sue-Hellen; Portilla, Rodrigo

2013-01-01

The authors aim to discuss the propensity or intention to create enterprise among university students in the region of Antofagasta, Chile, and to analyze the factors that influence the step from desire to intention. 681 students were surveyed. The data were analyzed by binary logistical regression. The results show that curriculum is among the…

Prescription-drug-related risk in driving: comparing conventional and lasso shrinkage logistic regressions.

PubMed

Avalos, Marta; Adroher, Nuria Duran; Lagarde, Emmanuel; Thiessard, Frantz; Grandvalet, Yves; Contrand, Benjamin; Orriols, Ludivine

2012-09-01

Large data sets with many variables provide particular challenges when constructing analytic models. Lasso-related methods provide a useful tool, although one that remains unfamiliar to most epidemiologists. We illustrate the application of lasso methods in an analysis of the impact of prescribed drugs on the risk of a road traffic crash, using a large French nationwide database (PLoS Med 2010;7:e1000366). In the original case-control study, the authors analyzed each exposure separately. We use the lasso method, which can simultaneously perform estimation and variable selection in a single model. We compare point estimates and confidence intervals using (1) a separate logistic regression model for each drug with a Bonferroni correction and (2) lasso shrinkage logistic regression analysis. Shrinkage regression had little effect on (bias corrected) point estimates, but led to less conservative results, noticeably for drugs with moderate levels of exposure. Carbamates, carboxamide derivative and fatty acid derivative antiepileptics, drugs used in opioid dependence, and mineral supplements of potassium showed stronger associations. Lasso is a relevant method in the analysis of databases with large number of exposures and can be recommended as an alternative to conventional strategies.

Unconditional or Conditional Logistic Regression Model for Age-Matched Case-Control Data?

PubMed

Kuo, Chia-Ling; Duan, Yinghui; Grady, James

2018-01-01

Matching on demographic variables is commonly used in case-control studies to adjust for confounding at the design stage. There is a presumption that matched data need to be analyzed by matched methods. Conditional logistic regression has become a standard for matched case-control data to tackle the sparse data problem. The sparse data problem, however, may not be a concern for loose-matching data when the matching between cases and controls is not unique, and one case can be matched to other controls without substantially changing the association. Data matched on a few demographic variables are clearly loose-matching data, and we hypothesize that unconditional logistic regression is a proper method to perform. To address the hypothesis, we compare unconditional and conditional logistic regression models by precision in estimates and hypothesis testing using simulated matched case-control data. Our results support our hypothesis; however, the unconditional model is not as robust as the conditional model to the matching distortion that the matching process not only makes cases and controls similar for matching variables but also for the exposure status. When the study design involves other complex features or the computational burden is high, matching in loose-matching data can be ignored for negligible loss in testing and estimation if the distributions of matching variables are not extremely different between cases and controls.

Unconditional or Conditional Logistic Regression Model for Age-Matched Case–Control Data?

PubMed Central

Kuo, Chia-Ling; Duan, Yinghui; Grady, James

2018-01-01

Matching on demographic variables is commonly used in case–control studies to adjust for confounding at the design stage. There is a presumption that matched data need to be analyzed by matched methods. Conditional logistic regression has become a standard for matched case–control data to tackle the sparse data problem. The sparse data problem, however, may not be a concern for loose-matching data when the matching between cases and controls is not unique, and one case can be matched to other controls without substantially changing the association. Data matched on a few demographic variables are clearly loose-matching data, and we hypothesize that unconditional logistic regression is a proper method to perform. To address the hypothesis, we compare unconditional and conditional logistic regression models by precision in estimates and hypothesis testing using simulated matched case–control data. Our results support our hypothesis; however, the unconditional model is not as robust as the conditional model to the matching distortion that the matching process not only makes cases and controls similar for matching variables but also for the exposure status. When the study design involves other complex features or the computational burden is high, matching in loose-matching data can be ignored for negligible loss in testing and estimation if the distributions of matching variables are not extremely different between cases and controls. PMID:29552553

Estimating multilevel logistic regression models when the number of clusters is low: a comparison of different statistical software procedures.

PubMed

Austin, Peter C

2010-04-22

Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public health, epidemiological, and educational research. Procedures for estimating the parameters of such models are available in many statistical software packages. There is currently little evidence on the minimum number of clusters necessary to reliably fit multilevel regression models. We conducted a Monte Carlo study to compare the performance of different statistical software procedures for estimating multilevel logistic regression models when the number of clusters was low. We examined procedures available in BUGS, HLM, R, SAS, and Stata. We found that there were qualitative differences in the performance of different software procedures for estimating multilevel logistic models when the number of clusters was low. Among the likelihood-based procedures, estimation methods based on adaptive Gauss-Hermite approximations to the likelihood (glmer in R and xtlogit in Stata) or adaptive Gaussian quadrature (Proc NLMIXED in SAS) tended to have superior performance for estimating variance components when the number of clusters was small, compared to software procedures based on penalized quasi-likelihood. However, only Bayesian estimation with BUGS allowed for accurate estimation of variance components when there were fewer than 10 clusters. For all statistical software procedures, estimation of variance components tended to be poor when there were only five subjects per cluster, regardless of the number of clusters.

Analysis of a database to predict the result of allergy testing in vivo in patients with chronic nasal symptoms.

PubMed

Lacagnina, Valerio; Leto-Barone, Maria S; La Piana, Simona; Seidita, Aurelio; Pingitore, Giuseppe; Di Lorenzo, Gabriele

2014-01-01

This article uses the logistic regression model for diagnostic decision making in patients with chronic nasal symptoms. We studied the ability of the logistic regression model, obtained by the evaluation of a database, to detect patients with positive allergy skin-prick test (SPT) and patients with negative SPT. The model developed was validated using the data set obtained from another medical institution. The analysis was performed using a database obtained from a questionnaire administered to the patients with nasal symptoms containing personal data, clinical data, and results of allergy testing (SPT). All variables found to be significantly different between patients with positive and negative SPT (p < 0.05) were selected for the logistic regression models and were analyzed with backward stepwise logistic regression, evaluated with area under the curve of the receiver operating characteristic curve. A second set of patients from another institution was used to prove the model. The accuracy of the model in identifying, over the second set, both patients whose SPT will be positive and negative was high. The model detected 96% of patients with nasal symptoms and positive SPT and classified 94% of those with negative SPT. This study is preliminary to the creation of a software that could help the primary care doctors in a diagnostic decision making process (need of allergy testing) in patients complaining of chronic nasal symptoms.

Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

PubMed

Faul, Franz; Erdfelder, Edgar; Buchner, Axel; Lang, Albert-Georg

2009-11-01

G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.

Sample size determination for logistic regression on a logit-normal distribution.

PubMed

Kim, Seongho; Heath, Elisabeth; Heilbrun, Lance

2017-06-01

Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination ([Formula: see text]) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal distribution which can be generated from a logistic transformation of a normal distribution. Using this property of logistic regression, we propose new methods of determining the sample size for simple and multiple logistic regressions using a normal transformation of outcome measures. Simulation studies and a motivating example show several advantages of the proposed methods over the existing methods: (i) no need for [Formula: see text] for multiple logistic regression, (ii) available interim or group-sequential designs, and (iii) much smaller required sample size.

A comparison of Cox and logistic regression for use in genome-wide association studies of cohort and case-cohort design.

PubMed

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.

Mining pharmacovigilance data using Bayesian logistic regression with James-Stein type shrinkage estimation.

PubMed

An, Lihua; Fung, Karen Y; Krewski, Daniel

2010-09-01

Spontaneous adverse event reporting systems are widely used to identify adverse reactions to drugs following their introduction into the marketplace. In this article, a James-Stein type shrinkage estimation strategy was developed in a Bayesian logistic regression model to analyze pharmacovigilance data. This method is effective in detecting signals as it combines information and borrows strength across medically related adverse events. Computer simulation demonstrated that the shrinkage estimator is uniformly better than the maximum likelihood estimator in terms of mean squared error. This method was used to investigate the possible association of a series of diabetic drugs and the risk of cardiovascular events using data from the Canada Vigilance Online Database.

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…

A logistic regression analysis of factors related to the treatment compliance of infertile patients with polycystic ovary syndrome.

PubMed

Li, Saijiao; He, Aiyan; Yang, Jing; Yin, TaiLang; Xu, Wangming

2011-01-01

To investigate factors that can affect compliance with treatment of polycystic ovary syndrome (PCOS) in infertile patients and to provide a basis for clinical treatment, specialist consultation and health education. Patient compliance was assessed via a questionnaire based on the Morisky-Green test and the treatment principles of PCOS. Then interviews were conducted with 99 infertile patients diagnosed with PCOS at Renmin Hospital of Wuhan University in China, from March to September 2009. Finally, these data were analyzed using logistic regression analysis. Logistic regression analysis revealed that a total of 23 (25.6%) of the participants showed good compliance. Factors that significantly (p < 0.05) affected compliance with treatment were the patient's body mass index, convenience of medical treatment and concerns about adverse drug reactions. Patients who are obese, experience inconvenient medical treatment or are concerned about adverse drug reactions are more likely to exhibit noncompliance. Treatment education and intervention aimed at these patients should be strengthened in the clinic to improve treatment compliance. Further research is needed to better elucidate the compliance behavior of patients with PCOS.

Alcohol and tobacco use and cognitive-motivational variables in school settings: effects on academic performance in Spanish adolescents.

PubMed

Inglés, Cándido J; Torregrosa, María S; Rodríguez-Marín, Jesús; García del Castillo, José A; Gázquez, José J; García-Fernández, José M; Delgado, Beatriz

2013-01-01

The aim of the present study was to analyze: (a) the relationship between alcohol and tobacco use and academic performance, and (b) the predictive role of psycho-educational factors and alcohol and tobacco abuse on academic performance in a sample of 352 Spanish adolescents from grades 8 to 10 of Compulsory Secondary Education. The Self-Description Questionnaire-II, the Sydney Attribution Scale, and the Achievement Goal Tendencies Questionnaire were administered in order to analyze cognitive-motivational variables. Alcohol and tobacco abuse, sex, and grade retention were also measured using self-reported questions. Academic performance was measured by school records. Frequency analyses and logistic regression analyses were used. Frequency analyses revealed that students who abuse of tobacco and alcohol show a higher rate of poor academic performance. Logistic regression analyses showed that health behaviours, and educational and cognitive-motivational variables exert a different effect on academic performance depending on the academic area analyzed. These results point out that not only academic, but also health variables should be address to improve academic performance in adolescence.

Experiences That Predict Early Career Teacher Commitment to and Retention in High-Poverty Urban Schools

ERIC Educational Resources Information Center

Whipp, Joan L.; Geronime, Lara

2017-01-01

Correlation analysis was used to analyze what experiences before and during teacher preparation for 72 graduates of an urban teacher education program were associated with urban commitment, first job location, and retention in urban schools for 3 or more years. Binary logistic regression was then used to analyze whether urban K-12 schooling,…

Analyzing the Administration Perception of the Teachers by Means of Logistic Regression According to Values

ERIC Educational Resources Information Center

Ugurlu, Celal Teyyar

2017-01-01

This study aims to analyze the administration perception of the teachers according to values in line with certain parameters. The model of the research is relational screening model. The population is applied to 470 teachers who work in 25 secondary schools at the center of Sivas with scales. 317 questionnaires which had been returned have been…

Analyzing Log Files to Predict Students' Problem Solving Performance in a Computer-Based Physics Tutor

ERIC Educational Resources Information Center

Lee, Young-Jin

2015-01-01

This study investigates whether information saved in the log files of a computer-based tutor can be used to predict the problem solving performance of students. The log files of a computer-based physics tutoring environment called Andes Physics Tutor was analyzed to build a logistic regression model that predicted success and failure of students'…

The microbiological profile and presence of bloodstream infection influence mortality rates in necrotizing fasciitis

PubMed Central

2011-01-01

Introduction Necrotizing fasciitis (NF) is a life threatening infectious disease with a high mortality rate. We carried out a microbiological characterization of the causative pathogens. We investigated the correlation of mortality in NF with bloodstream infection and with the presence of co-morbidities. Methods In this retrospective study, we analyzed 323 patients who presented with necrotizing fasciitis at two different institutions. Bloodstream infection (BSI) was defined as a positive blood culture result. The patients were categorized as survivors and non-survivors. Eleven clinically important variables which were statistically significant by univariate analysis were selected for multivariate regression analysis and a stepwise logistic regression model was developed to determine the association between BSI and mortality. Results Univariate logistic regression analysis showed that patients with hypotension, heart disease, liver disease, presence of Vibrio spp. in wound cultures, presence of fungus in wound cultures, and presence of Streptococcus group A, Aeromonas spp. or Vibrio spp. in blood cultures, had a significantly higher risk of in-hospital mortality. Our multivariate logistic regression analysis showed a higher risk of mortality in patients with pre-existing conditions like hypotension, heart disease, and liver disease. Multivariate logistic regression analysis also showed that presence of Vibrio spp in wound cultures, and presence of Streptococcus Group A in blood cultures were associated with a high risk of mortality while debridement > = 3 was associated with improved survival. Conclusions Mortality in patients with necrotizing fasciitis was significantly associated with the presence of Vibrio in wound cultures and Streptococcus group A in blood cultures. PMID:21693053

Applying Kaplan-Meier to Item Response Data

ERIC Educational Resources Information Center

McNeish, Daniel

2018-01-01

Some IRT models can be equivalently modeled in alternative frameworks such as logistic regression. Logistic regression can also model time-to-event data, which concerns the probability of an event occurring over time. Using the relation between time-to-event models and logistic regression and the relation between logistic regression and IRT, this…

EFFECTS OF LANDSCAPE CHARACTERISTICS ON LAND-COVER CLASS ACCURACY

EPA Science Inventory

Utilizing land-cover data gathered as part of the National Land-Cover Data (NLCD) set accuracy assessment, several logistic regression models were formulated to analyze the effects of patch size and land-cover heterogeneity on classification accuracy. Specific land-cover ...

Protective Effect of HLA-DQB1 Alleles Against Alloimmunization in Patients with Sickle Cell Disease

PubMed Central

Tatari-Calderone, Zohreh; Gordish-Dressman, Heather; Fasano, Ross; Riggs, Michael; Fortier, Catherine; Andrew; Campbell, D.; Charron, Dominique; Gordeuk, Victor R.; Luban, Naomi L.C.; Vukmanovic, Stanislav; Tamouza, Ryad

2015-01-01

Background Alloimmunization or the development of alloantibodies to Red Blood Cell (RBC) antigens is considered one of the major complications after RBC transfusions in patients with sickle cell disease (SCD) and can lead to both acute and delayed hemolytic reactions. It has been suggested that polymorphisms in HLA genes, may play a role in alloimmunization. We conducted a retrospective study analyzing the influence of HLA-DRB1 and DQB1 genetic diversity on RBC-alloimmunization. Study design Two-hundred four multi-transfused SCD patients with and without RBC-alloimmunization were typed at low/medium resolution by PCR-SSO, using IMGT-HLA Database. HLA-DRB1 and DQB1 allele frequencies were analyzed using logistic regression models, and global p-value was calculated using multiple logistic regression. Results While only trends towards associations between HLA-DR diversity and alloimmunization were observed, analysis of HLA-DQ showed that HLA-DQ2 (p=0.02), -DQ3 (p=0.02) and -DQ5 (p=0.01) alleles were significantly higher in non-alloimmunized patients, likely behaving as protective alleles. In addition, multiple logistic regression analysis showed both HLA-DQ2/6 (p=0.01) and HLA-DQ5/5 (p=0.03) combinations constitute additional predictor of protective status. Conclusion Our data suggest that particular HLA-DQ alleles influence the clinical course of RBC transfusion in patients with SCD, which could pave the way towards predictive strategies. PMID:26476208

Comparison of multinomial logistic regression and logistic regression: which is more efficient in allocating land use?

NASA Astrophysics Data System (ADS)

Lin, Yingzhi; Deng, Xiangzheng; Li, Xing; Ma, Enjun

2014-12-01

Spatially explicit simulation of land use change is the basis for estimating the effects of land use and cover change on energy fluxes, ecology and the environment. At the pixel level, logistic regression is one of the most common approaches used in spatially explicit land use allocation models to determine the relationship between land use and its causal factors in driving land use change, and thereby to evaluate land use suitability. However, these models have a drawback in that they do not determine/allocate land use based on the direct relationship between land use change and its driving factors. Consequently, a multinomial logistic regression method was introduced to address this flaw, and thereby, judge the suitability of a type of land use in any given pixel in a case study area of the Jiangxi Province, China. A comparison of the two regression methods indicated that the proportion of correctly allocated pixels using multinomial logistic regression was 92.98%, which was 8.47% higher than that obtained using logistic regression. Paired t-test results also showed that pixels were more clearly distinguished by multinomial logistic regression than by logistic regression. In conclusion, multinomial logistic regression is a more efficient and accurate method for the spatial allocation of land use changes. The application of this method in future land use change studies may improve the accuracy of predicting the effects of land use and cover change on energy fluxes, ecology, and environment.

A Multilevel Assessment of Differential Item Functioning.

ERIC Educational Resources Information Center

Shen, Linjun

A multilevel approach was proposed for the assessment of differential item functioning and compared with the traditional logistic regression approach. Data from the Comprehensive Osteopathic Medical Licensing Examination for 2,300 freshman osteopathic medical students were analyzed. The multilevel approach used three-level hierarchical generalized…

Standards for Standardized Logistic Regression Coefficients

ERIC Educational Resources Information Center

Menard, Scott

2011-01-01

Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…

A Bayesian goodness of fit test and semiparametric generalization of logistic regression with measurement data.

PubMed

Schörgendorfer, Angela; Branscum, Adam J; Hanson, Timothy E

2013-06-01

Logistic regression is a popular tool for risk analysis in medical and population health science. With continuous response data, it is common to create a dichotomous outcome for logistic regression analysis by specifying a threshold for positivity. Fitting a linear regression to the nondichotomized response variable assuming a logistic sampling model for the data has been empirically shown to yield more efficient estimates of odds ratios than ordinary logistic regression of the dichotomized endpoint. We illustrate that risk inference is not robust to departures from the parametric logistic distribution. Moreover, the model assumption of proportional odds is generally not satisfied when the condition of a logistic distribution for the data is violated, leading to biased inference from a parametric logistic analysis. We develop novel Bayesian semiparametric methodology for testing goodness of fit of parametric logistic regression with continuous measurement data. The testing procedures hold for any cutoff threshold and our approach simultaneously provides the ability to perform semiparametric risk estimation. Bayes factors are calculated using the Savage-Dickey ratio for testing the null hypothesis of logistic regression versus a semiparametric generalization. We propose a fully Bayesian and a computationally efficient empirical Bayesian approach to testing, and we present methods for semiparametric estimation of risks, relative risks, and odds ratios when parametric logistic regression fails. Theoretical results establish the consistency of the empirical Bayes test. Results from simulated data show that the proposed approach provides accurate inference irrespective of whether parametric assumptions hold or not. Evaluation of risk factors for obesity shows that different inferences are derived from an analysis of a real data set when deviations from a logistic distribution are permissible in a flexible semiparametric framework. © 2013, The International Biometric Society.

Habitat features and predictive habitat modeling for the Colorado chipmunk in southern New Mexico

USGS Publications Warehouse

Rivieccio, M.; Thompson, B.C.; Gould, W.R.; Boykin, K.G.

2003-01-01

Two subspecies of Colorado chipmunk (state threatened and federal species of concern) occur in southern New Mexico: Tamias quadrivittatus australis in the Organ Mountains and T. q. oscuraensis in the Oscura Mountains. We developed a GIS model of potentially suitable habitat based on vegetation and elevation features, evaluated site classifications of the GIS model, and determined vegetation and terrain features associated with chipmunk occurrence. We compared GIS model classifications with actual vegetation and elevation features measured at 37 sites. At 60 sites we measured 18 habitat variables regarding slope, aspect, tree species, shrub species, and ground cover. We used logistic regression to analyze habitat variables associated with chipmunk presence/absence. All (100%) 37 sample sites (28 predicted suitable, 9 predicted unsuitable) were classified correctly by the GIS model regarding elevation and vegetation. For 28 sites predicted suitable by the GIS model, 18 sites (64%) appeared visually suitable based on habitat variables selected from logistic regression analyses, of which 10 sites (36%) were specifically predicted as suitable habitat via logistic regression. We detected chipmunks at 70% of sites deemed suitable via the logistic regression models. Shrub cover, tree density, plant proximity, presence of logs, and presence of rock outcrop were retained in the logistic model for the Oscura Mountains; litter, shrub cover, and grass cover were retained in the logistic model for the Organ Mountains. Evaluation of predictive models illustrates the need for multi-stage analyses to best judge performance. Microhabitat analyses indicate prospective needs for different management strategies between the subspecies. Sensitivities of each population of the Colorado chipmunk to natural and prescribed fire suggest that partial burnings of areas inhabited by Colorado chipmunks in southern New Mexico may be beneficial. These partial burnings may later help avoid a fire that could substantially reduce habitat of chipmunks over a mountain range.

Propensity score estimation: machine learning and classification methods as alternatives to logistic regression

PubMed Central

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-01-01

Summary Objective Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this Review was to assess machine learning alternatives to logistic regression which may accomplish the same goals but with fewer assumptions or greater accuracy. Study Design and Setting We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. Results We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (CART), and meta-classifiers (in particular, boosting). Conclusion While the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and to a lesser extent decision trees (particularly CART) appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. PMID:20630332

Robust mislabel logistic regression without modeling mislabel probabilities.

PubMed

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

Fungible weights in logistic regression.

PubMed

Jones, Jeff A; Waller, Niels G

2016-06-01

In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression.

PubMed

Westreich, Daniel; Lessler, Justin; Funk, Michele Jonsson

2010-08-01

Propensity scores for the analysis of observational data are typically estimated using logistic regression. Our objective in this review was to assess machine learning alternatives to logistic regression, which may accomplish the same goals but with fewer assumptions or greater accuracy. We identified alternative methods for propensity score estimation and/or classification from the public health, biostatistics, discrete mathematics, and computer science literature, and evaluated these algorithms for applicability to the problem of propensity score estimation, potential advantages over logistic regression, and ease of use. We identified four techniques as alternatives to logistic regression: neural networks, support vector machines, decision trees (classification and regression trees [CART]), and meta-classifiers (in particular, boosting). Although the assumptions of logistic regression are well understood, those assumptions are frequently ignored. All four alternatives have advantages and disadvantages compared with logistic regression. Boosting (meta-classifiers) and, to a lesser extent, decision trees (particularly CART), appear to be most promising for use in the context of propensity score analysis, but extensive simulation studies are needed to establish their utility in practice. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Should metacognition be measured by logistic regression?

PubMed

Rausch, Manuel; Zehetleitner, Michael

2017-03-01

Are logistic regression slopes suitable to quantify metacognitive sensitivity, i.e. the efficiency with which subjective reports differentiate between correct and incorrect task responses? We analytically show that logistic regression slopes are independent from rating criteria in one specific model of metacognition, which assumes (i) that rating decisions are based on sensory evidence generated independently of the sensory evidence used for primary task responses and (ii) that the distributions of evidence are logistic. Given a hierarchical model of metacognition, logistic regression slopes depend on rating criteria. According to all considered models, regression slopes depend on the primary task criterion. A reanalysis of previous data revealed that massive numbers of trials are required to distinguish between hierarchical and independent models with tolerable accuracy. It is argued that researchers who wish to use logistic regression as measure of metacognitive sensitivity need to control the primary task criterion and rating criteria. Copyright © 2017 Elsevier Inc. All rights reserved.

Bayesian Analysis of Nonlinear Structural Equation Models with Nonignorable Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum

2006-01-01

A Bayesian approach is developed for analyzing nonlinear structural equation models with nonignorable missing data. The nonignorable missingness mechanism is specified by a logistic regression model. A hybrid algorithm that combines the Gibbs sampler and the Metropolis-Hastings algorithm is used to produce the joint Bayesian estimates of…

Victimization by Bullying and Physical Symptoms among South Korean Schoolchildren

ERIC Educational Resources Information Center

Lee, Ji Hyeon

2018-01-01

This study examined the relationship between victimization by bullying and physical symptoms among South Korean school children. Data were analyzed from a nationally representative sample of 2006 schoolchildren across South Korea aged 9-17 years. Multiple logistic regression analysis was used to estimate the associations between victimization by…

Job Satisfaction, School Rule Enforcement, and Teacher Victimization

ERIC Educational Resources Information Center

Kapa, Ryan; Gimbert, Belinda

2018-01-01

Job satisfaction is an essential component of teacher motivation, performance, and retention. Teacher job satisfaction is primarily affected by workplace conditions. This paper analyzes data from over 37,000 public school teachers from the 2011--2012 Schools and Staffing Survey. Hierarchical ordinal logistic regression was utilized to analyze…

Victimization and Health Risk Factors among Weapon-Carrying Youth

ERIC Educational Resources Information Center

Stayton, Catherine; McVeigh, Katharine H.; Olson, E. Carolyn; Perkins, Krystal; Kerker, Bonnie D.

2011-01-01

Objective: To compare health risks of 2 subgroups of weapon carriers: victimized and nonvictimized youth. Methods: 2003-2007 NYC Youth Risk Behavior Surveys were analyzed using bivariate analyses and multinomial logistic regression. Results: Among NYC teens, 7.5% reported weapon carrying without victimization; 6.9% reported it with victimization.…

Logistic models--an odd(s) kind of regression.

PubMed

Jupiter, Daniel C

2013-01-01

The logistic regression model bears some similarity to the multivariable linear regression with which we are familiar. However, the differences are great enough to warrant a discussion of the need for and interpretation of logistic regression. Copyright © 2013 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Modeling health survey data with excessive zero and K responses.

PubMed

Lin, Ting Hsiang; Tsai, Min-Hsiao

2013-04-30

Zero-inflated Poisson regression is a popular tool used to analyze data with excessive zeros. Although much work has already been performed to fit zero-inflated data, most models heavily depend on special features of the individual data. To be specific, this means that there is a sizable group of respondents who endorse the same answers making the data have peaks. In this paper, we propose a new model with the flexibility to model excessive counts other than zero, and the model is a mixture of multinomial logistic and Poisson regression, in which the multinomial logistic component models the occurrence of excessive counts, including zeros, K (where K is a positive integer) and all other values. The Poisson regression component models the counts that are assumed to follow a Poisson distribution. Two examples are provided to illustrate our models when the data have counts containing many ones and sixes. As a result, the zero-inflated and K-inflated models exhibit a better fit than the zero-inflated Poisson and standard Poisson regressions. Copyright © 2012 John Wiley & Sons, Ltd.

Genomic-Enabled Prediction of Ordinal Data with Bayesian Logistic Ordinal Regression.

PubMed

Montesinos-López, Osval A; Montesinos-López, Abelardo; Crossa, José; Burgueño, Juan; Eskridge, Kent

2015-08-18

Most genomic-enabled prediction models developed so far assume that the response variable is continuous and normally distributed. The exception is the probit model, developed for ordered categorical phenotypes. In statistical applications, because of the easy implementation of the Bayesian probit ordinal regression (BPOR) model, Bayesian logistic ordinal regression (BLOR) is implemented rarely in the context of genomic-enabled prediction [sample size (n) is much smaller than the number of parameters (p)]. For this reason, in this paper we propose a BLOR model using the Pólya-Gamma data augmentation approach that produces a Gibbs sampler with similar full conditional distributions of the BPOR model and with the advantage that the BPOR model is a particular case of the BLOR model. We evaluated the proposed model by using simulation and two real data sets. Results indicate that our BLOR model is a good alternative for analyzing ordinal data in the context of genomic-enabled prediction with the probit or logit link. Copyright © 2015 Montesinos-López et al.

Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

NASA Astrophysics Data System (ADS)

Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami

2017-06-01

A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.

Statistical-learning strategies generate only modestly performing predictive models for urinary symptoms following external beam radiotherapy of the prostate: A comparison of conventional and machine-learning methods

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

Yahya, Noorazrul, E-mail: noorazrul.yahya@research.uwa.edu.au; Ebert, Martin A.; Bulsara, Max

Purpose: Given the paucity of available data concerning radiotherapy-induced urinary toxicity, it is important to ensure derivation of the most robust models with superior predictive performance. This work explores multiple statistical-learning strategies for prediction of urinary symptoms following external beam radiotherapy of the prostate. Methods: The performance of logistic regression, elastic-net, support-vector machine, random forest, neural network, and multivariate adaptive regression splines (MARS) to predict urinary symptoms was analyzed using data from 754 participants accrued by TROG03.04-RADAR. Predictive features included dose-surface data, comorbidities, and medication-intake. Four symptoms were analyzed: dysuria, haematuria, incontinence, and frequency, each with three definitions (grade ≥more » 1, grade ≥ 2 and longitudinal) with event rate between 2.3% and 76.1%. Repeated cross-validations producing matched models were implemented. A synthetic minority oversampling technique was utilized in endpoints with rare events. Parameter optimization was performed on the training data. Area under the receiver operating characteristic curve (AUROC) was used to compare performance using sample size to detect differences of ≥0.05 at the 95% confidence level. Results: Logistic regression, elastic-net, random forest, MARS, and support-vector machine were the highest-performing statistical-learning strategies in 3, 3, 3, 2, and 1 endpoints, respectively. Logistic regression, MARS, elastic-net, random forest, neural network, and support-vector machine were the best, or were not significantly worse than the best, in 7, 7, 5, 5, 3, and 1 endpoints. The best-performing statistical model was for dysuria grade ≥ 1 with AUROC ± standard deviation of 0.649 ± 0.074 using MARS. For longitudinal frequency and dysuria grade ≥ 1, all strategies produced AUROC>0.6 while all haematuria endpoints and longitudinal incontinence models produced AUROC<0.6. Conclusions: Logistic regression and MARS were most likely to be the best-performing strategy for the prediction of urinary symptoms with elastic-net and random forest producing competitive results. The predictive power of the models was modest and endpoint-dependent. New features, including spatial dose maps, may be necessary to achieve better models.« less

Confidential donation confirmation as an alternative to confidential unit exclusion: 15 months experience of the HEMOMINAS foundation.

PubMed

Loureiro, Flávia Cristine Martineli; Oliveira, Cláudia Di Lorenzo; Proietti, Anna Bárbara F Carneiro; Proietti, Fernando Augusto

2011-01-01

Confidential unit exclusion remains a controversial strategy to reduce the residual risk of transfusion-transmitted infections. This study aimed to analyze confidential unit exclusion from its development in a large institution in light of confidential donation confirmation. Data of individuals who donated from October 1, 2008 to December 31, 2009 were analyzed in a case-control study. The serological results and sociodemographic characteristics of donors who did not confirm their donations were compared to those who did. Variables with p-values < 0.20 in univariate analysis were included in a logistic multivariate analysis. In the univariate analysis there was a statically significant association between positive serological results and response to confidential donation confirmation of "No". Donation type, (firsttime or return donor - OR 1.69, CI 1.37-2.09), gender (OR 1.66, CI 1.35-2.04), education level (OR 2.82, CI 2.30-3.47) and ethnic background (OR 0.67, CI 0.55-0.82) were included in the final logistic regression model. In all logistic regression models analyzed, the serological suitability and confidential donation confirmation were not found to be statistically associated. The adoption of new measures of clinical classification such as audiovisual touch-screen computer-assisted self-administered interviews might be more effective than confidential unit exclusion in the identification of donor risk behavior. The requirement that transfusion services continue to use confidential unit exclusion needs to be debated in countries where more specific and sensitive clinical and serological screening methods are available. Our findings suggest that there are not enough benefits to justify continued use of confidential donation confirmation in the analyzed institution.

Confidential donation confirmation as an alternative to confidential unit exclusion: 15 months experience of the HEMOMINAS foundation

PubMed Central

Loureiro, Flávia Cristine Martineli; Oliveira, Cláudia Di Lorenzo; Proietti, Anna Bárbara F. Carneiro; Proietti, Fernando Augusto

2011-01-01

Background Confidential unit exclusion remains a controversial strategy to reduce the residual risk of transfusion-transmitted infections. Objective This study aimed to analyze confidential unit exclusion from its development in a large institution in light of confidential donation confirmation. Methods Data of individuals who donated from October 1, 2008 to December 31, 2009 were analyzed in a case-control study. The serological results and sociodemographic characteristics of donors who did not confirm their donations were compared to those who did. Variables with p-values < 0.20 in univariate analysis were included in a logistic multivariate analysis. Results In the univariate analysis there was a statically significant association between positive serological results and response to confidential donation confirmation of "No". Donation type, (firsttime or return donor - OR 1.69, CI 1.37-2.09), gender (OR 1.66, CI 1.35-2.04), education level (OR 2.82, CI 2.30-3.47) and ethnic background (OR 0.67, CI 0.55-0.82) were included in the final logistic regression model. In all logistic regression models analyzed, the serological suitability and confidential donation confirmation were not found to be statistically associated. The adoption of new measures of clinical classification such as audiovisual touch-screen computer-assisted self-administered interviews might be more effective than confidential unit exclusion in the identification of donor risk behavior. The requirement that transfusion services continue to use confidential unit exclusion needs to be debated in countries where more specific and sensitive clinical and serological screening methods are available. Conclusion Our findings suggest that there are not enough benefits to justify continued use of confidential donation confirmation in the analyzed institution. PMID:23049316

PARAMETRIC AND NON PARAMETRIC (MARS: MULTIVARIATE ADDITIVE REGRESSION SPLINES) LOGISTIC REGRESSIONS FOR PREDICTION OF A DICHOTOMOUS RESPONSE VARIABLE WITH AN EXAMPLE FOR PRESENCE/ABSENCE OF AMPHIBIANS

EPA Science Inventory

The purpose of this report is to provide a reference manual that could be used by investigators for making informed use of logistic regression using two methods (standard logistic regression and MARS). The details for analyses of relationships between a dependent binary response ...

Predicting U.S. Army Reserve Unit Manning Using Market Demographics

DTIC Science & Technology

2015-06-01

develops linear regression , classification tree, and logistic regression models to determine the ability of the location to support manning requirements... logistic regression model delivers predictive results that allow decision-makers to identify locations with a high probability of meeting unit...manning requirements. The recommendation of this thesis is that the USAR implement the logistic regression model. 14. SUBJECT TERMS U.S

An appraisal of convergence failures in the application of logistic regression model in published manuscripts.

PubMed

Yusuf, O B; Bamgboye, E A; Afolabi, R F; Shodimu, M A

2014-09-01

Logistic regression model is widely used in health research for description and predictive purposes. Unfortunately, most researchers are sometimes not aware that the underlying principles of the techniques have failed when the algorithm for maximum likelihood does not converge. Young researchers particularly postgraduate students may not know why separation problem whether quasi or complete occurs, how to identify it and how to fix it. This study was designed to critically evaluate convergence issues in articles that employed logistic regression analysis published in an African Journal of Medicine and medical sciences between 2004 and 2013. Problems of quasi or complete separation were described and were illustrated with the National Demographic and Health Survey dataset. A critical evaluation of articles that employed logistic regression was conducted. A total of 581 articles was reviewed, of which 40 (6.9%) used binary logistic regression. Twenty-four (60.0%) stated the use of logistic regression model in the methodology while none of the articles assessed model fit. Only 3 (12.5%) properly described the procedures. Of the 40 that used the logistic regression model, the problem of convergence occurred in 6 (15.0%) of the articles. Logistic regression tends to be poorly reported in studies published between 2004 and 2013. Our findings showed that the procedure may not be well understood by researchers since very few described the process in their reports and may be totally unaware of the problem of convergence or how to deal with it.

Discontinuation among University Students in Southern Thailand

ERIC Educational Resources Information Center

Sittichai, Ruthaychonnee; Tongkumchum, Phattrawan; McNeil, Nittaya

2009-01-01

This study uses a statistical model to account for the pattern of discontinuation of university study at Pattani campus of Prince of Songkla University (PSU) in southern Thailand. University records for 11,408 bachelor degree students enrolled between 1999 and 2006 were used. Discontinuation rates were analyzed by using a logistic regression model…

Health Risk Behaviors in a Representative Sample of Bisexual and Heterosexual Female High School Students in Massachusetts

ERIC Educational Resources Information Center

White Hughto, Jaclyn M.; Biello, Katie B.; Reisner, Sari L.; Perez-Brumer, Amaya; Heflin, Katherine J.; Mimiaga, Matthew J.

2016-01-01

Background: Differences in sexual health-related outcomes by sexual behavior and identity remain underinvestigated among bisexual female adolescents. Methods: Data from girls (N?=?875) who participated in the Massachusetts Youth Risk Behavior Surveillance survey were analyzed. Weighted logistic regression models were fit to examine sexual and…

Who Gets Market Supplements? Gender Differences within a Large Canadian University

ERIC Educational Resources Information Center

Doucet, Christine; Durand, Claire; Smith, Michael

2008-01-01

This study examines the gender pay gap among university faculty by analyzing gender differences in one component of faculty members' salaries--"market premiums." The data were collected during the Fall of 2002 using a survey of faculty at a single Canadian research university. Correspondence analysis and logistic regression analysis were…

The Differential Effects of Preschool: Evidence from Virginia

ERIC Educational Resources Information Center

Huang, Francis L.; Invernizzi, Marcia A.; Drake, E. Allison

2012-01-01

This study investigated the differential and persistent effects of a state-funded pre-K program, the Virginia Preschool Initiative (VPI). We analyzed data from a cohort of over 60,000 students nested in approximately 1000 schools from the beginning of kindergarten to the end of first grade using two-level hierarchical logistic regression models.…

Motivation to Learn among Older Adults in Taiwan

ERIC Educational Resources Information Center

Chang, Dian-Fu; Lin, Sung-Po

2011-01-01

This study analyzed the survey on adults administered by the Ministry of Education in Taiwan in 2008, and logistic regression analysis showed a close relationship between learning motivations of older adults. The finding revealed that the higher age or the lower education attainment of older adults, the lower their learning motivation. The…

Academic Success for Student Veterans Enrolled in Two-Year Colleges

ERIC Educational Resources Information Center

Chan, Hsun-Yu

2018-01-01

This study examined the relationship between college readiness of student veterans and retention, graduation, or transfer. I analyzed transcript and administrative data for student veterans who used GI Bill benefits at a public two-year college in Wisconsin. Results from logistic regression show that successful course completion rate (earning a C…

The Effect of Loans on the Persistence and Attainment of Community College Students

ERIC Educational Resources Information Center

Dowd, Alicia C.; Coury, Tarek

2006-01-01

This study informs public policies regarding the use of subsidized loans as financial aid for community college students. Using logistic regression, it analyzes the National Center for Education Statistics' Beginning Postsecondary Students (BPS 90/94) data to predict persistence to the second year of college and associate's degree attainment over…

College Student Persistence to Degree: The Burden of Debt

ERIC Educational Resources Information Center

Robb, Cliff A.; Moody, Beth; Abdel-Ghany, Mohamed

2012-01-01

Data collected from two major universities (one in the Midwest and one in the Southeast) in the United States were used to analyze student persistence behavior and perceptions of debt. Results from four separate logistic regression analyses suggested that financial factors play a significant role in student persistence behavior as well as in…

Associations between Smoking and Extreme Dieting among Adolescents

ERIC Educational Resources Information Center

Seo, Dong-Chul; Jiang, Nan

2009-01-01

This study examined the association between cigarette smoking and dieting behaviors and trends in that association among US adolescents in grades 9-12 between 1999 and 2007. Youth Risk Behavior Survey datasets were analyzed using the multivariable logistic regression method. The sample size of each survey year ranged from 13,554 to 15,273 with…

Analyzing Army Reserve Unsatisfactory Participants through Logistic Regression

DTIC Science & Technology

2012-06-08

Unsatisfactory Participants by State/Territory ...............................45 Figure 14. Observed vs . Expected Unsatisfactory Participants by Grade...47 Figure 15. Observed vs . Expected Unsatisfactory Participants by Age ............................47 x TABLES Page Table...rank, and previous military experience. “A typical 1995-96 USAR Unsatisfactory Participant was a white, unmarried male whose highest level of

American Youths' Access to Substance Abuse Treatment: Does Type of Treatment Facility Matter?

ERIC Educational Resources Information Center

Lo, Celia C.; Cheng, Tyrone C.

2013-01-01

Using data from the 2007 National Survey on Drug Use and Health, this study examines whether several social exclusion and psychological factors affect adolescents' receipt of substance abuse treatment. Multinomial logistic regression techniques were used to analyze data. The study asked how the specified factors provide pathways to receipt of…

Exploring Milk and Yogurt Selection in an Urban Universal School Breakfast Program

ERIC Educational Resources Information Center

Miller, M. Elizabeth; Kwon, Sockju

2015-01-01

Purpose/Objectives: The purpose of this study was to explore milk and yogurt selection among students participating in a School Breakfast Program. Methods: Researchers observed breakfast selection of milk, juice and yogurt in six elementary and four secondary schools. Data were analyzed using descriptive statistics and logistic regression to…

Primary Factors Related to Multiple Placements for Children in Out-of-Home Care

ERIC Educational Resources Information Center

Eggertsen, Lars

2008-01-01

Using an ecological framework, this study identified which factors related to out-of-home placements significantly influenced multiple placements for children in Utah during 2000, 2001, and 2002. Multinomial logistic regression statistical procedures and a geographical information system (GIS) were used to analyze the data. The final model…

What Does Democracy Mean? Correlates of Adolescents' Views

ERIC Educational Resources Information Center

Flanagan, Constance A.; Gallay, Leslie S.; Gill, Sukhdeep; Gallay, Erin; Nti, Naana

2005-01-01

The open-ended responses of 701 7th to 12th graders to the question "What does democracy mean to you?" were analyzed. In logistic regressions, age, parental education, political discussions, and participation in extracurricular activities distinguished youth who could define democracy (53%) from those who could not. Case clustering revealed three…

Food Insecurity in U.S. Households That Include Children with Disabilities

ERIC Educational Resources Information Center

Sonik, Rajan; Parish, Susan L.; Ghosh, Subharati; Igdalsky, Leah

2016-01-01

The authors examined food insecurity in households including children with disabilities, analyzing data from the 2004 and 2008 panels of the Survey of Income and Program Participation, which included 24,729 households with children, 3,948 of which had children with disabilities. Logistic regression models were used to estimate the likelihood of…

Freshman Year Dropouts: Interactions between Student and School Characteristics and Student Dropout Status

ERIC Educational Resources Information Center

Zvoch, Keith

2006-01-01

Data from a large school district in the southwestern United States were analyzed to investigate relations between student and school characteristics and high school freshman dropout patterns. Application of a multilevel logistic regression model to student dropout data revealed evidence of school-to-school differences in student dropout rates and…

Factors Affecting Code Status in a University Hospital Intensive Care Unit

ERIC Educational Resources Information Center

Van Scoy, Lauren Jodi; Sherman, Michael

2013-01-01

The authors collected data on diagnosis, hospital course, and end-of-life preparedness in patients who died in the intensive care unit (ICU) with "full code" status (defined as receiving cardiopulmonary resuscitation), compared with those who didn't. Differences were analyzed using binary and stepwise logistic regression. They found no…

Analyzing thresholds and efficiency with hierarchical Bayesian logistic regression.

PubMed

Houpt, Joseph W; Bittner, Jennifer L

2018-07-01

Ideal observer analysis is a fundamental tool used widely in vision science for analyzing the efficiency with which a cognitive or perceptual system uses available information. The performance of an ideal observer provides a formal measure of the amount of information in a given experiment. The ratio of human to ideal performance is then used to compute efficiency, a construct that can be directly compared across experimental conditions while controlling for the differences due to the stimuli and/or task specific demands. In previous research using ideal observer analysis, the effects of varying experimental conditions on efficiency have been tested using ANOVAs and pairwise comparisons. In this work, we present a model that combines Bayesian estimates of psychometric functions with hierarchical logistic regression for inference about both unadjusted human performance metrics and efficiencies. Our approach improves upon the existing methods by constraining the statistical analysis using a standard model connecting stimulus intensity to human observer accuracy and by accounting for variability in the estimates of human and ideal observer performance scores. This allows for both individual and group level inferences. Copyright © 2018 Elsevier Ltd. All rights reserved.

Factors associated with young adults' knowledge regarding family history of Stroke 1

PubMed Central

Lima, Maria Jose Melo Ramos; Moreira, Thereza Maria Magalhães; Florêncio, Raquel Sampaio; Braga, Predro

2016-01-01

ABSTRACT Objective: to analyze the factors associated with young adults' knowledge regarding family history of stroke. Method: an analytical transversal study, with 579 young adults from state schools, with collection of sociodemographic, clinical and risk factor-related variables, analyzed using logistic regression (backward elimination). Results: a statistical association was detected between age, civil status, and classification of arterial blood pressure and abdominal circumference with knowledge of family history of stroke. In the final logistic regression model, a statistical association was observed between knowledge regarding family history of stroke and the civil status of having a partner (ORa=1.61[1.07-2.42]; p=0.023), abdominal circumference (ORa=0.98[0.96-0.99]; p=0.012) and normal arterial blood pressure (ORa=2.56[1.19-5.52]; p=0.016). Conclusion: an association was observed between socioeconomic factors and risk factors for stroke and knowledge of family history of stroke, suggesting the need for health education or even educational programs on this topic for the clientele in question. PMID:27878217

Seroprevalence of human hydatidosis using ELISA method in qom province, central iran.

PubMed

Rakhshanpour, A; Harandi, M Fasihi; Moazezi, Ss; Rahimi, Mt; Mohebali, M; Mowlavi, Ghh; Babaei, Z; Ariaeipour, M; Heidari, Z; Rokni, Mb

2012-01-01

The objective of this study was to determine the prevalence of cystic echinococcosis (CE) in Qom Province, central Iran using ELISA test. Overall, 1564 serum samples (800 males and 764 females) were collected from selected subjects by randomized cluster sampling in 2011-2012. Sera were analyzed by ELISA test using AgB. Before sampling, a questionnaire was filled out for each case. Data were analyzed using Chi-square test and multivariate logistic regression for risk factors analysis. Seropositivity was 1.6% (25 cases). Males (2.2%) showed significantly more positivity than females (0.9%) (P= 0.03). There was no significant association between CE seropositivity and age group, occupation, and region. Age group of 30-60 years encompassed the highest rate of positivity. The seropositivity of CE was 2.1% and 1.2% for urban and rural cases respectively. Binary logistic regression showed that males were 2.5 times at higher risk for infection than females. Although seroprevalence of CE is relatively low in Qom Province, yet due to the importance of the disease, all preventive measures should be taken into consideration.

Risk factor for preterm labor in Haji Adam Malik General Hospital, Pirngadi General hospital and satellite hospitals in Medan from January 2014 to December 2016

NASA Astrophysics Data System (ADS)

Sukatendel, K.; Hasibuan, C. L.; Pasaribu, H. P.; Sihite, H.; Ardyansah, E.; Situmorang, M. F.

2018-03-01

In 2010, Indonesia was ranked fifth in the world for the number of premature birth. Prematurity is a multifactorial problem. Preterm Labor (PTL) can occur spontaneously without a clear cause. Preventing PTL, its associated risk factors must be recognized first. To analyze risk factors associated with the incidence of PTL. It is a cross sectional study using secondary data obtained from medical records in Haji Adam Malik general hospital, Pirngadi general hospital and satellite hospitals in Medan from January 2014 to December 2016. Data were analyzed using chi-square method and logistic regression test. 148 cases for each group of preterm labor and obtained term laborin this study. Using the logistic regression test, three factors with astrong association to the incidence of identifiedpreterm labor. Antenatal Care frequency (OR 2,326; CI 95%), leucorrhea (OR 6,291; 95%), and premature rupture of membrane (OR 9,755; CI 95%). In conclusion, antenatal care frequency, leucorrhea, and history of premature rupture of themembrane may increase the incidence of Preterm Labor (PTL).

Analyzing musculoskeletal neck pain, measured as present pain and periods of pain, with three different regression models: a cohort study.

PubMed

Grimby-Ekman, Anna; Andersson, Eva M; Hagberg, Mats

2009-06-19

In the literature there are discussions on the choice of outcome and the need for more longitudinal studies of musculoskeletal disorders. The general aim of this longitudinal study was to analyze musculoskeletal neck pain, in a group of young adults. Specific aims were to determine whether psychosocial factors, computer use, high work/study demands, and lifestyle are long-term or short-term factors for musculoskeletal neck pain, and whether these factors are important for developing or ongoing musculoskeletal neck pain. Three regression models were used to analyze the different outcomes. Pain at present was analyzed with a marginal logistic model, for number of years with pain a Poisson regression model was used and for developing and ongoing pain a logistic model was used. Presented results are odds ratios and proportion ratios (logistic models) and rate ratios (Poisson model). The material consisted of web-based questionnaires answered by 1204 Swedish university students from a prospective cohort recruited in 2002. Perceived stress was a risk factor for pain at present (PR = 1.6), for developing pain (PR = 1.7) and for number of years with pain (RR = 1.3). High work/study demands was associated with pain at present (PR = 1.6); and with number of years with pain when the demands negatively affect home life (RR = 1.3). Computer use pattern (number of times/week with a computer session > or = 4 h, without break) was a risk factor for developing pain (PR = 1.7), but also associated with pain at present (PR = 1.4) and number of years with pain (RR = 1.2). Among life style factors smoking (PR = 1.8) was found to be associated to pain at present. The difference between men and women in prevalence of musculoskeletal pain was confirmed in this study. It was smallest for the outcome ongoing pain (PR = 1.4) compared to pain at present (PR = 2.4) and developing pain (PR = 2.5). By using different regression models different aspects of neck pain pattern could be addressed and the risk factors impact on pain pattern was identified. Short-term risk factors were perceived stress, high work/study demands and computer use pattern (break pattern). Those were also long-term risk factors. For developing pain perceived stress and computer use pattern were risk factors.

Analyzing musculoskeletal neck pain, measured as present pain and periods of pain, with three different regression models: a cohort study

PubMed Central

Grimby-Ekman, Anna; Andersson, Eva M; Hagberg, Mats

2009-01-01

Background In the literature there are discussions on the choice of outcome and the need for more longitudinal studies of musculoskeletal disorders. The general aim of this longitudinal study was to analyze musculoskeletal neck pain, in a group of young adults. Specific aims were to determine whether psychosocial factors, computer use, high work/study demands, and lifestyle are long-term or short-term factors for musculoskeletal neck pain, and whether these factors are important for developing or ongoing musculoskeletal neck pain. Methods Three regression models were used to analyze the different outcomes. Pain at present was analyzed with a marginal logistic model, for number of years with pain a Poisson regression model was used and for developing and ongoing pain a logistic model was used. Presented results are odds ratios and proportion ratios (logistic models) and rate ratios (Poisson model). The material consisted of web-based questionnaires answered by 1204 Swedish university students from a prospective cohort recruited in 2002. Results Perceived stress was a risk factor for pain at present (PR = 1.6), for developing pain (PR = 1.7) and for number of years with pain (RR = 1.3). High work/study demands was associated with pain at present (PR = 1.6); and with number of years with pain when the demands negatively affect home life (RR = 1.3). Computer use pattern (number of times/week with a computer session ≥ 4 h, without break) was a risk factor for developing pain (PR = 1.7), but also associated with pain at present (PR = 1.4) and number of years with pain (RR = 1.2). Among life style factors smoking (PR = 1.8) was found to be associated to pain at present. The difference between men and women in prevalence of musculoskeletal pain was confirmed in this study. It was smallest for the outcome ongoing pain (PR = 1.4) compared to pain at present (PR = 2.4) and developing pain (PR = 2.5). Conclusion By using different regression models different aspects of neck pain pattern could be addressed and the risk factors impact on pain pattern was identified. Short-term risk factors were perceived stress, high work/study demands and computer use pattern (break pattern). Those were also long-term risk factors. For developing pain perceived stress and computer use pattern were risk factors. PMID:19545386

Logistic Regression: Concept and Application

ERIC Educational Resources Information Center

Cokluk, Omay

2010-01-01

The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.

Estimation of Recurrence of Colorectal Adenomas with Dependent Censoring Using Weighted Logistic Regression

PubMed Central

Hsu, Chiu-Hsieh; Li, Yisheng; Long, Qi; Zhao, Qiuhong; Lance, Peter

2011-01-01

In colorectal polyp prevention trials, estimation of the rate of recurrence of adenomas at the end of the trial may be complicated by dependent censoring, that is, time to follow-up colonoscopy and dropout may be dependent on time to recurrence. Assuming that the auxiliary variables capture the dependence between recurrence and censoring times, we propose to fit two working models with the auxiliary variables as covariates to define risk groups and then extend an existing weighted logistic regression method for independent censoring to each risk group to accommodate potential dependent censoring. In a simulation study, we show that the proposed method results in both a gain in efficiency and reduction in bias for estimating the recurrence rate. We illustrate the methodology by analyzing a recurrent adenoma dataset from a colorectal polyp prevention trial. PMID:22065985

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

PubMed Central

Weiss, Brandi A.; Dardick, William

2015-01-01

This article introduces an entropy-based measure of data–model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data–model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data–model fit to assess how well logistic regression models classify cases into observed categories. PMID:29795897

Logistic regression applied to natural hazards: rare event logistic regression with replications

NASA Astrophysics Data System (ADS)

Guns, M.; Vanacker, V.

2012-06-01

Statistical analysis of natural hazards needs particular attention, as most of these phenomena are rare events. This study shows that the ordinary rare event logistic regression, as it is now commonly used in geomorphologic studies, does not always lead to a robust detection of controlling factors, as the results can be strongly sample-dependent. In this paper, we introduce some concepts of Monte Carlo simulations in rare event logistic regression. This technique, so-called rare event logistic regression with replications, combines the strength of probabilistic and statistical methods, and allows overcoming some of the limitations of previous developments through robust variable selection. This technique was here developed for the analyses of landslide controlling factors, but the concept is widely applicable for statistical analyses of natural hazards.

Large unbalanced credit scoring using Lasso-logistic regression ensemble.

PubMed

Wang, Hong; Xu, Qingsong; Zhou, Lifeng

2015-01-01

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data.

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression.

PubMed

Weiss, Brandi A; Dardick, William

2016-12-01

This article introduces an entropy-based measure of data-model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify the quality of classification and separation of group membership. Entropy complements preexisting measures of data-model fit and provides unique information not contained in other measures. Hypothetical data scenarios, an applied example, and Monte Carlo simulation results are used to demonstrate the application of entropy in logistic regression. Entropy should be used in conjunction with other measures of data-model fit to assess how well logistic regression models classify cases into observed categories.

Logistic Regression and Path Analysis Method to Analyze Factors influencing Students’ Achievement

NASA Astrophysics Data System (ADS)

Noeryanti, N.; Suryowati, K.; Setyawan, Y.; Aulia, R. R.

2018-04-01

Students' academic achievement cannot be separated from the influence of two factors namely internal and external factors. The first factors of the student (internal factors) consist of intelligence (X1), health (X2), interest (X3), and motivation of students (X4). The external factors consist of family environment (X5), school environment (X6), and society environment (X7). The objects of this research are eighth grade students of the school year 2016/2017 at SMPN 1 Jiwan Madiun sampled by using simple random sampling. Primary data are obtained by distributing questionnaires. The method used in this study is binary logistic regression analysis that aims to identify internal and external factors that affect student’s achievement and how the trends of them. Path Analysis was used to determine the factors that influence directly, indirectly or totally on student’s achievement. Based on the results of binary logistic regression, variables that affect student’s achievement are interest and motivation. And based on the results obtained by path analysis, factors that have a direct impact on student’s achievement are students’ interest (59%) and students’ motivation (27%). While the factors that have indirect influences on students’ achievement, are family environment (97%) and school environment (37).

Power and Sample Size Calculations for Logistic Regression Tests for Differential Item Functioning

ERIC Educational Resources Information Center

Li, Zhushan

2014-01-01

Logistic regression is a popular method for detecting uniform and nonuniform differential item functioning (DIF) effects. Theoretical formulas for the power and sample size calculations are derived for likelihood ratio tests and Wald tests based on the asymptotic distribution of the maximum likelihood estimators for the logistic regression model.…

A Methodology for Generating Placement Rules that Utilizes Logistic Regression

ERIC Educational Resources Information Center

Wurtz, Keith

2008-01-01

The purpose of this article is to provide the necessary tools for institutional researchers to conduct a logistic regression analysis and interpret the results. Aspects of the logistic regression procedure that are necessary to evaluate models are presented and discussed with an emphasis on cutoff values and choosing the appropriate number of…

Comparison of standard maximum likelihood classification and polytomous logistic regression used in remote sensing

Treesearch

John Hogland; Nedret Billor; Nathaniel Anderson

2013-01-01

Discriminant analysis, referred to as maximum likelihood classification within popular remote sensing software packages, is a common supervised technique used by analysts. Polytomous logistic regression (PLR), also referred to as multinomial logistic regression, is an alternative classification approach that is less restrictive, more flexible, and easy to interpret. To...

Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble

PubMed Central

Wang, Hong; Xu, Qingsong; Zhou, Lifeng

2015-01-01

Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal with credit scoring problems. In this research, the data is first balanced and diversified by clustering and bagging algorithms. Then we apply a Lasso-logistic regression learning ensemble to evaluate the credit risks. We show that the proposed algorithm outperforms popular credit scoring models such as decision tree, Lasso-logistic regression and random forests in terms of AUC and F-measure. We also provide two importance measures for the proposed model to identify important variables in the data. PMID:25706988

Analysis of the discriminative methods for diagnosis of benign and malignant solitary pulmonary nodules based on serum markers.

PubMed

Wang, Wanping; Liu, Mingyue; Wang, Jing; Tian, Rui; Dong, Junqiang; Liu, Qi; Zhao, Xianping; Wang, Yuanfang

2014-01-01

Screening indexes of tumor serum markers for benign and malignant solitary pulmonary nodules (SPNs) were analyzed to find the optimum method for diagnosis. Enzyme-linked immunosorbent assays, an automatic immune analyzer and radioimmunoassay methods were used to examine the levels of 8 serum markers in 164 SPN patients, and the sensitivity for differential diagnosis of malignant or benign SPN was compared for detection using a single plasma marker or a combination of markers. The results for serological indicators that closely relate to benign and malignant SPNs were screened using the Fisher discriminant analysis and a non-conditional logistic regression analysis method, respectively. The results were then verified by the k-means clustering analysis method. The sensitivity when using a combination of serum markers to detect SPN was higher than that using a single marker. By Fisher discriminant analysis, cytokeratin 19 fragments (CYFRA21-1), carbohydrate antigen 125 (CA125), squamous cell carcinoma antigen (SCC) and breast cancer antigen (CA153), which relate to the benign and malignant SPNs, were screened. Through non-conditional logistic regression analysis, CYFRA21-1, SCC and CA153 were obtained. Using the k-means clustering analysis, the cophenetic correlation coefficient (0.940) obtained by the Fisher discriminant analysis was higher than that obtained with logistic regression analysis (0.875). This study indicated that the Fisher discriminant analysis functioned better in screening out serum markers to recognize the benign and malignant SPN. The combined detection of CYFRA21-1, CA125, SCC and CA153 is an effective way to distinguish benign and malignant SPN, and will find an important clinical application in the early diagnosis of SPN. © 2014 S. Karger GmbH, Freiburg.

Analysis of mortality in a cohort of 650 cases of bacteremic osteoarticular infections.

PubMed

Gomez-Junyent, Joan; Murillo, Oscar; Grau, Imma; Benavent, Eva; Ribera, Alba; Cabo, Xavier; Tubau, Fe; Ariza, Javier; Pallares, Roman

2018-01-31

The mortality of patients with bacteremic osteoarticular infections (B-OAIs) is poorly understood. Whether certain types of OAIs carry higher mortality or interventions like surgical debridement can improve prognosis, are unclarified questions. Retrospective analysis of a prospective cohort of patients with B-OAIs treated at a teaching hospital in Barcelona (1985-2014), analyzing mortality (30-day case-fatality rate). B-OAIs were categorized as peripheral septic arthritis or other OAIs. Factors influencing mortality were analyzed using logistic regression models. The association of surgical debridement with mortality in patients with peripheral septic arthritis was evaluated with a multivariate logistic regression model and a propensity score matching analysis. Among 650 cases of B-OAIs, mortality was 12.2% (41.8% of deaths within 7 days). Compared with other B-OAI, cases of peripheral septic arthritis were associated with higher mortality (18.6% vs 8.3%, p < 0.001). In a multiple logistic regression model, peripheral septic arthritis was an independent predictor of mortality (adjusted odds ratio [OR] 2.12; 95% CI: 1.22-3.69; p = 0.008). Cases with peripheral septic arthritis managed with surgical debridement had lower mortality than those managed without surgery (14.7% vs 33.3%; p = 0.003). Surgical debridement was associated with reduced mortality after adjusting for covariates (adjusted OR 0.23; 95% CI: 0.09-0.57; p = 0.002) and in the propensity score matching analysis (OR 0.81; 95% CI: 0.68-0.96; p = 0.014). Among patients with B-OAIs, mortality was greater in those with peripheral septic arthritis. Surgical debridement was associated with decreased mortality in cases of peripheral septic arthritis. Copyright © 2018 Elsevier Inc. All rights reserved.

Effect of duration of denervation on outcomes of ansa-recurrent laryngeal nerve reinnervation.

PubMed

Li, Meng; Chen, Shicai; Wang, Wei; Chen, Donghui; Zhu, Minhui; Liu, Fei; Zhang, Caiyun; Li, Yan; Zheng, Hongliang

2014-08-01

To investigate the efficacy of laryngeal reinnervation with ansa cervicalis among unilateral vocal fold paralysis (UVFP) patients with different denervation durations. We retrospectively reviewed 349 consecutive UVFP cases of delayed ansa cervicalis to the recurrent laryngeal nerve (RLN) anastomosis. Potential influencing factors were analyzed in multivariable logistic regression analysis. Stratification analysis performed was aimed at one of the identified significant variables: denervation duration. Videostroboscopy, perceptual evaluation, acoustic analysis, maximum phonation time (MPT), and laryngeal electromyography (EMG) were performed preoperatively and postoperatively. Gender, age, preoperative EMG status and denervation duration were analyzed in multivariable logistic regression analysis. Stratification analysis was performed on denervation duration, which was divided into three groups according to the interval between RLN injury and reinnervation: group A, 6 to 12 months; group B, 12 to 24 months; and group C, > 24 months. Age, preoperative EMG, and denervation duration were identified as significant variables in multivariable logistic regression analysis. Stratification analysis on denervation duration showed significant differences between group A and C and between group B and C (P < 0.05)-but showed no significant difference between group A and B (P > 0.05) with regard to parameters overall grade, jitter, shimmer, noise-to-harmonics ratio, MPT, and postoperative EMG. In addition, videostroboscopic and laryngeal EMG data, perceptual and acoustic parameters, and MPT values were significantly improved postoperatively in each denervation duration group (P < 0.01). Although delayed laryngeal reinnervation is proved valid for UVFP, surgical outcome is better if the procedure is performed within 2 years after nerve injury than that over 2 years. © 2014 The American Laryngological, Rhinological and Otological Society, Inc.

Clinical multifactorial analysis of early postoperative seizures in elderly patients following meningioma resection

PubMed Central

ZHANG, BO; WANG, DAN; GUO, YUNBAO; YU, JINLU

2015-01-01

The aim of the present study was to identify the major factors correlated with early postoperative seizures in elderly patients who had undergone a meningioma resection, and subsequently, to develop a logistic regression equation for assessing the seizures risk. Fourteen factors possibly correlated with early postoperative seizures in a cohort of 209 elderly patients who had undergone meningioma resection, as analyzed by multifactorial stepwise logistic regression. Phenobarbital sodium (0.1 g, intramuscularly) was administered to all 209 patients 30 min prior to undergoing surgery. All the patients had no previous history of seizures. The correlation of the 14 clinical factors (gender, tumor site, dyskinesia, peritumoral brain edema (PTBE), tumor diameter, pre- and postoperative prophylaxes, surgery time, tumor adhesion, circumscription, blood supply, intraoperative transfusion, original site of the tumor and dysphasia) was assessed in association with the risk for post-operative seizures. Tumor diameter, postoperative prophylactic antiepileptic drug (PPAD) administration, PTBE and tumor site were entered as risk factors into a mathematical regression model. The odds ratio (OR) of the tumor diameter was >1, and PPAD administration showed an OR >1, relative to a non-prophylactic group. A logistic regression equation was obtained and the sensitivity, specificity and misdiagnosis rates were 91.4, 74.3 and 25.7%, respectively. Tumor diameter, PPAD administration, PTBE and tumor site were closely correlated with early postoperative seizures; PTBE and PPAD administration were risk and protective factors, respectively. PMID:26137257

Lack of Correlation between Metallic Elements Analyzed in Hair by ICP-MS and Autism

ERIC Educational Resources Information Center

De Palma, Giuseppe; Catalani, Simona; Franco, Anna; Brighenti, Maurizio; Apostoli, Pietro

2012-01-01

A cross-sectional case-control study was carried out to evaluate the concentrations of metallic elements in the hair of 44 children with diagnosis of autism and 61 age-balanced controls. Unadjusted comparisons showed higher concentrations of molybdenum, lithium and selenium in autistic children. Logistic regression analysis confirmed the role of…

Predictors of Gender Inequalities in the Rank of Full Professor

ERIC Educational Resources Information Center

Heijstra, Thamar; Bjarnason, Thoroddur; Rafnsdóttir, Gudbjörg Linda

2015-01-01

This article examines whether age, work-related, and family-related predictors explain differences in the academic advancement of women and men in Iceland. Survey data were analyzed by binary logistic regression. The findings put that women climb the academic career ladder at a slower pace than men. This finding puts one of the widely known…

A Multilevel Study of Students' Motivations of Studying Accounting: Implications for Employers

ERIC Educational Resources Information Center

Law, Philip; Yuen, Desmond

2012-01-01

Purpose: The purpose of this study is to examine the influence of factors affecting students' choice of accounting as a study major in Hong Kong. Design/methodology/approach: Multinomial logistic regression and Hierarchical Generalized Linear Modeling (HGLM) are used to analyze the survey data for the level one and level two data, which is the…

U.S. Emergency Department Admissions for Nontraumatic Dental Conditions for Individuals with Intellectual and Developmental Disabilities

ERIC Educational Resources Information Center

Chi, Donald L.; Masterson, Erin E.; Wong, Jacqueline J.

2014-01-01

The authors hypothesized that individuals with intellectual and developmental disabilities (IDDs) are more likely to have an emergency department (ED) admission for nontraumatic dental conditions (NTDCs). The authors analyzed 2009 U.S. National Emergency Department Sample data and ran logistic regression models for children ages 3-17 years and…

Different Pathways to Juvenile Delinquency: Characteristics of Early and Late Starters in a Sample of Previously Incarcerated Youth

ERIC Educational Resources Information Center

Alltucker, Kevin W.; Bullis, Michael; Close, Daniel; Yovanoff, Paul

2006-01-01

We examined the differences between early and late start juvenile delinquents in a sample of 531 previously incarcerated youth in Oregon's juvenile justice system. Data were analyzed with logistic regression to predict early start delinquency based on four explanatory variables: foster care experience, family criminality, special education…

South Texas Mexican American Use of Traditional Folk and Mainstream Alternative Therapies

ERIC Educational Resources Information Center

Martinez, Leslie N.

2009-01-01

A telephone survey was conducted with a large sample of Mexican Americans from border (n = 1,001) and nonborder (n = 1,030) regions in Texas. Patterns of traditional folk and mainstream complementary and alternative medicine (CAM) use were analyzed with two binary logistic regressions, using gender, self-rated health, confidence in medical…

Exploring Crossing Differential Item Functioning by Gender in Mathematics Assessment

ERIC Educational Resources Information Center

Ong, Yoke Mooi; Williams, Julian; Lamprianou, Iasonas

2015-01-01

The purpose of this article is to explore crossing differential item functioning (DIF) in a test drawn from a national examination of mathematics for 11-year-old pupils in England. An empirical dataset was analyzed to explore DIF by gender in a mathematics assessment. A two-step process involving the logistic regression (LR) procedure for…

Assessing West Virginia NIPF owner preferred forest management assistance topics and delivery methods

Treesearch

Daniel J. Magill; Rory F. Fraser; David W. McGill

2003-01-01

Four hundred and fourteen non-industrial private forest (NIPF) owners in West Virginia responded to a mail survey questionnaire assessing their forest management assistance topics and delivery methods of interest. Logistic regression was used to analyze 39 independent variables in relation to the dependent variables of wanting a specific topic of forestry assistance or...

Is It Considered Violence? The Acceptability of Physical Punishment of Children in Europe

ERIC Educational Resources Information Center

Gracia, Enrique; Herrero, Juan

2008-01-01

This study analyzes correlates of the acceptability of physical punishment of children in Europe. The design was a three-level ordinal logistic regression of 10,812 people nested within 208 localities (cities), nested within 14 countries of the European Union. Results showed that higher levels of acceptability were reported by men, the older, the…

Past-Year Sexual Inactivity among Older Married Persons and Their Partners

ERIC Educational Resources Information Center

Karraker, Amelia; DeLamater, John

2013-01-01

Family scholars have focused on the onset of sexual activity early in the life course, but little is known about the cessation of sexual activity in relationships in later life. We use event-history analysis techniques and logistic regression to identify the correlates of sexual inactivity among older married men and women. We analyze data for…

HOPE or No-HOPE: Merit-Based College Scholarship Status and Financial Behaviors among College Students

ERIC Educational Resources Information Center

Goetz, Joseph W.; Mimura, Yoko; Desai, Miti P.; Cude, Brenda J.

2008-01-01

The current study explored differences in financial behaviors between college students in Georgia who retained the merit-based HOPE Scholarship and those who lost it. Logistic regression was used to analyze data from a sample of 557 undergraduate students from a large southeastern university. Students who initially had HOPE Scholarships but lost…

Who Stays and for How Long: Examining Attrition in Canadian Graduate Programs

ERIC Educational Resources Information Center

DeClou, Lindsay

2016-01-01

Attrition from Canadian graduate programs is a point of concern on a societal, institutional, and individual level. To improve retention in graduate school, a better understanding of what leads to withdrawal needs to be reached. This paper uses logistic regression and discrete-time survival analysis with time-varying covariates to analyze data…

Mental Disorders and Communication of Intent to Die in Indigenous Suicide Cases, Queensland, Australia

ERIC Educational Resources Information Center

De Leo, Diego; Milner, Allison; Sveticic, Jerneja

2012-01-01

In comparing Indigenous to non-Indigenous suicide in Australia, this study focussed on the frequency of the association between some psychiatric conditions, such as depression and alcohol abuse, and some aspect of suicidality, in particular communication of suicide intent. Logistic regression was implemented to analyze cases of Indigenous (n =…

Hot callusing for propagation of American beech by grafting

Treesearch

David W. Carey; Mary E. Mason; Paul Bloese; Jennifer L. Koch

2013-01-01

To increase grafting success rate, a hot callus grafting system was designed and implemented as part of a multiagency collaborative project to manage beech bark disease (BBD) through the establishment of regional BBD-resistant grafted seed orchards. Five years of data from over 2000 hot callus graft attempts were analyzed using a logistic regression model to determine...

An Entropy-Based Measure for Assessing Fuzziness in Logistic Regression

ERIC Educational Resources Information Center

Weiss, Brandi A.; Dardick, William

2016-01-01

This article introduces an entropy-based measure of data-model fit that can be used to assess the quality of logistic regression models. Entropy has previously been used in mixture-modeling to quantify how well individuals are classified into latent classes. The current study proposes the use of entropy for logistic regression models to quantify…

What Are the Odds of that? A Primer on Understanding Logistic Regression

ERIC Educational Resources Information Center

Huang, Francis L.; Moon, Tonya R.

2013-01-01

The purpose of this Methodological Brief is to present a brief primer on logistic regression, a commonly used technique when modeling dichotomous outcomes. Using data from the National Education Longitudinal Study of 1988 (NELS:88), logistic regression techniques were used to investigate student-level variables in eighth grade (i.e., enrolled in a…

On the Usefulness of a Multilevel Logistic Regression Approach to Person-Fit Analysis

ERIC Educational Resources Information Center

Conijn, Judith M.; Emons, Wilco H. M.; van Assen, Marcel A. L. M.; Sijtsma, Klaas

2011-01-01

The logistic person response function (PRF) models the probability of a correct response as a function of the item locations. Reise (2000) proposed to use the slope parameter of the logistic PRF as a person-fit measure. He reformulated the logistic PRF model as a multilevel logistic regression model and estimated the PRF parameters from this…

Mortality risk prediction in burn injury: Comparison of logistic regression with machine learning approaches.

PubMed

Stylianou, Neophytos; Akbarov, Artur; Kontopantelis, Evangelos; Buchan, Iain; Dunn, Ken W

2015-08-01

Predicting mortality from burn injury has traditionally employed logistic regression models. Alternative machine learning methods have been introduced in some areas of clinical prediction as the necessary software and computational facilities have become accessible. Here we compare logistic regression and machine learning predictions of mortality from burn. An established logistic mortality model was compared to machine learning methods (artificial neural network, support vector machine, random forests and naïve Bayes) using a population-based (England & Wales) case-cohort registry. Predictive evaluation used: area under the receiver operating characteristic curve; sensitivity; specificity; positive predictive value and Youden's index. All methods had comparable discriminatory abilities, similar sensitivities, specificities and positive predictive values. Although some machine learning methods performed marginally better than logistic regression the differences were seldom statistically significant and clinically insubstantial. Random forests were marginally better for high positive predictive value and reasonable sensitivity. Neural networks yielded slightly better prediction overall. Logistic regression gives an optimal mix of performance and interpretability. The established logistic regression model of burn mortality performs well against more complex alternatives. Clinical prediction with a small set of strong, stable, independent predictors is unlikely to gain much from machine learning outside specialist research contexts. Copyright © 2015 Elsevier Ltd and ISBI. All rights reserved.

Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches.

PubMed

Valle, Denis; Lima, Joanna M Tucker; Millar, Justin; Amratia, Punam; Haque, Ubydul

2015-11-04

Logistic regression is a statistical model widely used in cross-sectional and cohort studies to identify and quantify the effects of potential disease risk factors. However, the impact of imperfect tests on adjusted odds ratios (and thus on the identification of risk factors) is under-appreciated. The purpose of this article is to draw attention to the problem associated with modelling imperfect diagnostic tests, and propose simple Bayesian models to adequately address this issue. A systematic literature review was conducted to determine the proportion of malaria studies that appropriately accounted for false-negatives/false-positives in a logistic regression setting. Inference from the standard logistic regression was also compared with that from three proposed Bayesian models using simulations and malaria data from the western Brazilian Amazon. A systematic literature review suggests that malaria epidemiologists are largely unaware of the problem of using logistic regression to model imperfect diagnostic test results. Simulation results reveal that statistical inference can be substantially improved when using the proposed Bayesian models versus the standard logistic regression. Finally, analysis of original malaria data with one of the proposed Bayesian models reveals that microscopy sensitivity is strongly influenced by how long people have lived in the study region, and an important risk factor (i.e., participation in forest extractivism) is identified that would have been missed by standard logistic regression. Given the numerous diagnostic methods employed by malaria researchers and the ubiquitous use of logistic regression to model the results of these diagnostic tests, this paper provides critical guidelines to improve data analysis practice in the presence of misclassification error. Easy-to-use code that can be readily adapted to WinBUGS is provided, enabling straightforward implementation of the proposed Bayesian models.

Modifiable Lifestyle Behaviors Are Associated With Metabolic Syndrome in a Taiwanese Population.

PubMed

Lin, Kuei-Man; Chiou, Jeng-Yuan; Ko, Shu-Hua; Tan, Jung-Ying; Huang, Chien-Ning; Liao, Wen-Chun

2015-11-01

To explore associations between metabolic syndrome and modifiable lifestyle behaviors among the adult population in Taiwan. This cross-sectional study analyzed data from a nationally representative sample that participated in the 2005-2008 Nutrition and Health Survey in Taiwan. The sample (2,337 participants older than 19 years) provided data on demographic characteristics, modifiable lifestyle behaviors, anthropometric measurements, and blood chemistry panel. These data were analyzed by descriptive statistics, univariate logistic regression, and multivariate logistic regression to determine factors associated with metabolic syndrome. Metabolic syndrome had a prevalence of 25.2%, and this prevalence increased with age. In univariate regression analysis, metabolic syndrome was associated with age, living with family members, educational level, and modifiable lifestyle behaviors (smoking, drinking, betel quid chewing, and physical activity). Individuals with a smoking history and currently chewing betel quid had the highest risk for metabolic syndrome. The risk for metabolic syndrome might be reduced by public health campaigns to encourage people to quit smoking cigarettes and chewing betel quid. Implementing more modifiable lifestyle behaviors in daily life will decrease metabolic syndrome in Taiwan. Considering that betel quid chewing and tobacco smoking interact to adversely affect metabolic syndrome risk, public health campaigns against both behaviors seem to be a cost-effective and efficient health promotion strategy to reduce the prevalence rate of metabolic syndrome. © 2015 Sigma Theta Tau International.

Work performance decrements are associated with Australian working conditions, particularly the demand to work longer hours.

PubMed

Holden, Libby; Scuffham, Paul A; Hilton, Michael F; Vecchio, Nerina N; Whiteford, Harvey A

2010-03-01

To demonstrate the importance of including a range of working conditions in models exploring the association between health- and work-related performance. The Australian Work Outcomes Research Cost-benefit study cross-sectional screening data set was used to explore health-related absenteeism and work performance losses on a sample of approximately 78,000 working Australians, including available demographic and working condition factors. Data collected using the World Health Organization Health and Productivity Questionnaire were analyzed with negative binomial logistic regression and multinomial logistic regressions for absenteeism and work performance, respectively. Hours expected to work, annual wage, and job insecurity play a vital role in the association between health- and work-related performance for both work attendance and self-reported work performance. Australian working conditions are contributing to both absenteeism and low work performance, regardless of health status.

Modeling data for pancreatitis in presence of a duodenal diverticula using logistic regression

NASA Astrophysics Data System (ADS)

Dineva, S.; Prodanova, K.; Mlachkova, D.

2013-12-01

The presence of a periampullary duodenal diverticulum (PDD) is often observed during upper digestive tract barium meal studies and endoscopic retrograde cholangiopancreatography (ERCP). A few papers reported that the diverticulum had something to do with the incidence of pancreatitis. The aim of this study is to investigate if the presence of duodenal diverticula predisposes to the development of a pancreatic disease. A total 3966 patients who had undergone ERCP were studied retrospectively. They were divided into 2 groups-with and without PDD. Patients with a duodenal diverticula had a higher rate of acute pancreatitis. The duodenal diverticula is a risk factor for acute idiopathic pancreatitis. A multiple logistic regression to obtain adjusted estimate of odds and to identify if a PDD is a predictor of acute or chronic pancreatitis was performed. The software package STATISTICA 10.0 was used for analyzing the real data.

Association of school, family, and mental health characteristics with suicidal ideation among Korean adolescents.

PubMed

Lee, Gyu-Young; Choi, Yun-Jung

2015-08-01

In a cross-sectional research design, we investigated factors related to suicidal ideation in adolescents using data from the 2013 Online Survey of Youth Health Behavior in Korea. This self-report questionnaire was administered to 72,435 adolescents aged 13-18 years in middle and high school. School characteristics, family characteristics, and mental health variables were analyzed using descriptive statistics, χ(2) tests, and logistic regression. Both suicidal ideation and behavior were more common in girls. Suicidal ideation was most common in 11th grade for boys and 8th grade for girls. Across the sample, in logistic regression, suicidal ideation was predicted by low socioeconomic status, high stress, inadequate sleep, substance use, alcohol use, and smoking. Living apart from family predicted suicidal ideation in boys but not in girls. Gender- and school-grade-specific intervention programs may be useful for reducing suicidal ideation in students. © 2015 Wiley Periodicals, Inc.

Logistic regression for risk factor modelling in stuttering research.

PubMed

Reed, Phil; Wu, Yaqionq

2013-06-01

To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.

Dynamic Dimensionality Selection for Bayesian Classifier Ensembles

DTIC Science & Technology

2015-03-19

learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but much more...classifier, Generative learning, Discriminative learning, Naïve Bayes, Feature selection, Logistic regression , higher order attribute independence 16...discriminative learning of weights in an otherwise generatively learned naive Bayes classifier. WANBIA-C is very cometitive to Logistic Regression but

A review of logistic regression models used to predict post-fire tree mortality of western North American conifers

Treesearch

Travis Woolley; David C. Shaw; Lisa M. Ganio; Stephen Fitzgerald

2012-01-01

Logistic regression models used to predict tree mortality are critical to post-fire management, planning prescribed bums and understanding disturbance ecology. We review literature concerning post-fire mortality prediction using logistic regression models for coniferous tree species in the western USA. We include synthesis and review of: methods to develop, evaluate...

Preserving Institutional Privacy in Distributed binary Logistic Regression.

PubMed

Wu, Yuan; Jiang, Xiaoqian; Ohno-Machado, Lucila

2012-01-01

Privacy is becoming a major concern when sharing biomedical data across institutions. Although methods for protecting privacy of individual patients have been proposed, it is not clear how to protect the institutional privacy, which is many times a critical concern of data custodians. Built upon our previous work, Grid Binary LOgistic REgression (GLORE)1, we developed an Institutional Privacy-preserving Distributed binary Logistic Regression model (IPDLR) that considers both individual and institutional privacy for building a logistic regression model in a distributed manner. We tested our method using both simulated and clinical data, showing how it is possible to protect the privacy of individuals and of institutions using a distributed strategy.

Covariate Imbalance and Adjustment for Logistic Regression Analysis of Clinical Trial Data

PubMed Central

Ciolino, Jody D.; Martin, Reneé H.; Zhao, Wenle; Jauch, Edward C.; Hill, Michael D.; Palesch, Yuko Y.

2014-01-01

In logistic regression analysis for binary clinical trial data, adjusted treatment effect estimates are often not equivalent to unadjusted estimates in the presence of influential covariates. This paper uses simulation to quantify the benefit of covariate adjustment in logistic regression. However, International Conference on Harmonization guidelines suggest that covariate adjustment be pre-specified. Unplanned adjusted analyses should be considered secondary. Results suggest that that if adjustment is not possible or unplanned in a logistic setting, balance in continuous covariates can alleviate some (but never all) of the shortcomings of unadjusted analyses. The case of log binomial regression is also explored. PMID:24138438

Differentially private distributed logistic regression using private and public data.

PubMed

Ji, Zhanglong; Jiang, Xiaoqian; Wang, Shuang; Xiong, Li; Ohno-Machado, Lucila

2014-01-01

Privacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced. In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data. We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios. Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee.

Prevalence and Determinants of Preterm Birth in Tehran, Iran: A Comparison between Logistic Regression and Decision Tree Methods.

PubMed

Amini, Payam; Maroufizadeh, Saman; Samani, Reza Omani; Hamidi, Omid; Sepidarkish, Mahdi

2017-06-01

Preterm birth (PTB) is a leading cause of neonatal death and the second biggest cause of death in children under five years of age. The objective of this study was to determine the prevalence of PTB and its associated factors using logistic regression and decision tree classification methods. This cross-sectional study was conducted on 4,415 pregnant women in Tehran, Iran, from July 6-21, 2015. Data were collected by a researcher-developed questionnaire through interviews with mothers and review of their medical records. To evaluate the accuracy of the logistic regression and decision tree methods, several indices such as sensitivity, specificity, and the area under the curve were used. The PTB rate was 5.5% in this study. The logistic regression outperformed the decision tree for the classification of PTB based on risk factors. Logistic regression showed that multiple pregnancies, mothers with preeclampsia, and those who conceived with assisted reproductive technology had an increased risk for PTB ( p < 0.05). Identifying and training mothers at risk as well as improving prenatal care may reduce the PTB rate. We also recommend that statisticians utilize the logistic regression model for the classification of risk groups for PTB.

Prevalence and prognosis of ventricular tachycardia/ventricular fibrillation in patients with post-infarction left ventricular aneurysm: Analysis of 575 cases.

PubMed

Ning, Xiaohui; Ye, Xuerui; Si, Yanhua; Yang, Zihe; Zhao, Yunzi; Sun, Qi; Chen, Ruohan; Tang, Min; Chen, Keping; Zhang, Xiaoli; Zhang, Shu

2018-03-21

We investigated the prevalence of ventricular tachycardia/ventricular fibrillation (VT/VF) in Post-infarction left ventricular aneurysm (PI-LVA) patients and analyze clinical outcomes in patients presenting with VT/VF. 575 PI-LVA patients were enrolled and investigated by logistic regression analysis. Patients with VT/VF were followed up, the composite primary endpoint was cardiac death and appropriate ICD/external shocks. The incidence of sustained VT/VF was 11%. Logistical regression analysis showed male gender, enlarged LV end diastolic diameter (LVEDD) and higher NYHA class were correlated with VT/VF development. During follow up of 46 ± 15 months, 19 out of 62(31%) patients reached study end point. Multivariate Cox regression analysis revealed that enlarged LVEDD and moderate/severe mitral regurgitation (MR) were independently predictive of clinical outcome. Male gender, enlarged LVEDD and higher NYHA class associated with risk of sustained VT/VF in PI-LVA patients. Among VT/VF positive patients, enlarged LVEDD and moderate/severe MR independently predicted poor clinical prognosis. Copyright © 2018. Published by Elsevier Inc.

Associations between physical activity parenting practices and adolescent girls' self-perceptions and physical activity intentions.

PubMed

Sebire, Simon J; Haase, Anne M; Montgomery, Alan A; McNeill, Jade; Jago, Russ

2014-05-01

The current study investigated cross-sectional associations between maternal and paternal logistic and modeling physical activity support and the self-efficacy, self-esteem, and physical activity intentions of 11- to 12-year-old girls. 210 girls reported perceptions of maternal and paternal logistic and modeling support and their self-efficacy, self-esteem and intention to be physically active. Data were analyzed using multivariable regression models. Maternal logistic support was positively associated with participants' self-esteem, physical activity self-efficacy, and intention to be active. Maternal modeling was positively associated with self-efficacy. Paternal modeling was positively associated with self-esteem and self-efficacy but there was no evidence that paternal logistic support was associated with the psychosocial variables. Activity-related parenting practices were associated with psychosocial correlates of physical activity among adolescent girls. Logistic support from mothers, rather than modeling support or paternal support may be a particularly important target when designing interventions aimed at preventing the age-related decline in physical activity among girls.

Design, innovation, and rural creative places: Are the arts the cherry on top, or the secret sauce?

PubMed

Wojan, Timothy R; Nichols, Bonnie

2018-01-01

Creative class theory explains the positive relationship between the arts and commercial innovation as the mutual attraction of artists and other creative workers by an unobserved creative milieu. This study explores alternative theories for rural settings, by analyzing establishment-level survey data combined with data on the local arts scene. The study identifies the local contextual factors associated with a strong design orientation, and estimates the impact that a strong design orientation has on the local economy. Data on innovation and design come from a nationally representative sample of establishments in tradable industries. Latent class analysis allows identifying unobserved subpopulations comprised of establishments with different design and innovation orientations. Logistic regression allows estimating the association between an establishment's design orientation and local contextual factors. A quantile instrumental variable regression allows assessing the robustness of the logistic regression results with respect to endogeneity. An estimate of design orientation at the local level derived from the survey is used to examine variation in economic performance during the period of recovery from the Great Recession (2010-2014). Three distinct innovation (substantive, nominal, and non-innovators) and design orientations (design-integrated, "design last finish," and no systematic approach to design) are identified. Innovation- and design-intensive establishments were identified in both rural and urban areas. Rural design-integrated establishments tended to locate in counties with more highly educated workforces and containing at least one performing arts organization. A quantile instrumental variable regression confirmed that the logistic regression result is robust to endogeneity concerns. Finally, rural areas characterized by design-integrated establishments experienced faster growth in wages relative to rural areas characterized by establishments using no systematic approach to design.

Design, innovation, and rural creative places: Are the arts the cherry on top, or the secret sauce?

PubMed Central

Nichols, Bonnie

2018-01-01

Objective Creative class theory explains the positive relationship between the arts and commercial innovation as the mutual attraction of artists and other creative workers by an unobserved creative milieu. This study explores alternative theories for rural settings, by analyzing establishment-level survey data combined with data on the local arts scene. The study identifies the local contextual factors associated with a strong design orientation, and estimates the impact that a strong design orientation has on the local economy. Method Data on innovation and design come from a nationally representative sample of establishments in tradable industries. Latent class analysis allows identifying unobserved subpopulations comprised of establishments with different design and innovation orientations. Logistic regression allows estimating the association between an establishment’s design orientation and local contextual factors. A quantile instrumental variable regression allows assessing the robustness of the logistic regression results with respect to endogeneity. An estimate of design orientation at the local level derived from the survey is used to examine variation in economic performance during the period of recovery from the Great Recession (2010–2014). Results Three distinct innovation (substantive, nominal, and non-innovators) and design orientations (design-integrated, “design last finish,” and no systematic approach to design) are identified. Innovation- and design-intensive establishments were identified in both rural and urban areas. Rural design-integrated establishments tended to locate in counties with more highly educated workforces and containing at least one performing arts organization. A quantile instrumental variable regression confirmed that the logistic regression result is robust to endogeneity concerns. Finally, rural areas characterized by design-integrated establishments experienced faster growth in wages relative to rural areas characterized by establishments using no systematic approach to design. PMID:29489884

The Impact of Household Heads' Education Levels on the Poverty Risk: The Evidence from Turkey

ERIC Educational Resources Information Center

Bilenkisi, Fikret; Gungor, Mahmut Sami; Tapsin, Gulcin

2015-01-01

This study aims to analyze the relationship between the education levels of household heads and the poverty risk of households in Turkey. The logistic regression models have been estimated with the poverty risk of a household as a dependent variable and a set of educational levels as explanatory variables for all households. There are subgroups of…

The interrelation between intestinal parasites and latent TB infections among newly resettled refugees in Texas.

PubMed

Board, Amy R; Suzuki, Sumihiro

2016-01-01

Previous research has documented that parasite infection may increase vulnerability to TB among certain at risk populations. The purpose of this study was to identify whether an association exists between latent tuberculosis infection (LTBI) and intestinal parasite infection among newly resettled refugees in Texas while controlling for additional effects of region of origin, age and sex. Data for all refugees screened for both TB and intestinal parasites between January 2010 and mid-October 2013 were obtained from the Texas Refugee Health Screening Program and were analyzed using logistic regression. A total of 9860 refugees were included. In multivariable logistic regression analysis, pathogenic and non-pathogenic intestinal parasite infections yielded statistically significant reduced odds of LTBI. However, when individual parasite species were analyzed, hookworm infection indicated statistically significant increased odds of LTBI (OR 1.674, CI: 1.126-2.488). A positive association exists between hookworm infection and LTBI in newly arrived refugees to Texas. More research is needed to assess the nature and extent of these associations. © The Author 2015. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Factors associated with preventable infant death: a multiple logistic regression.

PubMed

Vidal E Silva, Sandra Maria Cunha; Tuon, Rogério Antonio; Probst, Livia Fernandes; Gondinho, Brunna Verna Castro; Pereira, Antonio Carlos; Meneghim, Marcelo de Castro; Cortellazzi, Karine Laura; Ambrosano, Glaucia Maria Bovi

2018-01-01

OBJECTIVE To identify and analyze factors associated with preventable child deaths. METHODS This analytical cross-sectional study had preventable child mortality as dependent variable. From a population of 34,284 live births, we have selected a systematic sample of 4,402 children who did not die compared to 272 children who died from preventable causes during the period studied. The independent variables were analyzed in four hierarchical blocks: sociodemographic factors, the characteristics of the mother, prenatal and delivery care, and health conditions of the patient and neonatal care. We performed a descriptive statistical analysis and estimated multiple hierarchical logistic regression models. RESULTS Approximatelly 35.3% of the deaths could have been prevented with the early diagnosis and treatment of diseases during pregnancy and 26.8% of them could have been prevented with better care conditions for pregnant women. CONCLUSIONS The following characteristics of the mother are determinant for the higher mortality of children before the first year of life: living in neighborhoods with an average family income lower than four minimum wages, being aged ≤ 19 years, having one or more alive children, having a child with low APGAR level at the fifth minute of life, and having a child with low birth weight.

Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets.

PubMed

Gruber, Susan; Logan, Roger W; Jarrín, Inmaculada; Monge, Susana; Hernán, Miguel A

2015-01-15

Inverse probability weights used to fit marginal structural models are typically estimated using logistic regression. However, a data-adaptive procedure may be able to better exploit information available in measured covariates. By combining predictions from multiple algorithms, ensemble learning offers an alternative to logistic regression modeling to further reduce bias in estimated marginal structural model parameters. We describe the application of two ensemble learning approaches to estimating stabilized weights: super learning (SL), an ensemble machine learning approach that relies on V-fold cross validation, and an ensemble learner (EL) that creates a single partition of the data into training and validation sets. Longitudinal data from two multicenter cohort studies in Spain (CoRIS and CoRIS-MD) were analyzed to estimate the mortality hazard ratio for initiation versus no initiation of combined antiretroviral therapy among HIV positive subjects. Both ensemble approaches produced hazard ratio estimates further away from the null, and with tighter confidence intervals, than logistic regression modeling. Computation time for EL was less than half that of SL. We conclude that ensemble learning using a library of diverse candidate algorithms offers an alternative to parametric modeling of inverse probability weights when fitting marginal structural models. With large datasets, EL provides a rich search over the solution space in less time than SL with comparable results. Copyright © 2014 John Wiley & Sons, Ltd.

Impact of low vision on employment.

PubMed

Mojon-Azzi, Stefania M; Sousa-Poza, Alfonso; Mojon, Daniel S

2010-01-01

We investigated the influence of self-reported corrected eyesight on several variables describing the perception by employees and self-employed persons of their employment. Our study was based on data from the Survey of Health, Ageing and Retirement in Europe (SHARE). SHARE is a multidisciplinary, cross-national database of microdata on health, socioeconomic status, social and family networks, collected on 31,115 individuals in 11 European countries and in Israel. With the help of ordered logistic regressions and binary logistic regressions, we analyzed the influence of perceived visual impairment--corrected by 19 covariates capturing socioeconomic and health-related factors--on 10 variables describing the respondents' employment situation. Based on data covering 10,340 working individuals, the results of the logistic and ordered regressions indicate that respondents with lower levels of self-reported general eyesight were significantly less satisfied with their jobs, felt they had less freedom to decide, less opportunity to develop new skills, less support in difficult situations, less recognition for their work, and an inadequate salary. Respondents with a lower eyesight level more frequently reported that they feared their health might limit their ability to work before regular retirement age and more often indicated that they were seeking early retirement. Analysis of this dataset from 12 countries demonstrates the strong impact of self-reported visual impairment on individual employment, and therefore on job satisfaction, productivity, and well-being. Copyright © 2010 S. Karger AG, Basel.

Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets

PubMed Central

Gruber, Susan; Logan, Roger W.; Jarrín, Inmaculada; Monge, Susana; Hernán, Miguel A.

2014-01-01

Inverse probability weights used to fit marginal structural models are typically estimated using logistic regression. However a data-adaptive procedure may be able to better exploit information available in measured covariates. By combining predictions from multiple algorithms, ensemble learning offers an alternative to logistic regression modeling to further reduce bias in estimated marginal structural model parameters. We describe the application of two ensemble learning approaches to estimating stabilized weights: super learning (SL), an ensemble machine learning approach that relies on V -fold cross validation, and an ensemble learner (EL) that creates a single partition of the data into training and validation sets. Longitudinal data from two multicenter cohort studies in Spain (CoRIS and CoRIS-MD) were analyzed to estimate the mortality hazard ratio for initiation versus no initiation of combined antiretroviral therapy among HIV positive subjects. Both ensemble approaches produced hazard ratio estimates further away from the null, and with tighter confidence intervals, than logistic regression modeling. Computation time for EL was less than half that of SL. We conclude that ensemble learning using a library of diverse candidate algorithms offers an alternative to parametric modeling of inverse probability weights when fitting marginal structural models. With large datasets, EL provides a rich search over the solution space in less time than SL with comparable results. PMID:25316152

Logistic regression for dichotomized counts.

PubMed

Preisser, John S; Das, Kalyan; Benecha, Habtamu; Stamm, John W

2016-12-01

Sometimes there is interest in a dichotomized outcome indicating whether a count variable is positive or zero. Under this scenario, the application of ordinary logistic regression may result in efficiency loss, which is quantifiable under an assumed model for the counts. In such situations, a shared-parameter hurdle model is investigated for more efficient estimation of regression parameters relating to overall effects of covariates on the dichotomous outcome, while handling count data with many zeroes. One model part provides a logistic regression containing marginal log odds ratio effects of primary interest, while an ancillary model part describes the mean count of a Poisson or negative binomial process in terms of nuisance regression parameters. Asymptotic efficiency of the logistic model parameter estimators of the two-part models is evaluated with respect to ordinary logistic regression. Simulations are used to assess the properties of the models with respect to power and Type I error, the latter investigated under both misspecified and correctly specified models. The methods are applied to data from a randomized clinical trial of three toothpaste formulations to prevent incident dental caries in a large population of Scottish schoolchildren. © The Author(s) 2014.

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Evaluation of logistic regression models and effect of covariates for case-control study in RNA-Seq analysis.

PubMed

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.

Differentially private distributed logistic regression using private and public data

PubMed Central

2014-01-01

Background Privacy protecting is an important issue in medical informatics and differential privacy is a state-of-the-art framework for data privacy research. Differential privacy offers provable privacy against attackers who have auxiliary information, and can be applied to data mining models (for example, logistic regression). However, differentially private methods sometimes introduce too much noise and make outputs less useful. Given available public data in medical research (e.g. from patients who sign open-consent agreements), we can design algorithms that use both public and private data sets to decrease the amount of noise that is introduced. Methodology In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data. Experiments and results We try our algorithm on three different data sets, and show its advantage over: (1) a logistic regression model based solely on public data, and (2) a differentially private distributed logistic regression model based on private data under various scenarios. Conclusion Logistic regression models built with our new algorithm based on both private and public datasets demonstrate better utility than models that trained on private or public datasets alone without sacrificing the rigorous privacy guarantee. PMID:25079786

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy.

PubMed

Park, Ji Hyun; Kim, Hyeon-Young; Lee, Hanna; Yun, Eun Kyoung

2015-12-01

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy. Copyright © 2015 Elsevier Ltd. All rights reserved.

Performance and strategy comparisons of human listeners and logistic regression in discriminating underwater targets.

PubMed

Yang, Lixue; Chen, Kean

2015-11-01

To improve the design of underwater target recognition systems based on auditory perception, this study compared human listeners with automatic classifiers. Performances measures and strategies in three discrimination experiments, including discriminations between man-made and natural targets, between ships and submarines, and among three types of ships, were used. In the experiments, the subjects were asked to assign a score to each sound based on how confident they were about the category to which it belonged, and logistic regression, which represents linear discriminative models, also completed three similar tasks by utilizing many auditory features. The results indicated that the performances of logistic regression improved as the ratio between inter- and intra-class differences became larger, whereas the performances of the human subjects were limited by their unfamiliarity with the targets. Logistic regression performed better than the human subjects in all tasks but the discrimination between man-made and natural targets, and the strategies employed by excellent human subjects were similar to that of logistic regression. Logistic regression and several human subjects demonstrated similar performances when discriminating man-made and natural targets, but in this case, their strategies were not similar. An appropriate fusion of their strategies led to further improvement in recognition accuracy.

Simulating land-use changes by incorporating spatial autocorrelation and self-organization in CLUE-S modeling: a case study in Zengcheng District, Guangzhou, China

NASA Astrophysics Data System (ADS)

Mei, Zhixiong; Wu, Hao; Li, Shiyun

2018-06-01

The Conversion of Land Use and its Effects at Small regional extent (CLUE-S), which is a widely used model for land-use simulation, utilizes logistic regression to estimate the relationships between land use and its drivers, and thus, predict land-use change probabilities. However, logistic regression disregards possible spatial autocorrelation and self-organization in land-use data. Autologistic regression can depict spatial autocorrelation but cannot address self-organization, while logistic regression by considering only self-organization (NElogistic regression) fails to capture spatial autocorrelation. Therefore, this study developed a regression (NE-autologistic regression) method, which incorporated both spatial autocorrelation and self-organization, to improve CLUE-S. The Zengcheng District of Guangzhou, China was selected as the study area. The land-use data of 2001, 2005, and 2009, as well as 10 typical driving factors, were used to validate the proposed regression method and the improved CLUE-S model. Then, three future land-use scenarios in 2020: the natural growth scenario, ecological protection scenario, and economic development scenario, were simulated using the improved model. Validation results showed that NE-autologistic regression performed better than logistic regression, autologistic regression, and NE-logistic regression in predicting land-use change probabilities. The spatial allocation accuracy and kappa values of NE-autologistic-CLUE-S were higher than those of logistic-CLUE-S, autologistic-CLUE-S, and NE-logistic-CLUE-S for the simulations of two periods, 2001-2009 and 2005-2009, which proved that the improved CLUE-S model achieved the best simulation and was thereby effective to a certain extent. The scenario simulation results indicated that under all three scenarios, traffic land and residential/industrial land would increase, whereas arable land and unused land would decrease during 2009-2020. Apparent differences also existed in the simulated change sizes and locations of each land-use type under different scenarios. The results not only demonstrate the validity of the improved model but also provide a valuable reference for relevant policy-makers.

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

Determining factors influencing survival of breast cancer by fuzzy logistic regression model.

PubMed

Nikbakht, Roya; Bahrampour, Abbas

2017-01-01

Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.

Sperm Retrieval in Patients with Klinefelter Syndrome: A Skewed Regression Model Analysis.

PubMed

Chehrazi, Mohammad; Rahimiforoushani, Abbas; Sabbaghian, Marjan; Nourijelyani, Keramat; Sadighi Gilani, Mohammad Ali; Hoseini, Mostafa; Vesali, Samira; Yaseri, Mehdi; Alizadeh, Ahad; Mohammad, Kazem; Samani, Reza Omani

2017-01-01

The most common chromosomal abnormality due to non-obstructive azoospermia (NOA) is Klinefelter syndrome (KS) which occurs in 1-1.72 out of 500-1000 male infants. The probability of retrieving sperm as the outcome could be asymmetrically different between patients with and without KS, therefore logistic regression analysis is not a well-qualified test for this type of data. This study has been designed to evaluate skewed regression model analysis for data collected from microsurgical testicular sperm extraction (micro-TESE) among azoospermic patients with and without non-mosaic KS syndrome. This cohort study compared the micro-TESE outcome between 134 men with classic KS and 537 men with NOA and normal karyotype who were referred to Royan Institute between 2009 and 2011. In addition to our main outcome, which was sperm retrieval, we also used logistic and skewed regression analyses to compare the following demographic and hormonal factors: age, level of follicle stimulating hormone (FSH), luteinizing hormone (LH), and testosterone between the two groups. A comparison of the micro-TESE between the KS and control groups showed a success rate of 28.4% (38/134) for the KS group and 22.2% (119/537) for the control group. In the KS group, a significantly difference (P<0.001) existed between testosterone levels for the successful sperm retrieval group (3.4 ± 0.48 mg/mL) compared to the unsuccessful sperm retrieval group (2.33 ± 0.23 mg/mL). The index for quasi Akaike information criterion (QAIC) had a goodness of fit of 74 for the skewed model which was lower than logistic regression (QAIC=85). According to the results, skewed regression is more efficient in estimating sperm retrieval success when the data from patients with KS are analyzed. This finding should be investigated by conducting additional studies with different data structures.

Prevalence and Correlates of Physical Spousal Violence against Women in Slum and Nonslum Areas of Urban Bangladesh

ERIC Educational Resources Information Center

Sambisa, William; Angeles, Gustavo; Lance, Peter M.; Naved, Ruchira T.; Thornton, Juliana

2011-01-01

This study explores the prevalence and correlates of past-year physical violence against women in slum and nonslum areas of urban Bangladesh. The authors use multivariate logistic regression to analyze data from the 2006 Urban Health Survey, a population-based survey of 9,122 currently married women aged between 15 and 49 who were selected using a…

Factors Contributing to Successful Employment Outcomes for Hispanic Women Who Are Deaf: Utilization of Chi-Squared Automatic Interaction Detector and Logistic Regression Analysis

ERIC Educational Resources Information Center

Feist, Amber M.

2013-01-01

Hispanic women who are deaf constitute a heterogeneous group of individuals with varying vocational needs. To understand the unique needs of this population, it is important to analyze how consumer characteristics, presence of public supports, and type of services provided influence employment outcomes for Hispanic women who are deaf. The purpose…

Mixed conditional logistic regression for habitat selection studies.

PubMed

Duchesne, Thierry; Fortin, Daniel; Courbin, Nicolas

2010-05-01

1. Resource selection functions (RSFs) are becoming a dominant tool in habitat selection studies. RSF coefficients can be estimated with unconditional (standard) and conditional logistic regressions. While the advantage of mixed-effects models is recognized for standard logistic regression, mixed conditional logistic regression remains largely overlooked in ecological studies. 2. We demonstrate the significance of mixed conditional logistic regression for habitat selection studies. First, we use spatially explicit models to illustrate how mixed-effects RSFs can be useful in the presence of inter-individual heterogeneity in selection and when the assumption of independence from irrelevant alternatives (IIA) is violated. The IIA hypothesis states that the strength of preference for habitat type A over habitat type B does not depend on the other habitat types also available. Secondly, we demonstrate the significance of mixed-effects models to evaluate habitat selection of free-ranging bison Bison bison. 3. When movement rules were homogeneous among individuals and the IIA assumption was respected, fixed-effects RSFs adequately described habitat selection by simulated animals. In situations violating the inter-individual homogeneity and IIA assumptions, however, RSFs were best estimated with mixed-effects regressions, and fixed-effects models could even provide faulty conclusions. 4. Mixed-effects models indicate that bison did not select farmlands, but exhibited strong inter-individual variations in their response to farmlands. Less than half of the bison preferred farmlands over forests. Conversely, the fixed-effect model simply suggested an overall selection for farmlands. 5. Conditional logistic regression is recognized as a powerful approach to evaluate habitat selection when resource availability changes. This regression is increasingly used in ecological studies, but almost exclusively in the context of fixed-effects models. Fitness maximization can imply differences in trade-offs among individuals, which can yield inter-individual differences in selection and lead to departure from IIA. These situations are best modelled with mixed-effects models. Mixed-effects conditional logistic regression should become a valuable tool for ecological research.

A secure distributed logistic regression protocol for the detection of rare adverse drug events

PubMed Central

El Emam, Khaled; Samet, Saeed; Arbuckle, Luk; Tamblyn, Robyn; Earle, Craig; Kantarcioglu, Murat

2013-01-01

Background There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. Objective To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. Methods We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. Results The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. Conclusion The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through generalized estimating equations, and to accommodate other link functions by extending it to generalized linear models. PMID:22871397

SU-E-J-256: Predicting Metastasis-Free Survival of Rectal Cancer Patients Treated with Neoadjuvant Chemo-Radiotherapy by Data-Mining of CT Texture Features of Primary Lesions

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

Zhong, H; Wang, J; Shen, L

Purpose: The purpose of this study is to investigate the relationship between computed tomographic (CT) texture features of primary lesions and metastasis-free survival for rectal cancer patients; and to develop a datamining prediction model using texture features. Methods: A total of 220 rectal cancer patients treated with neoadjuvant chemo-radiotherapy (CRT) were enrolled in this study. All patients underwent CT scans before CRT. The primary lesions on the CT images were delineated by two experienced oncologists. The CT images were filtered by Laplacian of Gaussian (LoG) filters with different filter values (1.0–2.5: from fine to coarse). Both filtered and unfiltered imagesmore » were analyzed using Gray-level Co-occurrence Matrix (GLCM) texture analysis with different directions (transversal, sagittal, and coronal). Totally, 270 texture features with different species, directions and filter values were extracted. Texture features were examined with Student’s t-test for selecting predictive features. Principal Component Analysis (PCA) was performed upon the selected features to reduce the feature collinearity. Artificial neural network (ANN) and logistic regression were applied to establish metastasis prediction models. Results: Forty-six of 220 patients developed metastasis with a follow-up time of more than 2 years. Sixtyseven texture features were significantly different in t-test (p<0.05) between patients with and without metastasis, and 12 of them were extremely significant (p<0.001). The Area-under-the-curve (AUC) of ANN was 0.72, and the concordance index (CI) of logistic regression was 0.71. The predictability of ANN was slightly better than logistic regression. Conclusion: CT texture features of primary lesions are related to metastasisfree survival of rectal cancer patients. Both ANN and logistic regression based models can be developed for prediction.« less

Changes of visual-field global indices after cataract surgery in primary open-angle glaucoma patients.

PubMed

Seol, Bo Ram; Jeoung, Jin Wook; Park, Ki Ho

2016-11-01

To determine changes of visual-field (VF) global indices after cataract surgery and the factors associated with the effect of cataracts on those indices in primary open-angle glaucoma (POAG) patients. A retrospective chart review of 60 POAG patients who had undergone phacoemulsification and intraocular lens insertion was conducted. All of the patients were evaluated with standard automated perimetry (SAP; 30-2 Swedish interactive threshold algorithm; Carl Zeiss Meditec Inc.) before and after surgery. VF global indices before surgery were compared with those after surgery. The best-corrected visual acuity, intraocular pressure (IOP), number of glaucoma medications before surgery, mean total deviation (TD) values, mean pattern deviation (PD) value, and mean TD-PD value were also compared with the corresponding postoperative values. Additionally, postoperative peak IOP and mean IOP were evaluated. Univariate and multivariate logistic regression analyses were performed to identify the factors associated with the effect of cataract on global indices. Mean deviation (MD) after cataract surgery was significantly improved compared with the preoperative MD. Pattern standard deviation (PSD) and visual-field index (VFI) after surgery were similar to those before surgery. Also, mean TD and mean TD-PD were significantly improved after surgery. The posterior subcapsular cataract (PSC) type showed greater MD changes than did the non-PSC type in both the univariate and multivariate logistic regression analyses. In the univariate logistic regression analysis, the preoperative TD-PD value and type of cataract were associated with MD change. However, in the multivariate logistic regression analysis, type of cataract was the only associated factor. None of the other factors was associated with MD change. MD was significantly affected by cataracts, whereas PSD and VFI were not. Most notably, the PSC type showed better MD improvement compared with the non-PSC type after cataract surgery. Clinicians therefore should carefully analyze VF examination results for POAG patients with the PSC type.

A secure distributed logistic regression protocol for the detection of rare adverse drug events.

PubMed

El Emam, Khaled; Samet, Saeed; Arbuckle, Luk; Tamblyn, Robyn; Earle, Craig; Kantarcioglu, Murat

2013-05-01

There is limited capacity to assess the comparative risks of medications after they enter the market. For rare adverse events, the pooling of data from multiple sources is necessary to have the power and sufficient population heterogeneity to detect differences in safety and effectiveness in genetic, ethnic and clinically defined subpopulations. However, combining datasets from different data custodians or jurisdictions to perform an analysis on the pooled data creates significant privacy concerns that would need to be addressed. Existing protocols for addressing these concerns can result in reduced analysis accuracy and can allow sensitive information to leak. To develop a secure distributed multi-party computation protocol for logistic regression that provides strong privacy guarantees. We developed a secure distributed logistic regression protocol using a single analysis center with multiple sites providing data. A theoretical security analysis demonstrates that the protocol is robust to plausible collusion attacks and does not allow the parties to gain new information from the data that are exchanged among them. The computational performance and accuracy of the protocol were evaluated on simulated datasets. The computational performance scales linearly as the dataset sizes increase. The addition of sites results in an exponential growth in computation time. However, for up to five sites, the time is still short and would not affect practical applications. The model parameters are the same as the results on pooled raw data analyzed in SAS, demonstrating high model accuracy. The proposed protocol and prototype system would allow the development of logistic regression models in a secure manner without requiring the sharing of personal health information. This can alleviate one of the key barriers to the establishment of large-scale post-marketing surveillance programs. We extended the secure protocol to account for correlations among patients within sites through generalized estimating equations, and to accommodate other link functions by extending it to generalized linear models.

Detection of high GS risk group prostate tumors by diffusion tensor imaging and logistic regression modelling.

PubMed

Ertas, Gokhan

2018-07-01

To assess the value of joint evaluation of diffusion tensor imaging (DTI) measures by using logistic regression modelling to detect high GS risk group prostate tumors. Fifty tumors imaged using DTI on a 3 T MRI device were analyzed. Regions of interests focusing on the center of tumor foci and noncancerous tissue on the maps of mean diffusivity (MD) and fractional anisotropy (FA) were used to extract the minimum, the maximum and the mean measures. Measure ratio was computed by dividing tumor measure by noncancerous tissue measure. Logistic regression models were fitted for all possible pair combinations of the measures using 5-fold cross validation. Systematic differences are present for all MD measures and also for all FA measures in distinguishing the high risk tumors [GS ≥ 7(4 + 3)] from the low risk tumors [GS ≤ 7(3 + 4)] (P < 0.05). Smaller value for MD measures and larger value for FA measures indicate the high risk. The models enrolling the measures achieve good fits and good classification performances (R 2 adj  = 0.55-0.60, AUC = 0.88-0.91), however the models using the measure ratios perform better (R 2 adj  = 0.59-0.75, AUC = 0.88-0.95). The model that employs the ratios of minimum MD and maximum FA accomplishes the highest sensitivity, specificity and accuracy (Se = 77.8%, Sp = 96.9% and Acc = 90.0%). Joint evaluation of MD and FA diffusion tensor imaging measures is valuable to detect high GS risk group peripheral zone prostate tumors. However, use of the ratios of the measures improves the accuracy of the detections substantially. Logistic regression modelling provides a favorable solution for the joint evaluations easily adoptable in clinical practice. Copyright © 2018 Elsevier Inc. All rights reserved.

Advanced colorectal neoplasia risk stratification by penalized logistic regression.

PubMed

Lin, Yunzhi; Yu, Menggang; Wang, Sijian; Chappell, Richard; Imperiale, Thomas F

2016-08-01

Colorectal cancer is the second leading cause of death from cancer in the United States. To facilitate the efficiency of colorectal cancer screening, there is a need to stratify risk for colorectal cancer among the 90% of US residents who are considered "average risk." In this article, we investigate such risk stratification rules for advanced colorectal neoplasia (colorectal cancer and advanced, precancerous polyps). We use a recently completed large cohort study of subjects who underwent a first screening colonoscopy. Logistic regression models have been used in the literature to estimate the risk of advanced colorectal neoplasia based on quantifiable risk factors. However, logistic regression may be prone to overfitting and instability in variable selection. Since most of the risk factors in our study have several categories, it was tempting to collapse these categories into fewer risk groups. We propose a penalized logistic regression method that automatically and simultaneously selects variables, groups categories, and estimates their coefficients by penalizing the [Formula: see text]-norm of both the coefficients and their differences. Hence, it encourages sparsity in the categories, i.e. grouping of the categories, and sparsity in the variables, i.e. variable selection. We apply the penalized logistic regression method to our data. The important variables are selected, with close categories simultaneously grouped, by penalized regression models with and without the interactions terms. The models are validated with 10-fold cross-validation. The receiver operating characteristic curves of the penalized regression models dominate the receiver operating characteristic curve of naive logistic regressions, indicating a superior discriminative performance. © The Author(s) 2013.

Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity in each basin, particle size sorting, average storm intensity (millimeters per hour), soil organic matter content, soil permeability, and soil drainage. The results of this study demonstrate that logistic regression is a valuable tool for predicting the probability of debris flows occurring in recently-burned landscapes.

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis

PubMed Central

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Background: Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. Methods: In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. Results: The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Conclusion: Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended. PMID:26793655

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

PubMed

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Predictors of course in obsessive-compulsive disorder: logistic regression versus Cox regression for recurrent events.

PubMed

Kempe, P T; van Oppen, P; de Haan, E; Twisk, J W R; Sluis, A; Smit, J H; van Dyck, R; van Balkom, A J L M

2007-09-01

Two methods for predicting remissions in obsessive-compulsive disorder (OCD) treatment are evaluated. Y-BOCS measurements of 88 patients with a primary OCD (DSM-III-R) diagnosis were performed over a 16-week treatment period, and during three follow-ups. Remission at any measurement was defined as a Y-BOCS score lower than thirteen combined with a reduction of seven points when compared with baseline. Logistic regression models were compared with a Cox regression for recurrent events model. Logistic regression yielded different models at different evaluation times. The recurrent events model remained stable when fewer measurements were used. Higher baseline levels of neuroticism and more severe OCD symptoms were associated with a lower chance of remission, early age of onset and more depressive symptoms with a higher chance. Choice of outcome time affects logistic regression prediction models. Recurrent events analysis uses all information on remissions and relapses. Short- and long-term predictors for OCD remission show overlap.

Estimating the exceedance probability of rain rate by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.; Kedem, Benjamin

1990-01-01

Recent studies have shown that the fraction of an area with rain intensity above a fixed threshold is highly correlated with the area-averaged rain rate. To estimate the fractional rainy area, a logistic regression model, which estimates the conditional probability that rain rate over an area exceeds a fixed threshold given the values of related covariates, is developed. The problem of dependency in the data in the estimation procedure is bypassed by the method of partial likelihood. Analyses of simulated scanning multichannel microwave radiometer and observed electrically scanning microwave radiometer data during the Global Atlantic Tropical Experiment period show that the use of logistic regression in pixel classification is superior to multiple regression in predicting whether rain rate at each pixel exceeds a given threshold, even in the presence of noisy data. The potential of the logistic regression technique in satellite rain rate estimation is discussed.

Variable Selection in Logistic Regression.

DTIC Science & Technology

1987-06-01

23 %. AUTIOR(.) S. CONTRACT OR GRANT NUMBE Rf.i %Z. D. Bai, P. R. Krishnaiah and . C. Zhao F49620-85- C-0008 " PERFORMING ORGANIZATION NAME AND AOORESS...d I7 IOK-TK- d 7 -I0 7’ VARIABLE SELECTION IN LOGISTIC REGRESSION Z. D. Bai, P. R. Krishnaiah and L. C. Zhao Center for Multivariate Analysis...University of Pittsburgh Center for Multivariate Analysis University of Pittsburgh Y !I VARIABLE SELECTION IN LOGISTIC REGRESSION Z- 0. Bai, P. R. Krishnaiah

Multinomial Logistic Regression Predicted Probability Map To Visualize The Influence Of Socio-Economic Factors On Breast Cancer Occurrence in Southern Karnataka

NASA Astrophysics Data System (ADS)

Madhu, B.; Ashok, N. C.; Balasubramanian, S.

2014-11-01

Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007-2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that Multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.

Comparison of Logistic Regression and Artificial Neural Network in Low Back Pain Prediction: Second National Health Survey

PubMed Central

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

Background: The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Methods: Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. Results: The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Conclusions: Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant. PMID:23113198

Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey.

PubMed

Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

2012-01-01

The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.

Modelling of binary logistic regression for obesity among secondary students in a rural area of Kedah

NASA Astrophysics Data System (ADS)

Kamaruddin, Ainur Amira; Ali, Zalila; Noor, Norlida Mohd.; Baharum, Adam; Ahmad, Wan Muhamad Amir W.

2014-07-01

Logistic regression analysis examines the influence of various factors on a dichotomous outcome by estimating the probability of the event's occurrence. Logistic regression, also called a logit model, is a statistical procedure used to model dichotomous outcomes. In the logit model the log odds of the dichotomous outcome is modeled as a linear combination of the predictor variables. The log odds ratio in logistic regression provides a description of the probabilistic relationship of the variables and the outcome. In conducting logistic regression, selection procedures are used in selecting important predictor variables, diagnostics are used to check that assumptions are valid which include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers and a test statistic is calculated to determine the aptness of the model. This study used the binary logistic regression model to investigate overweight and obesity among rural secondary school students on the basis of their demographics profile, medical history, diet and lifestyle. The results indicate that overweight and obesity of students are influenced by obesity in family and the interaction between a student's ethnicity and routine meals intake. The odds of a student being overweight and obese are higher for a student having a family history of obesity and for a non-Malay student who frequently takes routine meals as compared to a Malay student.

Comparing Methodologies for Developing an Early Warning System: Classification and Regression Tree Model versus Logistic Regression. REL 2015-077

ERIC Educational Resources Information Center

Koon, Sharon; Petscher, Yaacov

2015-01-01

The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…

Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models

NASA Astrophysics Data System (ADS)

Schlögel, R.; Marchesini, I.; Alvioli, M.; Reichenbach, P.; Rossi, M.; Malet, J.-P.

2018-01-01

We perform landslide susceptibility zonation with slope units using three digital elevation models (DEMs) of varying spatial resolution of the Ubaye Valley (South French Alps). In so doing, we applied a recently developed algorithm automating slope unit delineation, given a number of parameters, in order to optimize simultaneously the partitioning of the terrain and the performance of a logistic regression susceptibility model. The method allowed us to obtain optimal slope units for each available DEM spatial resolution. For each resolution, we studied the susceptibility model performance by analyzing in detail the relevance of the conditioning variables. The analysis is based on landslide morphology data, considering either the whole landslide or only the source area outline as inputs. The procedure allowed us to select the most useful information, in terms of DEM spatial resolution, thematic variables and landslide inventory, in order to obtain the most reliable slope unit-based landslide susceptibility assessment.

A population study of the contribution of medical comorbidity to the risk of prematurity in blacks.

PubMed

Ehrenthal, Deborah B; Jurkovitz, Claudine; Hoffman, Matthew; Kroelinger, Charlan; Weintraub, William

2007-10-01

The purpose of this study was to test the hypothesis that the higher prevalence of medical comorbidities among black women accounts for their increased risk of prematurity. A population-based regional cohort of women receiving obstetric care for singleton pregnancies at a large community hospital between 2003 and 2006 were analyzed using univariate and multivariable logistic regression. Data for 18,624 consecutive births found increased odds of adverse outcomes for black compared to white women: prematurity OR = 1.6 (1.4-1.8), extreme prematurity OR = 2.5 (2.0-3.2). Logistic regression modeling identified black race, age < 20, preconception diabetes and hypertension, smoking, underweight, and gestational hypertension as the greatest risks for adverse outcomes. Controlling for these risks did not attenuate the higher risk for prematurity among blacks. Though there is a greater burden of health risk among black women, this did not account for the higher rates of low birthweight and prematurity.

Gluten-free is not enough--perception and suggestions of celiac consumers.

PubMed

do Nascimento, Amanda Bagolin; Fiates, Giovanna Medeiros Rataichesck; dos Anjos, Adilson; Teixeira, Evanilda

2014-06-01

The present study investigated the perceptions of individuals with celiac disease about gluten-free (GF) products, their consumer behavior and which product is the most desired. A survey was used to collect information. Descriptive analysis, χ² tests and Multiple Logistic Regressions were conducted. Ninety-one questionnaires were analyzed. Limited variety and availability, the high price of products and the social restrictions imposed by the diet were the factors that caused the most dissatisfaction and difficulty. A total of 71% of the participants confirmed having moderate to high difficulty finding GF products. The logistic regression identified a significant relationship between dissatisfaction, texture and variety (p < 0.05) and between variety and difficulty of finding GF products (p < 0.05). The sensory characteristics were the most important variables considered for actual purchases. Bread was the most desired product. The participants were dissatisfaction with GF products. The desire for bread with better sensory characteristics reinforces the challenge to develop higher quality baking products.

The severity of Minamata disease declined in 25 years: temporal profile of the neurological findings analyzed by multiple logistic regression model.

PubMed

Uchino, Makoto; Hirano, Teruyuki; Satoh, Hiroshi; Arimura, Kimiyoshi; Nakagawa, Masanori; Wakamiya, Jyunji

2005-01-01

Minamata disease (MD) was caused by ingestion of seafood from the methylmercury-contaminated areas. Although 50 years have passed since the discovery of MD, there have been only a few studies on the temporal profile of neurological findings in certified MD patients. Thus, we evaluated changes in neurological symptoms and signs of MD using discriminants by multiple logistic regression analysis. The severity of predictive index declined in 25 years in most of the patients. Only a few patients showed aggravation of neurological findings, which was due to complications such as spino-cerebellar degeneration. Patients with chronic MD aged over 45 years had several concomitant diseases so that their clinical pictures were complicated. It was difficult to differentiate chronic MD using statistically established discriminants based on sensory disturbance alone. In conclusion, the severity of MD declined in 25 years along with the modification by age-related concomitant disorders.

Methods for estimating drought streamflow probabilities for Virginia streams

USGS Publications Warehouse

Austin, Samuel H.

2014-01-01

Maximum likelihood logistic regression model equations used to estimate drought flow probabilities for Virginia streams are presented for 259 hydrologic basins in Virginia. Winter streamflows were used to estimate the likelihood of streamflows during the subsequent drought-prone summer months. The maximum likelihood logistic regression models identify probable streamflows from 5 to 8 months in advance. More than 5 million streamflow daily values collected over the period of record (January 1, 1900 through May 16, 2012) were compiled and analyzed over a minimum 10-year (maximum 112-year) period of record. The analysis yielded the 46,704 equations with statistically significant fit statistics and parameter ranges published in two tables in this report. These model equations produce summer month (July, August, and September) drought flow threshold probabilities as a function of streamflows during the previous winter months (November, December, January, and February). Example calculations are provided, demonstrating how to use the equations to estimate probable streamflows as much as 8 months in advance.

Factors affecting choice between ureterostomy, ileal conduit and continent reservoir after radical cystectomy: Japanese series.

PubMed

Sugihara, Toru; Yasunaga, Hideo; Horiguchi, Hiromasa; Fujimura, Tetsuya; Fushimi, Kiyohide; Yu, Changhong; Kattan, Michael W; Homma, Yukio

2014-12-01

Little is known about the disparity of choices between three urinary diversions after radical cystectomy, focusing on patient and institutional factors. We identified urothelial carcinoma patients who received radical cystectomy with cutaneous ureterostomy, ileal conduit or continent reservoir using the Japanese Diagnosis Procedure Combination database from 2007 to 2012. Data comprised age, sex, comorbidities (converted into the Charlson index), TNM classification (converted into oncological stage), hospitals' academic status, hospital volume, bed volume and geographical region. Multivariate ordinal logistic regression analyses fitted with the proportional odds model were performed to analyze factors affecting urinary diversion choices. For dependent variables, the three diversions were converted into an ordinal variable in order of complexity: cutaneous ureterostomy (reference), ileal conduit and continent reservoir. Geographical variations were also examined by multivariate logistic regression models. A total of 4790 patients (1131 cutaneous ureterostomies [23.6 %], 2970 ileal conduits [62.0 %] and 689 continent reservoirs [14.4 %]) were included. Ordinal logistic regression analyses showed that male sex, lower age, lower Charlson index, early tumor stage, higher hospital volume (≥3.4 cases/year) and larger bed volume (≥450 beds) were significantly associated with the preference of more complex urinary diversion. Significant geographical disparity was also found. Good patient condition and early oncological status, as well as institutional factors, including high hospital volume, large bed volume and specific geographical regions, are independently related to the likelihood of choosing complex diversions. Recognizing this disparity would help reinforce the need for clinical practice uniformity.

Cross-national differences in the gender gap in subjective health in Europe: does country-level gender equality matter?

PubMed

Dahlin, Johanna; Härkönen, Juho

2013-12-01

Multiple studies have found that women report being in worse health despite living longer. Gender gaps vary cross-nationally, but relatively little is known about the causes of comparative differences. Existing literature is inconclusive as to whether gender gaps in health are smaller in more gender equal societies. We analyze gender gaps in self-rated health (SRH) and limiting longstanding illness (LLI) with five waves of European Social Survey data for 191,104 respondents from 28 countries. We use means, odds ratios, logistic regressions, and multilevel random slopes logistic regressions. Gender gaps in subjective health vary visibly across Europe. In many countries (especially in Eastern and Southern Europe), women report distinctly worse health, while in others (such as Estonia, Finland, and Great Britain) there are small or no differences. Logistic regressions ran separately for each country revealed that individual-level socioeconomic and demographic variables explain a majority of these gaps in some countries, but contribute little to their understanding in most countries. In yet other countries, men had worse health when these variables were controlled for. Cross-national variation in the gender gaps exists after accounting for individual-level factors. Against expectations, the remaining gaps are not systematically related to societal-level gender inequality in the multilevel analyses. Our findings stress persistent cross-national variability in gender gaps in health and call for further analysis. Copyright © 2013 Elsevier Ltd. All rights reserved.

Using Multiple and Logistic Regression to Estimate the Median WillCost and Probability of Cost and Schedule Overrun for Program Managers

DTIC Science & Technology

2017-03-23

PUBLIC RELEASE; DISTRIBUTION UNLIMITED Using Multiple and Logistic Regression to Estimate the Median Will- Cost and Probability of Cost and... Cost and Probability of Cost and Schedule Overrun for Program Managers Ryan C. Trudelle Follow this and additional works at: https://scholar.afit.edu...afit.edu. Recommended Citation Trudelle, Ryan C., "Using Multiple and Logistic Regression to Estimate the Median Will- Cost and Probability of Cost and

Expression of Proteins Involved in Epithelial-Mesenchymal Transition as Predictors of Metastasis and Survival in Breast Cancer Patients

DTIC Science & Technology

2013-11-01

Ptrend 0.78 0.62 0.75 Unconditional logistic regression was used to estimate odds ratios (OR) and 95 % confidence intervals (CI) for risk of node...Ptrend 0.71 0.67 Unconditional logistic regression was used to estimate odds ratios (OR) and 95 % confidence intervals (CI) for risk of high-grade tumors... logistic regression was used to estimate odds ratios (OR) and 95 % confidence intervals (CI) for the associations between each of the seven SNPs and

Logistic LASSO regression for the diagnosis of breast cancer using clinical demographic data and the BI-RADS lexicon for ultrasonography.

PubMed

Kim, Sun Mi; Kim, Yongdai; Jeong, Kuhwan; Jeong, Heeyeong; Kim, Jiyoung

2018-01-01

The aim of this study was to compare the performance of image analysis for predicting breast cancer using two distinct regression models and to evaluate the usefulness of incorporating clinical and demographic data (CDD) into the image analysis in order to improve the diagnosis of breast cancer. This study included 139 solid masses from 139 patients who underwent a ultrasonography-guided core biopsy and had available CDD between June 2009 and April 2010. Three breast radiologists retrospectively reviewed 139 breast masses and described each lesion using the Breast Imaging Reporting and Data System (BI-RADS) lexicon. We applied and compared two regression methods-stepwise logistic (SL) regression and logistic least absolute shrinkage and selection operator (LASSO) regression-in which the BI-RADS descriptors and CDD were used as covariates. We investigated the performances of these regression methods and the agreement of radiologists in terms of test misclassification error and the area under the curve (AUC) of the tests. Logistic LASSO regression was superior (P<0.05) to SL regression, regardless of whether CDD was included in the covariates, in terms of test misclassification errors (0.234 vs. 0.253, without CDD; 0.196 vs. 0.258, with CDD) and AUC (0.785 vs. 0.759, without CDD; 0.873 vs. 0.735, with CDD). However, it was inferior (P<0.05) to the agreement of three radiologists in terms of test misclassification errors (0.234 vs. 0.168, without CDD; 0.196 vs. 0.088, with CDD) and the AUC without CDD (0.785 vs. 0.844, P<0.001), but was comparable to the AUC with CDD (0.873 vs. 0.880, P=0.141). Logistic LASSO regression based on BI-RADS descriptors and CDD showed better performance than SL in predicting the presence of breast cancer. The use of CDD as a supplement to the BI-RADS descriptors significantly improved the prediction of breast cancer using logistic LASSO regression.

What is the predictor of surgical mortality in adult colorectal perforation? The clinical characteristics and results of a multivariate logistic regression analysis.

PubMed

Hsu, Chao-Wen; Wang, Jui-Ho; Kung, Ya-Hsin; Chang, Min-Chi

2017-06-01

Colorectal perforations are a serious condition associated with a high mortality. The aim of this study was to describe the clinical characteristics and identify predictors for the surgical mortality in adult patients with colorectal perforation, thereby achieving better outcomes. A retrospective study of adult patients diagnosed with colorectal perforation operated was performed. The clinical variables that might influence the surgical mortality were first analyzed, and the significant variables were then analyzed using a logistic regression model. A total of 423 patients were identified, and the surgical mortality rate was 36.9 %. The most common etiology was diverticulitis (38.2 %). The highest etiology-specific mortality was for colorectal cancer (61.5 %) and ischemic proctocolitis (59.8 %). In a logistic analysis, the significant predictors for the surgical mortality were ≥3 comorbidities (p = 0.034), preoperation American Society of Anesthesiologists score ≥4 (p = 0.025), preoperative sepsis or septic shock (p < 0.001), colorectal cancer or ischemic proctocolitis (p = 0.035), reoperation (p = 0.041), and Hinchey classification grade IV (p = 0.024). We demonstrated that ≥3 comorbidities, a preoperation American Society of Anesthesiologists score ≥4, preoperative sepsis or septic shock, colorectal cancer or ischemic proctocolitis, reoperation, and Hinchey classification grade IV are predictors for the surgical mortality in the adult cases of colorectal perforation. These predictors should be taken into consideration to prevent surgical mortality and to reduce potentially unnecessary medical expenses.

The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams.

PubMed

Yu, Yuanyuan; Li, Hongkai; Sun, Xiaoru; Su, Ping; Wang, Tingting; Liu, Yi; Yuan, Zhongshang; Liu, Yanxun; Xue, Fuzhong

2017-12-28

Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The "do-calculus" was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the parent nodes of outcome, which obtained the highest precision. All adjustment strategies through logistic regression were biased for causal effect estimation, while IPW-based-MSM could always obtain unbiased estimation when the adjusted set satisfied G-admissibility. Thus, IPW-based-MSM was recommended to adjust for confounders set.

Use and interpretation of logistic regression in habitat-selection studies

USGS Publications Warehouse

Keating, Kim A.; Cherry, Steve

2004-01-01

 Logistic regression is an important tool for wildlife habitat-selection studies, but the method frequently has been misapplied due to an inadequate understanding of the logistic model, its interpretation, and the influence of sampling design. To promote better use of this method, we review its application and interpretation under 3 sampling designs: random, case-control, and use-availability. Logistic regression is appropriate for habitat use-nonuse studies employing random sampling and can be used to directly model the conditional probability of use in such cases. Logistic regression also is appropriate for studies employing case-control sampling designs, but careful attention is required to interpret results correctly. Unless bias can be estimated or probability of use is small for all habitats, results of case-control studies should be interpreted as odds ratios, rather than probability of use or relative probability of use. When data are gathered under a use-availability design, logistic regression can be used to estimate approximate odds ratios if probability of use is small, at least on average. More generally, however, logistic regression is inappropriate for modeling habitat selection in use-availability studies. In particular, using logistic regression to fit the exponential model of Manly et al. (2002:100) does not guarantee maximum-likelihood estimates, valid probabilities, or valid likelihoods. We show that the resource selection function (RSF) commonly used for the exponential model is proportional to a logistic discriminant function. Thus, it may be used to rank habitats with respect to probability of use and to identify important habitat characteristics or their surrogates, but it is not guaranteed to be proportional to probability of use. Other problems associated with the exponential model also are discussed. We describe an alternative model based on Lancaster and Imbens (1996) that offers a method for estimating conditional probability of use in use-availability studies. Although promising, this model fails to converge to a unique solution in some important situations. Further work is needed to obtain a robust method that is broadly applicable to use-availability studies.

How Is Health Related to Literacy, Numeracy, and Technological Problem-Solving Skills among U.S. Adults? Evidence from the Program for the International Assessment of Adult Competencies (PIAAC)

ERIC Educational Resources Information Center

Prins, Esther; Monnat, Shannon; Clymer, Carol; Toso, Blaire Wilson

2015-01-01

This paper uses data from the Program for the International Assessment of Adult Competencies (PIAAC) to analyze the relationship between U.S. adults' self-reported health and proficiencies in literacy, numeracy, and technological problem solving. Ordinal logistic regression analyses showed that scores on all three scales were positively and…

Web 2.0 Articles: Content Analysis and a Statistical Model to Predict Recognition of the Need for New Instructional Design Strategies

ERIC Educational Resources Information Center

Liu, Leping; Maddux, Cleborne D.

2008-01-01

This article presents a study of Web 2.0 articles intended to (a) analyze the content of what is written and (b) develop a statistical model to predict whether authors' write about the need for new instructional design strategies and models. Eighty-eight technology articles were subjected to lexical analysis and a logistic regression model was…

Soldier Quality of Life Assessment

DTIC Science & Technology

2016-09-01

ABSTRACT This report documents survey research and modeling of Soldier quality of life (QoL) on contingency base camps by the U.S. Army Natick...Science and Technology Objective Demonstration, was to develop a way to quantify QoL for camps housing fewer than 1000 personnel. A discrete choice survey ... Survey results were analyzed using hierarchical Bayesian logistic regression to develop a quantitative model for estimating QoL based on base camp

Differential Item Functioning Analysis of the "Preschool Language Scale-4" between English-Speaking Hispanic and European American Children from Low-Income Families

ERIC Educational Resources Information Center

Qi, Cathy Huaqing; Marley, Scott C.

2009-01-01

The study examined whether item bias is present in the "Preschool Language Scale-4" (PLS-4). Participants were 440 children (3-5 years old; 86% English-speaking Hispanic and 14% European American) who were enrolled in Head Start programs. The PLS-4 items were analyzed for differential item functioning (DIF) using logistic regression and…

Analysis of reliability of professor recommendation letters based on concordance with self-introduction letter.

PubMed

Kim, Sang Hyun

2013-12-01

The purpose of this study was to examine the concordance between a checklist's categories of professor recommendation letters and characteristics of the self-introduction letter. Checklists of professor recommendation letters were analyzed and classified into cognitive, social, and affective domains. Simple correlation was performed to determine whether the characteristics of the checklists were concordant with those of the self-introduction letter. The difference in ratings of the checklists by pass or fail grades was analyzed by independent sample t-test. Logistic regression analysis was performed to determine whether a pass or fail grade was influenced by ratings on the checklists. The Cronbach alpha value of the checklists was 0.854. Initiative, as an affective domain, in the professor's recommendation letter was highly ranked among the six checklist categories. Self-directed learning in the self-introduction letter was influenced by a pass or fail grade by logistic regression analysis (p<0.05). Successful applicants received higher ratings than those who failed in every checklist category, particularly in problem-solving ability, communication skills, initiative, and morality (p<0.05). There was a strong correlation between cognitive and affective characteristics in the professor recommendation letters and the sum of all characteristics in the self-introduction letter.

Factors associated with preventable infant death: a multiple logistic regression

PubMed Central

Vidal e Silva, Sandra Maria Cunha; Tuon, Rogério Antonio; Probst, Livia Fernandes; Gondinho, Brunna Verna Castro; Pereira, Antonio Carlos; Meneghim, Marcelo de Castro; Cortellazzi, Karine Laura; Ambrosano, Glaucia Maria Bovi

2018-01-01

ABSTRACT OBJECTIVE To identify and analyze factors associated with preventable child deaths. METHODS This analytical cross-sectional study had preventable child mortality as dependent variable. From a population of 34,284 live births, we have selected a systematic sample of 4,402 children who did not die compared to 272 children who died from preventable causes during the period studied. The independent variables were analyzed in four hierarchical blocks: sociodemographic factors, the characteristics of the mother, prenatal and delivery care, and health conditions of the patient and neonatal care. We performed a descriptive statistical analysis and estimated multiple hierarchical logistic regression models. RESULTS Approximatelly 35.3% of the deaths could have been prevented with the early diagnosis and treatment of diseases during pregnancy and 26.8% of them could have been prevented with better care conditions for pregnant women. CONCLUSIONS The following characteristics of the mother are determinant for the higher mortality of children before the first year of life: living in neighborhoods with an average family income lower than four minimum wages, being aged ≤ 19 years, having one or more alive children, having a child with low APGAR level at the fifth minute of life, and having a child with low birth weight. PMID:29723389

Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data

PubMed Central

Sharafi, Zahra

2017-01-01

Background The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. Methods The ordinal logistic regression (OLR) and hierarchical ordinal logistic regression (HOLR) were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter) with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™) 4.0 collected from 576 healthy school children were analyzed. Results Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. Conclusions The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed. PMID:29312463

Assessment of Differential Item Functioning in Health-Related Outcomes: A Simulation and Empirical Analysis with Hierarchical Polytomous Data.

PubMed

Sharafi, Zahra; Mousavi, Amin; Ayatollahi, Seyyed Mohammad Taghi; Jafari, Peyman

2017-01-01

The purpose of this study was to evaluate the effectiveness of two methods of detecting differential item functioning (DIF) in the presence of multilevel data and polytomously scored items. The assessment of DIF with multilevel data (e.g., patients nested within hospitals, hospitals nested within districts) from large-scale assessment programs has received considerable attention but very few studies evaluated the effect of hierarchical structure of data on DIF detection for polytomously scored items. The ordinal logistic regression (OLR) and hierarchical ordinal logistic regression (HOLR) were utilized to assess DIF in simulated and real multilevel polytomous data. Six factors (DIF magnitude, grouping variable, intraclass correlation coefficient, number of clusters, number of participants per cluster, and item discrimination parameter) with a fully crossed design were considered in the simulation study. Furthermore, data of Pediatric Quality of Life Inventory™ (PedsQL™) 4.0 collected from 576 healthy school children were analyzed. Overall, results indicate that both methods performed equivalently in terms of controlling Type I error and detection power rates. The current study showed negligible difference between OLR and HOLR in detecting DIF with polytomously scored items in a hierarchical structure. Implications and considerations while analyzing real data were also discussed.

How Do Clinicians Prefer Cultural Competence Training? Findings from the DSM-5 Cultural Formulation Interview Field Trial

PubMed Central

Aggarwal, Neil Krishan; Lam, Peter; Castillo, Enrico; Weiss, Mitchell G.; Diaz, Esperanza; Alarcón, Renato D.; van Dijk, Rob; Rohlof, Hans; Ndetei, David M.; Scalco, Monica; Aguilar-Gaxiola, Sergio; Bassiri, Kavoos; Deshpande, Smita; Groen, Simon; Jadhav, Sushrut; Kirmayer, Laurence J.; Paralikar, Vasudeo; Westermeyer, Joseph; Santos, Filipa; Vega-Dienstmaier, Johann; Anez, Luis; Boiler, Marit; Nicasio, Andel V.; Lewis-Fernández, Roberto

2015-01-01

Objective This study’s objective is to analyze training methods clinicians reported as most and least helpful during the DSM-5 Cultural Formulation Interview field trial, reasons why, and associations between demographic characteristics and method preferences. Method The authors used mixed methods to analyze interviews from 75 clinicians in five continents on their training preferences after a standardized training session and clinicians’ first administration of the Cultural Formulation Interview. Content analysis identified most and least helpful educational methods by reason. Bivariate and logistic regression analysis compared clinician characteristics to method preferences. Results Most frequently, clinicians named case-based behavioral simulations as “most helpful” and video as “least helpful” training methods. Bivariate and logistic regression models, first unadjusted and then clustered by country, found that each additional year of a clinician’s age was associated with a preference for behavioral simulations: OR=1.05 (95% CI: 1.01–1.10; p=0.025). Conclusions Most clinicians preferred active behavioral simulations in cultural competence training, and this effect was most pronounced among older clinicians. Effective training may be best accomplished through a combination of reviewing written guidelines, video demonstration, and behavioral simulations. Future work can examine the impact of clinician training satisfaction on patient symptoms and quality of life. PMID:26449983

Coffee agroforestry for sustainability of Upper Sekampung Watershed management

NASA Astrophysics Data System (ADS)

Fitriani; Arifin, Bustanul; Zakaria, Wan Abbas; Hanung Ismono, R.

2018-03-01

The main objective of watershed management is to ensure the optimal hydrological and natural resource use for ecological, social and economic importance. One important adaptive management step in dealing with the risk of damage to forest ecosystems is the practice of agroforestry coffee. This study aimed to (1) assess the farmer's response to ecological service responsibility and (2) analyze the Sekampung watersheds management by providing environmental services. The research location was Air Naningan sub-district, Tanggamus, Lampung Province, Indonesia. The research was conducted from July until November 2016. Stratification random sampling based on the pattern of ownership of land rights is used to determine the respondents. Data were analyzed using descriptive statistics and logistic regression analysis. Based on the analysis, it was concluded that coffee farmers' participation in the practice of coffee agroforestry in the form of 38% shade plants and multiple cropping (62%). The logistic regression analysis indicated that the variables of experience and status of land ownership, and incentive-size plans were able to explain variations in the willingness of coffee growers to follow the scheme of providing environmental services. The existence of farmer with partnership and CBFM scheme on different land tenure on upper Sekampung has a strategic position to minimize the deforestation and recovery watersheds destruction.

Modeling Governance KB with CATPCA to Overcome Multicollinearity in the Logistic Regression

NASA Astrophysics Data System (ADS)

Khikmah, L.; Wijayanto, H.; Syafitri, U. D.

2017-04-01

The problem often encounters in logistic regression modeling are multicollinearity problems. Data that have multicollinearity between explanatory variables with the result in the estimation of parameters to be bias. Besides, the multicollinearity will result in error in the classification. In general, to overcome multicollinearity in regression used stepwise regression. They are also another method to overcome multicollinearity which involves all variable for prediction. That is Principal Component Analysis (PCA). However, classical PCA in only for numeric data. Its data are categorical, one method to solve the problems is Categorical Principal Component Analysis (CATPCA). Data were used in this research were a part of data Demographic and Population Survey Indonesia (IDHS) 2012. This research focuses on the characteristic of women of using the contraceptive methods. Classification results evaluated using Area Under Curve (AUC) values. The higher the AUC value, the better. Based on AUC values, the classification of the contraceptive method using stepwise method (58.66%) is better than the logistic regression model (57.39%) and CATPCA (57.39%). Evaluation of the results of logistic regression using sensitivity, shows the opposite where CATPCA method (99.79%) is better than logistic regression method (92.43%) and stepwise (92.05%). Therefore in this study focuses on major class classification (using a contraceptive method), then the selected model is CATPCA because it can raise the level of the major class model accuracy.

Logistic regression models of factors influencing the location of bioenergy and biofuels plants

Treesearch

T.M. Young; R.L. Zaretzki; J.H. Perdue; F.M. Guess; X. Liu

2011-01-01

Logistic regression models were developed to identify significant factors that influence the location of existing wood-using bioenergy/biofuels plants and traditional wood-using facilities. Logistic models provided quantitative insight for variables influencing the location of woody biomass-using facilities. Availability of "thinnings to a basal area of 31.7m2/ha...

Discrete post-processing of total cloud cover ensemble forecasts

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Haiden, Thomas; Pappenberger, Florian

2017-04-01

This contribution presents an approach to post-process ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical post-processing of ensemble predictions are tested. The first approach is based on multinomial logistic regression, the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on station-wise post-processing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model. Reference Hemri, S., Haiden, T., & Pappenberger, F. (2016). Discrete post-processing of total cloud cover ensemble forecasts. Monthly Weather Review 144, 2565-2577.

Fuzzy multinomial logistic regression analysis: A multi-objective programming approach

NASA Astrophysics Data System (ADS)

Abdalla, Hesham A.; El-Sayed, Amany A.; Hamed, Ramadan

2017-05-01

Parameter estimation for multinomial logistic regression is usually based on maximizing the likelihood function. For large well-balanced datasets, Maximum Likelihood (ML) estimation is a satisfactory approach. Unfortunately, ML can fail completely or at least produce poor results in terms of estimated probabilities and confidence intervals of parameters, specially for small datasets. In this study, a new approach based on fuzzy concepts is proposed to estimate parameters of the multinomial logistic regression. The study assumes that the parameters of multinomial logistic regression are fuzzy. Based on the extension principle stated by Zadeh and Bárdossy's proposition, a multi-objective programming approach is suggested to estimate these fuzzy parameters. A simulation study is used to evaluate the performance of the new approach versus Maximum likelihood (ML) approach. Results show that the new proposed model outperforms ML in cases of small datasets.

A Primer on Logistic Regression.

ERIC Educational Resources Information Center

Woldbeck, Tanya

This paper introduces logistic regression as a viable alternative when the researcher is faced with variables that are not continuous. If one is to use simple regression, the dependent variable must be measured on a continuous scale. In the behavioral sciences, it may not always be appropriate or possible to have a measured dependent variable on a…

Weight Fluctuation and Postmenopausal Breast Cancer in the National Health and Nutrition Examination Survey I Epidemiologic Follow-Up Study.

PubMed

Komaroff, Marina

2016-01-01

The aim of this study is to investigate if weight fluctuation is an independent risk factor for postmenopausal breast cancer (PBC) among women who gained weight in adult years. NHANES I Epidemiologic Follow-Up Study (NHEFS) database was used in the study. Women that were cancers-free at enrollment and diagnosed for the first time with breast cancer at age 50 or greater were considered cases. Controls were chosen from the subset of cancers-free women and matched to cases by years of follow-up and status of body mass index (BMI) at 25 years of age. Weight fluctuation was measured by the root-mean-square-error (RMSE) from a simple linear regression model for each woman with their body mass index (BMI) regressed on age (started at 25 years) while women with the positive slope from this regression were defined as weight gainers. Data were analyzed using conditional logistic regression models. A total of 158 women were included into the study. The conditional logistic regression adjusted for weight gain demonstrated positive association between weight fluctuation in adult years and postmenopausal breast cancers (odds ratio/OR = 1.67; 95% confidence interval/CI: 1.06-2.66). The data suggested that long-term weight fluctuation was significant risk factor for PBC among women who gained weight in adult years. This finding underscores the importance of maintaining lost weight and avoiding weight fluctuation.

Weight Fluctuation and Postmenopausal Breast Cancer in the National Health and Nutrition Examination Survey I Epidemiologic Follow-Up Study

PubMed Central

Komaroff, Marina

2016-01-01

Objective. The aim of this study is to investigate if weight fluctuation is an independent risk factor for postmenopausal breast cancer (PBC) among women who gained weight in adult years. Methods. NHANES I Epidemiologic Follow-Up Study (NHEFS) database was used in the study. Women that were cancers-free at enrollment and diagnosed for the first time with breast cancer at age 50 or greater were considered cases. Controls were chosen from the subset of cancers-free women and matched to cases by years of follow-up and status of body mass index (BMI) at 25 years of age. Weight fluctuation was measured by the root-mean-square-error (RMSE) from a simple linear regression model for each woman with their body mass index (BMI) regressed on age (started at 25 years) while women with the positive slope from this regression were defined as weight gainers. Data were analyzed using conditional logistic regression models. Results. A total of 158 women were included into the study. The conditional logistic regression adjusted for weight gain demonstrated positive association between weight fluctuation in adult years and postmenopausal breast cancers (odds ratio/OR = 1.67; 95% confidence interval/CI: 1.06–2.66). Conclusions. The data suggested that long-term weight fluctuation was significant risk factor for PBC among women who gained weight in adult years. This finding underscores the importance of maintaining lost weight and avoiding weight fluctuation. PMID:26953120

A Solution to Separation and Multicollinearity in Multiple Logistic Regression

PubMed Central

Shen, Jianzhao; Gao, Sujuan

2010-01-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27–38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth’s penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study. PMID:20376286

A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

PubMed

Shen, Jianzhao; Gao, Sujuan

2008-10-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

A deeper look at two concepts of measuring gene-gene interactions: logistic regression and interaction information revisited.

PubMed

Mielniczuk, Jan; Teisseyre, Paweł

2018-03-01

Detection of gene-gene interactions is one of the most important challenges in genome-wide case-control studies. Besides traditional logistic regression analysis, recently the entropy-based methods attracted a significant attention. Among entropy-based methods, interaction information is one of the most promising measures having many desirable properties. Although both logistic regression and interaction information have been used in several genome-wide association studies, the relationship between them has not been thoroughly investigated theoretically. The present paper attempts to fill this gap. We show that although certain connections between the two methods exist, in general they refer two different concepts of dependence and looking for interactions in those two senses leads to different approaches to interaction detection. We introduce ordering between interaction measures and specify conditions for independent and dependent genes under which interaction information is more discriminative measure than logistic regression. Moreover, we show that for so-called perfect distributions those measures are equivalent. The numerical experiments illustrate the theoretical findings indicating that interaction information and its modified version are more universal tools for detecting various types of interaction than logistic regression and linkage disequilibrium measures. © 2017 WILEY PERIODICALS, INC.

Predicting No-Shows in Radiology Using Regression Modeling of Data Available in the Electronic Medical Record.

PubMed

Harvey, H Benjamin; Liu, Catherine; Ai, Jing; Jaworsky, Cristina; Guerrier, Claude Emmanuel; Flores, Efren; Pianykh, Oleg

2017-10-01

To test whether data elements available in the electronic medical record (EMR) can be effectively leveraged to predict failure to attend a scheduled radiology examination. Using data from a large academic medical center, we identified all patients with a diagnostic imaging examination scheduled from January 1, 2016, to April 1, 2016, and determined whether the patient successfully attended the examination. Demographic, clinical, and health services utilization variables available in the EMR potentially relevant to examination attendance were recorded for each patient. We used descriptive statistics and logistic regression models to test whether these data elements could predict failure to attend a scheduled radiology examination. The predictive accuracy of the regression models were determined by calculating the area under the receiver operator curve. Among the 54,652 patient appointments with radiology examinations scheduled during the study period, 6.5% were no-shows. No-show rates were highest for the modalities of mammography and CT and lowest for PET and MRI. Logistic regression indicated that 16 of the 27 demographic, clinical, and health services utilization factors were significantly associated with failure to attend a scheduled radiology examination (P ≤ .05). Stepwise logistic regression analysis demonstrated that previous no-shows, days between scheduling and appointments, modality type, and insurance type were most strongly predictive of no-show. A model considering all 16 data elements had good ability to predict radiology no-shows (area under the receiver operator curve = 0.753). The predictive ability was similar or improved when these models were analyzed by modality. Patient and examination information readily available in the EMR can be successfully used to predict radiology no-shows. Moving forward, this information can be proactively leveraged to identify patients who might benefit from additional patient engagement through appointment reminders or other targeted interventions to avoid no-shows. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Controlling Type I Error Rates in Assessing DIF for Logistic Regression Method Combined with SIBTEST Regression Correction Procedure and DIF-Free-Then-DIF Strategy

ERIC Educational Resources Information Center

Shih, Ching-Lin; Liu, Tien-Hsiang; Wang, Wen-Chung

2014-01-01

The simultaneous item bias test (SIBTEST) method regression procedure and the differential item functioning (DIF)-free-then-DIF strategy are applied to the logistic regression (LR) method simultaneously in this study. These procedures are used to adjust the effects of matching true score on observed score and to better control the Type I error…

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

PubMed

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

2018-03-27

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

Item Response Theory Modeling of the Philadelphia Naming Test.

PubMed

Fergadiotis, Gerasimos; Kellough, Stacey; Hula, William D

2015-06-01

In this study, we investigated the fit of the Philadelphia Naming Test (PNT; Roach, Schwartz, Martin, Grewal, & Brecher, 1996) to an item-response-theory measurement model, estimated the precision of the resulting scores and item parameters, and provided a theoretical rationale for the interpretation of PNT overall scores by relating explanatory variables to item difficulty. This article describes the statistical model underlying the computer adaptive PNT presented in a companion article (Hula, Kellough, & Fergadiotis, 2015). Using archival data, we evaluated the fit of the PNT to 1- and 2-parameter logistic models and examined the precision of the resulting parameter estimates. We regressed the item difficulty estimates on three predictor variables: word length, age of acquisition, and contextual diversity. The 2-parameter logistic model demonstrated marginally better fit, but the fit of the 1-parameter logistic model was adequate. Precision was excellent for both person ability and item difficulty estimates. Word length, age of acquisition, and contextual diversity all independently contributed to variance in item difficulty. Item-response-theory methods can be productively used to analyze and quantify anomia severity in aphasia. Regression of item difficulty on lexical variables supported the validity of the PNT and interpretation of anomia severity scores in the context of current word-finding models.

Combinations of Multiple Neuroimaging Markers using Logistic Regression for Auxiliary Diagnosis of Alzheimer Disease and Mild Cognitive Impairment.

PubMed

Mao, Nini; Liu, Yunting; Chen, Kewei; Yao, Li; Wu, Xia

2018-06-05

Multiple neuroimaging modalities have been developed providing various aspects of information on the human brain. Used together and properly, these complementary multimodal neuroimaging data integrate multisource information which can facilitate a diagnosis and improve the diagnostic accuracy. In this study, 3 types of brain imaging data (sMRI, FDG-PET, and florbetapir-PET) were fused in the hope to improve diagnostic accuracy, and multivariate methods (logistic regression) were applied to these trimodal neuroimaging indices. Then, the receiver-operating characteristic (ROC) method was used to analyze the outcomes of the logistic classifier, with either each index, multiples from each modality, or all indices from all 3 modalities, to investigate their differential abilities to identify the disease. With increasing numbers of indices within each modality and across modalities, the accuracy of identifying Alzheimer disease (AD) increases to varying degrees. For example, the area under the ROC curve is above 0.98 when all the indices from the 3 imaging data types are combined. Using a combination of different indices, the results confirmed the initial hypothesis that different biomarkers were potentially complementary, and thus the conjoint analysis of multiple information from multiple sources would improve the capability to identify diseases such as AD and mild cognitive impairment. © 2018 S. Karger AG, Basel.

Access disparities to Magnet hospitals for patients undergoing neurosurgical operations

PubMed Central

Missios, Symeon; Bekelis, Kimon

2017-01-01

Background Centers of excellence focusing on quality improvement have demonstrated superior outcomes for a variety of surgical interventions. We investigated the presence of access disparities to hospitals recognized by the Magnet Recognition Program of the American Nurses Credentialing Center (ANCC) for patients undergoing neurosurgical operations. Methods We performed a cohort study of all neurosurgery patients who were registered in the New York Statewide Planning and Research Cooperative System (SPARCS) database from 2009–2013. We examined the association of African-American race and lack of insurance with Magnet status hospitalization for neurosurgical procedures. A mixed effects propensity adjusted multivariable regression analysis was used to control for confounding. Results During the study period, 190,535 neurosurgical patients met the inclusion criteria. Using a multivariable logistic regression, we demonstrate that African-Americans had lower admission rates to Magnet institutions (OR 0.62; 95% CI, 0.58–0.67). This persisted in a mixed effects logistic regression model (OR 0.77; 95% CI, 0.70–0.83) to adjust for clustering at the patient county level, and a propensity score adjusted logistic regression model (OR 0.75; 95% CI, 0.69–0.82). Additionally, lack of insurance was associated with lower admission rates to Magnet institutions (OR 0.71; 95% CI, 0.68–0.73), in a multivariable logistic regression model. This persisted in a mixed effects logistic regression model (OR 0.72; 95% CI, 0.69–0.74), and a propensity score adjusted logistic regression model (OR 0.72; 95% CI, 0.69–0.75). Conclusions Using a comprehensive all-payer cohort of neurosurgery patients in New York State we identified an association of African-American race and lack of insurance with lower rates of admission to Magnet hospitals. PMID:28684152

[Application of SAS macro to evaluated multiplicative and additive interaction in logistic and Cox regression in clinical practices].

PubMed

Nie, Z Q; Ou, Y Q; Zhuang, J; Qu, Y J; Mai, J Z; Chen, J M; Liu, X Q

2016-05-01

Conditional logistic regression analysis and unconditional logistic regression analysis are commonly used in case control study, but Cox proportional hazard model is often used in survival data analysis. Most literature only refer to main effect model, however, generalized linear model differs from general linear model, and the interaction was composed of multiplicative interaction and additive interaction. The former is only statistical significant, but the latter has biological significance. In this paper, macros was written by using SAS 9.4 and the contrast ratio, attributable proportion due to interaction and synergy index were calculated while calculating the items of logistic and Cox regression interactions, and the confidence intervals of Wald, delta and profile likelihood were used to evaluate additive interaction for the reference in big data analysis in clinical epidemiology and in analysis of genetic multiplicative and additive interactions.

Wildlife tradeoffs based on landscape models of habitat preference

USGS Publications Warehouse

Loehle, C.; Mitchell, M.S.; White, M.

2000-01-01

Wildlife tradeoffs based on landscape models of habitat preference were presented. Multiscale logistic regression models were used and based on these models a spatial optimization technique was utilized to generate optimal maps. The tradeoffs were analyzed by gradually increasing the weighting on a single species in the objective function over a series of simulations. Results indicated that efficiency of habitat management for species diversity could be maximized for small landscapes by incorporating spatial context.

No rationale for 1 variable per 10 events criterion for binary logistic regression analysis.

PubMed

van Smeden, Maarten; de Groot, Joris A H; Moons, Karel G M; Collins, Gary S; Altman, Douglas G; Eijkemans, Marinus J C; Reitsma, Johannes B

2016-11-24

Ten events per variable (EPV) is a widely advocated minimal criterion for sample size considerations in logistic regression analysis. Of three previous simulation studies that examined this minimal EPV criterion only one supports the use of a minimum of 10 EPV. In this paper, we examine the reasons for substantial differences between these extensive simulation studies. The current study uses Monte Carlo simulations to evaluate small sample bias, coverage of confidence intervals and mean square error of logit coefficients. Logistic regression models fitted by maximum likelihood and a modified estimation procedure, known as Firth's correction, are compared. The results show that besides EPV, the problems associated with low EPV depend on other factors such as the total sample size. It is also demonstrated that simulation results can be dominated by even a few simulated data sets for which the prediction of the outcome by the covariates is perfect ('separation'). We reveal that different approaches for identifying and handling separation leads to substantially different simulation results. We further show that Firth's correction can be used to improve the accuracy of regression coefficients and alleviate the problems associated with separation. The current evidence supporting EPV rules for binary logistic regression is weak. Given our findings, there is an urgent need for new research to provide guidance for supporting sample size considerations for binary logistic regression analysis.

4D-Fingerprint Categorical QSAR Models for Skin Sensitization Based on Classification Local Lymph Node Assay Measures

PubMed Central

Li, Yi; Tseng, Yufeng J.; Pan, Dahua; Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Hopfinger, Anton J.

2008-01-01

Currently, the only validated methods to identify skin sensitization effects are in vivo models, such as the Local Lymph Node Assay (LLNA) and guinea pig studies. There is a tremendous need, in particular due to novel legislation, to develop animal alternatives, eg. Quantitative Structure-Activity Relationship (QSAR) models. Here, QSAR models for skin sensitization using LLNA data have been constructed. The descriptors used to generate these models are derived from the 4D-molecular similarity paradigm and are referred to as universal 4D-fingerprints. A training set of 132 structurally diverse compounds and a test set of 15 structurally diverse compounds were used in this study. The statistical methodologies used to build the models are logistic regression (LR), and partial least square coupled logistic regression (PLS-LR), which prove to be effective tools for studying skin sensitization measures expressed in the two categorical terms of sensitizer and non-sensitizer. QSAR models with low values of the Hosmer-Lemeshow goodness-of-fit statistic, χHL2, are significant and predictive. For the training set, the cross-validated prediction accuracy of the logistic regression models ranges from 77.3% to 78.0%, while that of PLS-logistic regression models ranges from 87.1% to 89.4%. For the test set, the prediction accuracy of logistic regression models ranges from 80.0%-86.7%, while that of PLS-logistic regression models ranges from 73.3%-80.0%. The QSAR models are made up of 4D-fingerprints related to aromatic atoms, hydrogen bond acceptors and negatively partially charged atoms. PMID:17226934

MODELING SNAKE MICROHABITAT FROM RADIOTELEMETRY STUDIES USING POLYTOMOUS LOGISTIC REGRESSION

EPA Science Inventory

Multivariate analysis of snake microhabitat has historically used techniques that were derived under assumptions of normality and common covariance structure (e.g., discriminant function analysis, MANOVA). In this study, polytomous logistic regression (PLR which does not require ...

Deciphering factors controlling groundwater arsenic spatial variability in Bangladesh

NASA Astrophysics Data System (ADS)

Tan, Z.; Yang, Q.; Zheng, C.; Zheng, Y.

2017-12-01

Elevated concentrations of geogenic arsenic in groundwater have been found in many countries to exceed 10 μg/L, the WHO's guideline value for drinking water. A common yet unexplained characteristic of groundwater arsenic spatial distribution is the extensive variability at various spatial scales. This study investigates factors influencing the spatial variability of groundwater arsenic in Bangladesh to improve the accuracy of models predicting arsenic exceedance rate spatially. A novel boosted regression tree method is used to establish a weak-learning ensemble model, which is compared to a linear model using a conventional stepwise logistic regression method. The boosted regression tree models offer the advantage of parametric interaction when big datasets are analyzed in comparison to the logistic regression. The point data set (n=3,538) of groundwater hydrochemistry with 19 parameters was obtained by the British Geological Survey in 2001. The spatial data sets of geological parameters (n=13) were from the Consortium for Spatial Information, Technical University of Denmark, University of East Anglia and the FAO, while the soil parameters (n=42) were from the Harmonized World Soil Database. The aforementioned parameters were regressed to categorical groundwater arsenic concentrations below or above three thresholds: 5 μg/L, 10 μg/L and 50 μg/L to identify respective controlling factors. Boosted regression tree method outperformed logistic regression methods in all three threshold levels in terms of accuracy, specificity and sensitivity, resulting in an improvement of spatial distribution map of probability of groundwater arsenic exceeding all three thresholds when compared to disjunctive-kriging interpolated spatial arsenic map using the same groundwater arsenic dataset. Boosted regression tree models also show that the most important controlling factors of groundwater arsenic distribution include groundwater iron content and well depth for all three thresholds. The probability of a well with iron content higher than 5mg/L to contain greater than 5 μg/L, 10 μg/L and 50 μg/L As is estimated to be more than 91%, 85% and 51%, respectively, while the probability of a well from depth more than 160m to contain more than 5 μg/L, 10 μg/L and 50 μg/L As is estimated to be less than 38%, 25% and 14%, respectively.

Selecting risk factors: a comparison of discriminant analysis, logistic regression and Cox's regression model using data from the Tromsø Heart Study.

PubMed

Brenn, T; Arnesen, E

1985-01-01

For comparative evaluation, discriminant analysis, logistic regression and Cox's model were used to select risk factors for total and coronary deaths among 6595 men aged 20-49 followed for 9 years. Groups with mortality between 5 and 93 per 1000 were considered. Discriminant analysis selected variable sets only marginally different from the logistic and Cox methods which always selected the same sets. A time-saving option, offered for both the logistic and Cox selection, showed no advantage compared with discriminant analysis. Analysing more than 3800 subjects, the logistic and Cox methods consumed, respectively, 80 and 10 times more computer time than discriminant analysis. When including the same set of variables in non-stepwise analyses, all methods estimated coefficients that in most cases were almost identical. In conclusion, discriminant analysis is advocated for preliminary or stepwise analysis, otherwise Cox's method should be used.

Comparison of the Relationship between Women' Empowerment and Fertility between Single-child and Multi-child Families

PubMed Central

Saberi, Tahereh; Ehsanpour, Soheila; Mahaki, Behzad; Kohan, Shahnaz

2018-01-01

Background: The reduction in fertility and increase in the number of single-child families in Iran will result in an increased risk of population aging. One of the factors affecting fertility is women's empowerment. This study aimed to evaluate the relationship between women's empowerment and fertility in single-child and multi-child families. Materials and Methods: This case-control study was conducted among 350 women (120 who had only 1 child as case group and 230 who had 2 or more children as control group) of 15–49 years of age in Isfahan, Iran, in 2016. For data collection, a 2-part questionnaire was designed. Data were analyzed using independent t-test, Chi-square test, and logistic regression analysis. Results: The difference between average scores of women's empowerment in the case group 54.08 (9.88) and control group 51.47 (8.57) was significant (p = 0.002). Simple logistic regression analysis showed that under diploma education, compared to postgraduate education, (OR = 0.21, p = 0.001) and being a housewife, compared to being employed, (OR = 0.45, p = 0.004) decreased the odds of having only 1 child. Multiple logistic regression results showed that the relationship between women's empowerment and fertility was not significant (p = 0.265). Conclusions: Although women in single-child families were more empowered, this was not the main reason for their preference to have only 1 child. In fact, educated and employed women postpone marriage and childbearing and limit fertility to only 1 child despite their desire. PMID:29628961

Comprehension of texts by deaf elementary school students: The role of grammatical understanding.

PubMed

Barajas, Carmen; González-Cuenca, Antonia M; Carrero, Francisco

2016-12-01

The aim of this study was to analyze how the reading process of deaf Spanish elementary school students is affected both by those components that explain reading comprehension according to the Simple View of Reading model: decoding and linguistic comprehension (both lexical and grammatical) and by other variables that are external to the reading process: the type of assistive technology used, the age at which it is implanted or fitted, the participant's socioeconomic status and school stage. Forty-seven students aged between 6 and 13 years participated in the study; all presented with profound or severe prelingual bilateral deafness, and all used digital hearing aids or cochlear implants. Students' text comprehension skills, decoding skills and oral comprehension skills (both lexical and grammatical) were evaluated. Logistic regression analysis indicated that neither the type of assistive technology, age at time of fitting or activation, socioeconomic status, nor school stage could predict the presence or absence of difficulties in text comprehension. Furthermore, logistic regression analysis indicated that neither decoding skills, nor lexical age could predict competency in text comprehension; however, grammatical age could explain 41% of the variance. Probing deeper into the effect of grammatical understanding, logistic regression analysis indicated that a participant's understanding of reversible passive object-verb-subject sentences and reversible predicative subject-verb-object sentences accounted for 38% of the variance in text comprehension. Based on these results, we suggest that it might be beneficial to devise and evaluate interventions that focus specifically on grammatical comprehension. Copyright © 2016 Elsevier Ltd. All rights reserved.

The base rates and factors associated with reported access to firearms in psychiatric inpatients.

PubMed

Kolla, Bhanu Prakash; O'Connor, Stephen S; Lineberry, Timothy W

2011-01-01

The aim of this study was to define whether specific patient demographic groups, diagnoses or other factors are associated with psychiatric inpatients reporting firearms access. A retrospective medical records review study was conducted using information on access to firearms from electronic medical records for all patients 16 years and older admitted between July 2007 and May 2008 at the Mayo Clinic Psychiatric Hospital in Rochester, MN. Data were obtained only on patients providing authorization for record review. Data were analyzed using univariate and multivariate logistic regression analyses accounting for gender, diagnostic groups, comorbid substance use, history of suicide attempts and family history of suicide/suicide attempts. Seventy-four percent (1169/1580) of patients provided research authorization. The ratio of men to women was identical in both research and nonresearch authorization groups. There were 14.6% of inpatients who reported firearms access. In univariate analysis, men were more likely (P<.0001) to report access than women, and a history of previous suicide attempt(s) was associated with decreased access (P=.02). Multiple logistic regression analyses controlling for other factors found females and patients with history of previous suicide attempt(s) less likely to report access, while patients with a family history of suicide or suicide attempts reported increased firearms access. Diagnostic groups were not associated with access on univariate or multiple logistic regression analyses. Men and inpatients with a family history of suicide/suicide attempts were more likely to report firearms access. Clinicians should develop standardized systems of identification of firearms access and provide guidance on removal. Copyright © 2011 Elsevier Inc. All rights reserved.

[Multivariate ordinal logistic regression analysis on the association between consumption of fried food and both esophageal cancer and precancerous lesions].

PubMed

Guo, L W; Liu, S Z; Zhang, M; Chen, Q; Zhang, S K; Sun, X B

2017-12-10

Objective: To investigate the effect of fried food intake on the pathogenesis of esophageal cancer and precancerous lesions. Methods: From 2005 to 2013, all the residents aged 40-69 years from 11 counties (cities) where cancer screening of upper gastrointestinal cancer had been conducted in rural areas of Henan province, were recruited as the subjects of study. Information on demography and lifestyle was collected. The residents under study were screened with iodine staining endoscopic examination and biopsy samples were diagnosed pathologically, under standardized criteria. Subjects with high risk were divided into the groups based on their different pathological degrees. Multivariate ordinal logistic regression analysis was used to analyze the relationship between the frequency of fried food intake and esophageal cancer and precancerous lesions. Results: A total number of 8 792 cases with normal esophagus, 3 680 with mild hyperplasia, 972 with moderate hyperplasia, 413 with severe hyperplasia carcinoma in situ, and 336 cases of esophageal cancer were recruited. Results from multivariate logistic regression analysis showed that, when compared with those who did not eat fried food, the intake of fried food (<2 times/week: OR =1.60, 95% CI : 1.40-1.83; ≥2 times/week: OR =2.58, 95% CI : 1.98-3.37) appeared a risk factor for both esophageal cancer or precancerous lesions after adjustment for age, sex, marital status, educational level, body mass index, smoking and alcohol intake. Conclusion: The intake of fried food appeared a risk factor for both esophageal cancer and precancerous lesions.

Knowledge, attitudes and practices survey on organ donation among a selected adult population of Pakistan

PubMed Central

Saleem, Taimur; Ishaque, Sidra; Habib, Nida; Hussain, Syedda Saadia; Jawed, Areeba; Khan, Aamir Ali; Ahmad, Muhammad Imran; Iftikhar, Mian Omer; Mughal, Hamza Pervez; Jehan, Imtiaz

2009-01-01

Background To determine the knowledge, attitudes and practices regarding organ donation in a selected adult population in Pakistan. Methods Convenience sampling was used to generate a sample of 440; 408 interviews were successfully completed and used for analysis. Data collection was carried out via a face to face interview based on a pre-tested questionnaire in selected public areas of Karachi, Pakistan. Data was analyzed using SPSS v.15 and associations were tested using the Pearson's Chi square test. Multiple logistic regression was used to find independent predictors of knowledge status and motivation of organ donation. Results Knowledge about organ donation was significantly associated with education (p = 0.000) and socioeconomic status (p = 0.038). 70/198 (35.3%) people expressed a high motivation to donate. Allowance of organ donation in religion was significantly associated with the motivation to donate (p = 0.000). Multiple logistic regression analysis revealed that higher level of education and higher socioeconomic status were significant (p < 0.05) independent predictors of knowledge status of organ donation. For motivation, multiple logistic regression revealed that higher socioeconomic status, adequate knowledge score and belief that organ donation is allowed in religion were significant (p < 0.05) independent predictors. Television emerged as the major source of information. Only 3.5% had themselves donated an organ; with only one person being an actual kidney donor. Conclusion Better knowledge may ultimately translate into the act of donation. Effective measures should be taken to educate people with relevant information with the involvement of media, doctors and religious scholars. PMID:19534793

Modification of the Mantel-Haenszel and Logistic Regression DIF Procedures to Incorporate the SIBTEST Regression Correction

ERIC Educational Resources Information Center

DeMars, Christine E.

2009-01-01

The Mantel-Haenszel (MH) and logistic regression (LR) differential item functioning (DIF) procedures have inflated Type I error rates when there are large mean group differences, short tests, and large sample sizes.When there are large group differences in mean score, groups matched on the observed number-correct score differ on true score,…

Reviewers’ Ratings and Bibliometric Indicators: Hand in Hand When Assessing Over Research Proposals?

PubMed Central

Cabezas-Clavijo, Álvaro; Robinson-García, Nicolás; Escabias, Manuel; Jiménez-Contreras, Evaristo

2013-01-01

Background The peer review system has been traditionally challenged due to its many limitations especially for allocating funding. Bibliometric indicators may well present themselves as a complement. Objective We analyze the relationship between peers’ ratings and bibliometric indicators for Spanish researchers in the 2007 National R&D Plan for 23 research fields. Methods and Materials We analyze peers’ ratings for 2333 applications. We also gathered principal investigators’ research output and impact and studied the differences between accepted and rejected applications. We used the Web of Science database and focused on the 2002-2006 period. First, we analyzed the distribution of granted and rejected proposals considering a given set of bibliometric indicators to test if there are significant differences. Then, we applied a multiple logistic regression analysis to determine if bibliometric indicators can explain by themselves the concession of grant proposals. Results 63.4% of the applications were funded. Bibliometric indicators for accepted proposals showed a better previous performance than for those rejected; however the correlation between peer review and bibliometric indicators is very heterogeneous among most areas. The logistic regression analysis showed that the main bibliometric indicators that explain the granting of research proposals in most cases are the output (number of published articles) and the number of papers published in journals that belong to the first quartile ranking of the Journal Citations Report. Discussion Bibliometric indicators predict the concession of grant proposals at least as well as peer ratings. Social Sciences and Education are the only areas where no relation was found, although this may be due to the limitations of the Web of Science’s coverage. These findings encourage the use of bibliometric indicators as a complement to peer review in most of the analyzed areas. PMID:23840840

[The prevalence and risk factors of anemia in a general population from Kailuan in north China].

PubMed

Li, J; Li, Z F; Hou, J Y; Lu, Y K; Zhang, X L; Zhang, X M; Zou, H R; Zhang, H; Cui, Y; Xie, Y H; Lu, B J; Zhang, P; Wang, J W; Zhang, L X

2018-05-01

Objective: To analyze the prevalence and risk factors of anemia in a general population in Kailuan. Methods: Working and retired employees in Kailuan Company who had participated in biennial physical examination from 2006-2014 were investigated by questionnaire and blood test. Hemoglobin levels<120 g/L in male and<110 g/L in female are defined as anemia. The trend of prevalence of anemia was analyzed by chi square test. Multivariable logistic regression was used to analyze the factors associated with anemia. Results: (1) The biennial prevalence of anemia in Kailuan during 2006-2014 were 3.7%, 3.1%, 2.4%, 1.3%, 1.5%. The corresponding proportion were 3.3%, 2.3%, 1.9%, 0.8%, 1.0% in males and 5.3%, 5.9%, 4.2%, 3.1% and 3.3% in females, respectively. The differences between males and females were statistically significant (all P <0.05). The prevalence of anemia declined over time ( P for trend<0.05). (2) The results of multivariable logistic regression showed that aging and elevated hs-CRP were positively associated with anemia, with OR= 1.01 (95% CI 1.01-1.02) and 1.03 (95% CI 1.02-1.03) , respectively. While male, BMI, physical exercise, smoking, hyperlipidemia were negatively associated with anemia with OR= 0.60 (95% CI 0.55-0.65) , 0.99 (95% CI 0.98-0.99) , 0.91 (95% CI 0.82-0.98) , 0.87 (95% CI 0.81-0.95) and 0.87 (95% CI 0.81-0.94) , respectively. Conclusions: The prevalence of anemia in a large general population in Kailuan has been analyzed. Prevalence of anemia is higher in males than females and declines over time. Several demographic and clinical characteristics are associated with anemia.

Survival analysis of postoperative nausea and vomiting in patients receiving patient-controlled epidural analgesia.

PubMed

Lee, Shang-Yi; Hung, Chih-Jen; Chen, Chih-Chieh; Wu, Chih-Cheng

2014-11-01

Postoperative nausea and vomiting as well as postoperative pain are two major concerns when patients undergo surgery and receive anesthetics. Various models and predictive methods have been developed to investigate the risk factors of postoperative nausea and vomiting, and different types of preventive managements have subsequently been developed. However, there continues to be a wide variation in the previously reported incidence rates of postoperative nausea and vomiting. This may have occurred because patients were assessed at different time points, coupled with the overall limitation of the statistical methods used. However, using survival analysis with Cox regression, and thus factoring in these time effects, may solve this statistical limitation and reveal risk factors related to the occurrence of postoperative nausea and vomiting in the following period. In this retrospective, observational, uni-institutional study, we analyzed the results of 229 patients who received patient-controlled epidural analgesia following surgery from June 2007 to December 2007. We investigated the risk factors for the occurrence of postoperative nausea and vomiting, and also assessed the effect of evaluating patients at different time points using the Cox proportional hazards model. Furthermore, the results of this inquiry were compared with those results using logistic regression. The overall incidence of postoperative nausea and vomiting in our study was 35.4%. Using logistic regression, we found that only sex, but not the total doses and the average dose of opioids, had significant effects on the occurrence of postoperative nausea and vomiting at some time points. Cox regression showed that, when patients consumed a higher average dose of opioids, this correlated with a higher incidence of postoperative nausea and vomiting with a hazard ratio of 1.286. Survival analysis using Cox regression showed that the average consumption of opioids played an important role in postoperative nausea and vomiting, a result not found by logistic regression. Therefore, the incidence of postoperative nausea and vomiting in patients cannot be reliably determined on the basis of a single visit at one point in time. Copyright © 2014. Published by Elsevier Taiwan.

Satellite rainfall retrieval by logistic regression

NASA Technical Reports Server (NTRS)

Chiu, Long S.

1986-01-01

The potential use of logistic regression in rainfall estimation from satellite measurements is investigated. Satellite measurements provide covariate information in terms of radiances from different remote sensors.The logistic regression technique can effectively accommodate many covariates and test their significance in the estimation. The outcome from the logistical model is the probability that the rainrate of a satellite pixel is above a certain threshold. By varying the thresholds, a rainrate histogram can be obtained, from which the mean and the variant can be estimated. A logistical model is developed and applied to rainfall data collected during GATE, using as covariates the fractional rain area and a radiance measurement which is deduced from a microwave temperature-rainrate relation. It is demonstrated that the fractional rain area is an important covariate in the model, consistent with the use of the so-called Area Time Integral in estimating total rain volume in other studies. To calibrate the logistical model, simulated rain fields generated by rainfield models with prescribed parameters are needed. A stringent test of the logistical model is its ability to recover the prescribed parameters of simulated rain fields. A rain field simulation model which preserves the fractional rain area and lognormality of rainrates as found in GATE is developed. A stochastic regression model of branching and immigration whose solutions are lognormally distributed in some asymptotic limits has also been developed.

Occupational factors contributing to low self-esteem in registered nurses and licensed practical nurses: a multivariate analysis.

PubMed

Imai, K

2001-03-01

The present study examines job-related factors leading to low self-esteem in nurses. The lowering of self-esteem suggests that such nurses had difficulty in fully accepting themselves and their circumstances. Subjects were registered nurses (RN) and licensed practical nurses (LPN) at hospitals, and unemployed registered nurses (UEN) seeking employment. Questionnaires were provided at 53 hospitals and a Nurse Bank in Kanagawa Prefecture. The responses of 552 RN, 146 LPN and 433 UEN were analyzed. Questions were asked about personal life, past or present nursing experience, working conditions, nursing skills, satisfaction with work performance and self-esteem. Factors giving rise to low self-esteem were determined using logistic regression analysis and logistic discriminant analysis. Employment status and qualifications were determined to be the most important factors determining the self-esteem of nurses. The next most important factors were 'a limited number of years of experience (less than five years)' and 'dissatisfaction with discretion and responsibility as a nurse' (P < 0.01). Adjusted odds ratio for a reduction in self-esteem for LPN was 4.07 times higher than for UEN, and 2.2 times higher than for RN by logistic regression analysis. LPN are treated as unskilled workers, and thus significant differences were apparent in their performance of certain job tasks. These differences were analyzed using discriminant analysis, and were referred to as follows, 1: Advanced assessment skills, 2: Advanced technical skills, 3: Advanced communication skills, and 4: Nursing plan and documentation (positive discrimination rate was 70.8%). Job dissatisfaction is closely associated with the level of professional training. Continuous education and a feedback system for various levels of nurses are needed.

Practical Session: Logistic Regression

NASA Astrophysics Data System (ADS)

Clausel, M.; Grégoire, G.

2014-12-01

An exercise is proposed to illustrate the logistic regression. One investigates the different risk factors in the apparition of coronary heart disease. It has been proposed in Chapter 5 of the book of D.G. Kleinbaum and M. Klein, "Logistic Regression", Statistics for Biology and Health, Springer Science Business Media, LLC (2010) and also by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr341.pdf). This example is based on data given in the file evans.txt coming from http://www.sph.emory.edu/dkleinb/logreg3.htm#data.

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

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

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test ofmore » the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake.« less

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

NASA Astrophysics Data System (ADS)

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd; Baharum, Adam

2015-10-01

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test of the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake.

The cross-validated AUC for MCP-logistic regression with high-dimensional data.

PubMed

Jiang, Dingfeng; Huang, Jian; Zhang, Ying

2013-10-01

We propose a cross-validated area under the receiving operator characteristic (ROC) curve (CV-AUC) criterion for tuning parameter selection for penalized methods in sparse, high-dimensional logistic regression models. We use this criterion in combination with the minimax concave penalty (MCP) method for variable selection. The CV-AUC criterion is specifically designed for optimizing the classification performance for binary outcome data. To implement the proposed approach, we derive an efficient coordinate descent algorithm to compute the MCP-logistic regression solution surface. Simulation studies are conducted to evaluate the finite sample performance of the proposed method and its comparison with the existing methods including the Akaike information criterion (AIC), Bayesian information criterion (BIC) or Extended BIC (EBIC). The model selected based on the CV-AUC criterion tends to have a larger predictive AUC and smaller classification error than those with tuning parameters selected using the AIC, BIC or EBIC. We illustrate the application of the MCP-logistic regression with the CV-AUC criterion on three microarray datasets from the studies that attempt to identify genes related to cancers. Our simulation studies and data examples demonstrate that the CV-AUC is an attractive method for tuning parameter selection for penalized methods in high-dimensional logistic regression models.

The Persistence of the Gender Gap in Introductory Physics

NASA Astrophysics Data System (ADS)

Kost, Lauren E.; Pollock, Steven J.; Finkelstein, Noah D.

2008-10-01

We previously showed[l] that despite teaching with interactive engagement techniques, the gap in performance between males and females on conceptual learning surveys persisted from pre- to posttest, at our institution. Such findings were counter to previously published work[2]. Our current work analyzes factors that may influence the observed gender gap in our courses. Posttest conceptual assessment data are modeled using both multiple regression and logistic regression analyses to estimate the gender gap in posttest scores after controlling for background factors that vary by gender. We find that at our institution the gender gap persists in interactive physics classes, but is largely due to differences in physics and math preparation and incoming attitudes and beliefs.

Modeling the probability of giving birth at health institutions among pregnant women attending antenatal care in West Shewa Zone, Oromia, Ethiopia: a cross sectional study.

PubMed

Dida, Nagasa; Birhanu, Zewdie; Gerbaba, Mulusew; Tilahun, Dejen; Morankar, Sudhakar

2014-06-01

Although ante natal care and institutional delivery is effective means for reducing maternal morbidity and mortality, the probability of giving birth at health institutions among ante natal care attendants has not been modeled in Ethiopia. Therefore, the objective of this study was to model predictors of giving birth at health institutions among expectant mothers following antenatal care. Facility based cross sectional study design was conducted among 322 consecutively selected mothers who were following ante natal care in two districts of West Shewa Zone, Oromia Regional State, Ethiopia. Participants were proportionally recruited from six health institutions. The data were analyzed using SPSS version 17.0. Multivariable logistic regression was employed to develop the prediction model. The final regression model had good discrimination power (89.2%), optimum sensitivity (89.0%) and specificity (80.0%) to predict the probability of giving birth at health institutions. Accordingly, self efficacy (beta=0.41), perceived barrier (beta=-0.31) and perceived susceptibility (beta=0.29) were significantly predicted the probability of giving birth at health institutions. The present study showed that logistic regression model has predicted the probability of giving birth at health institutions and identified significant predictors which health care providers should take into account in promotion of institutional delivery.

A simple approach to power and sample size calculations in logistic regression and Cox regression models.

PubMed

Vaeth, Michael; Skovlund, Eva

2004-06-15

For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.

Effect of a laboratory result pager on provider behavior in a neonatal intensive care unit.

PubMed

Samal, L; Stavroudis, Ta; Miller, Re; Lehmann, Hp; Lehmann, Cu

2011-01-01

A computerized laboratory result paging system (LRPS) that alerts providers about abnormal results ("push") may improve upon active laboratory result review ("pull"). However, implementing such a system in the intensive care setting may be hindered by low signal-to-noise ratio, which may lead to alert fatigue. To evaluate the impact of an LRPS in a Neonatal Intensive Care Unit. Utilizing paper chart review, we tallied provider orders following an abnormal laboratory result before and after implementation of an LRPS. Orders were compared with a predefined set of appropriate orders for such an abnormal result. The likelihood of a provider response in the post-implementation period as compared to the pre-implementation period was analyzed using logistic regression. The provider responses were analyzed using logistic regression to control for potential confounders. The likelihood of a provider response to an abnormal laboratory result did not change significantly after implementation of an LRPS. (Odds Ratio 0.90, 95% CI 0.63-1.30, p-value 0.58) However, when providers did respond to an alert, the type of response was different. The proportion of repeat laboratory tests increased. (26/378 vs. 7/278, p-value = 0.02). Although the laboratory result pager altered healthcare provider behavior in the Neonatal Intensive Care Unit, it did not increase the overall likelihood of provider response.

Prophylaxis of venous thromboembolism in elderly patients with multimorbidity.

PubMed

Marcucci, Maura; Iorio, Alfonso; Nobili, Alessandro; Tettamanti, Mauro; Pasina, Luca; Djade, Codjo Djignefa; Marengoni, Alessandra; Salerno, Francesco; Corrao, Salvatore; Mannucci, Pier Mannuccio

2013-09-01

Pharmacological thromboprophylaxis (TP) is known to reduce venous thromboembolism (VTE) in medical inpatients, but the criteria for risk-driven prescription, safety and impact on mortality are still debated. We analyze data on elderly patients with multimorbidities admitted in the year 2010 to the Italian internal medicine wards participating in the REPOSI registry to investigate the rate of TP during the hospital stay, and analyze the factors that are related to its prescription. Multivariate logistic regression, area under the ROC curve and CART analysis were performed to look for independent predictors of TP prescription. Association between TP and VTE, bleeding and death in hospital and during the 3-month post-discharge follow-up were explored by logistic regression and propensity score analysis. Among the 1,380 patients enrolled, 171 (15.2 %) were on TP during the hospital stay (162 on low molecular weight heparins, 9 on fondaparinux). The disability Barthel index was the main independent predictor of TP prescription. Rate of fatal and non-fatal VTE and bleeding during and after hospitalization did not differ between TP and non-TP patients. In-hospital and post-discharge mortality was significantly higher in patients on TP, that however was not an independent predictor of mortality. Among elderly medical patients there was a relatively low rate of TP, that was more frequently prescribed to patients with a higher degree of disability and who had an overall higher mortality.

Radiation dose rates of differentiated thyroid cancer patients after 131I therapy.

PubMed

Jin, Pingyan; Feng, Huijuan; Ouyang, Wei; Wu, Juqing; Chen, Pan; Wang, Jing; Sun, Yungang; Xian, Jialang; Huang, Liuhua

2018-05-01

Postoperative 131 I treatment for differentiated thyroid cancer (DTC) can create a radiation hazard for nearby persons. The present prospective study aimed to investigate radiation dose rates in 131 I-treated DTC patients to provide references for radiation protection. A total of 141 131 I-treated DTC patients were enrolled, and grouped into a singular treatment (ST) group and a repeated treatment (RT) group. The radiation dose rate of 131 I-treated patients was measured. The rate of achieving discharge compliance and restricted contact time were analyzed based on Chinese regulations. Multivariate logistic regression analysis was used to analyze the independent factors associated with the clearance of radioiodine. The rate of achieving discharge compliance ( 131 I retention < 400 MBq) was 79.8 and 93.7% at day 2 (D2) for the ST and RT groups, respectively, and reached 100% at D7 and D4, respectively. The restricted contact time with 131 I-treated patients at 0.5 m for medical staff, caregivers, family members, and the general public ranged from 4 to 7 days. Multivariate logistic regression analysis showed that the 24-h iodine uptake rate was the only significant factor associated with radioiodine clearance. For the radiation safety of 131 I-treated DTC patients, the present results can provide radiometric data for radiation protection.

Predictors of Gestational Diabetes Mellitus in Chinese Women with Polycystic Ovary Syndrome: A Cross-Sectional Study.

PubMed

Zhang, Ya-Jie; Jin, Hua; Qin, Zhen-Li; Ma, Jin-Long; Zhao, Han; Zhang, Ling; Chen, Zi-Jiang

2016-01-01

This study aims to explore the independent predictors of gestational diabetes mellitus (GDM) in Chinese women with polycystic ovary syndrome (PCOS). This cross-sectional study analyzed primigravid women with PCOS and classified them as those with and without GDM. Independent risk factors and model performance were analyzed using multivariate logistic regression and the area under the curve (AUC) of receiver operating characteristic (ROC), respectively. Maternal body mass index, waist circumference, waist-to-hip ratio (WHR), fasting glucose, insulin, sex hormone-binding globulin (SHBG), homeostasis model assessment-insulin resistance (HOMA-IR) before pregnancy, gestation weight gain before 24 weeks and the incidence of family history of diabetes were different in the 2 groups. Logistic regression analysis showed that pre-pregnancy WHR, SHBG, HOMA-IR and gestation weight gain before 24 weeks were the independent predictors of GDM. ROC curve analysis confirmed that gestation weight gain before 24 weeks (AUC 0.767, 95% CI 0.688-0.841), pre-pregnant WHR (AUC 0.725, 95% CI 0.649-0.802), HOMA-IR (AUC 0.711, 95% CI 0.632-0.790) and SHBG levels (AUC 0.709, 95% CI 0.625-0.793) were the strong risk factors. In Chinese women with PCOS, factors of gestation weight gain before 24 weeks, pre-pregnant WHR, HOMA-IR and SHBG levels are strongly associated with subsequent development of GDM. © 2015 S. Karger AG, Basel.

Cigarette smoking and its association with serum lipid/lipoprotein among Chinese nonagenarians/centenarians

PubMed Central

2012-01-01

Objective Cigarette smoking had been confirmed as an increased risk for dyslipidemia, but none of the evidence was from long-lived population. In present study, we detected relationship between cigarette smoking habits and serum lipid/lipoprotein (serum Triglyceride (TG), Total cholesterol (TC), Low-density lipoprotein (LDL) and high-density lipoprotein (HDL)) among Chinese Nonagenarians/Centenarian. Methods The present study analyzed data from the survey that was conducted on all residents aged 90 years or more in a district, there were 2,311,709 inhabitants in 2005. Unpaired Student’s t test, χ2 test, and multiple logistic regression were used to analyze datas. Results The individuals included in the statistical analysis were 216 men and 445 women. Current smokers had lower level of TC (4.05 ± 0.81 vs. 4.21 ± 0.87, t = 2.403, P = 0.017) and lower prevalence of hypercholesteremia (9.62% vs. 15.13%, χ2 = 3.018,P = 0.049) than nonsmokers. Unadjusted and adjusted multiple logistic regressions showed that cigarette smoking was not associated with risk for abnormal serum lipid/lipoprotein. Conclusions In summary, we found that among Chinese nonagenarians/centenarians, cigarette smoking habits were not associated with increased risk for dyslipidemia, which was different from the association of smoking habits with dyslipidemia in general population. PMID:22828289

Induced abortion: risk factors for adolescent female students, a Brazilian study.

PubMed

Correia, Divanise S; Cavalcante, Jairo C; Maia, Eulália M C

2009-12-16

The purpose of this study was to analyze risk factors for abortion among female teenagers from 12 to 19 years of age in the city of Maceió, Brazil. This is a cross-sectional study, conducted in ten schools. The sample was calculated by considering the number of admissions for postabortion curettage, obtained from the Information System of Hospitalization. Data were obtained through a semi-structured questionnaire divided into three basic blocks of data: sociodemographic, sexual life, and pregnancy/abortion. To analyze the data, the logistic regression model was used. The Forward Method was chosen to set the final model that minimizes the number of variables and maximizes the accuracy of the model. The significant analysis between the dichotomous variables provided eight significant variables. Two of them are protective for abortion: the ages 12-14 years and talking with parents about sex. After the logistic regression, the receipt of support for abortion was the most significant variable of all. The adolescent with an active sexual life, a previous pregnancy, who is married, and has received support for an abortion has a 99.74% probability for an abortion. The results of this study, demonstrating the importance of the group in adolescence, and the statistical significance of having a partner to support and approve the pregnancy appears as a preventive factor for abortion. It shows the importance of support and companionship for adolescent women.

[Characteristics of elderly leaders volunteering to participate in a fall prevention programme].

PubMed

Shimanuki, Hideki; Ueki, Shouzoh; Ito, Tunehisa; Honda, Haruhiko; Takato, Jinro; Kasai, Toshiyuki; Sakamoto, Yuzuru; Niino, Naoakira; Haga, Hiroshi

2005-09-01

This study was conducted to assess characteristics of elderly leaders volunteering to participate in a fall prevention programme. We surveyed 1,503 individuals (75 elderly leaders volunteering to participate in a fall prevention programme and 1,428 non-leader elderly) among the elderly population living in a rural community, Miyagi Prefecture. Subjects were aged 70-84 years. The questionnaire covered socio-demographic factors, as well as physical, psychology and social variables. To analyze the characteristics of the elderly leaders volunteering to participate in this programme, the relationships of socio-demographic, physical, psychology and social factors to whether the elderly were leaders in the programme were analyzed using logistic regression. As a result of multiple logistic regression analysis, the characteristics of elderly leaders volunteering to participate in the fall prevention programme were as follows; 1) being male (OR = 0.25, 95%CI 0.14-0.44); 2) young age (OR=0.43, 95%CI 0.25-0.73); 3) having a high intellectual activity (OR = 2.72, 95%CI 1.65-4.48); 4) being well satisfied with their health (OR = 1.45, 95%CI 1.02-2.07), and 5) having a high IKIGAI (OR = 1.06, 95%CI 1.01-1.13). Only elderly individuals capable of high-level intellectual activities can fill the roles of elderly volunteer group leaders discussed in this study.

Gender and metabolic differences of gallstone diseases

PubMed Central

Sun, Hui; Tang, Hong; Jiang, Shan; Zeng, Li; Chen, En-Qiang; Zhou, Tao-You; Wang, You-Juan

2009-01-01

AIM: To investigate the risk factors for gallstone disease in the general population of Chengdu, China. METHODS: This study was conducted at the West China Hospital. Subjects who received a physical examination at this hospital between January and December 2007 were included. Body mass index, blood pressure, fasting plasma glucose, serum lipid and lipoproteins concentrations were analyzed. Gallstone disease was diagnosed by ultrasound or on the basis of a history of cholecystectomy because of gallstone disease. Unconditional logistic regression analysis was used to investigate the risk factors for gallstone disease, and the Chi-square test was used to analyze differences in the incidence of metabolic disorders between subjects with and without gallstone disease. RESULTS: A total of 3573 people were included, 10.7% (384/3573) of whom had gallstone diseases. Multiple logistic regression analysis indicated that the incidence of gallstone disease in subjects aged 40-64 or ≥ 65 years was significantly different from that in those aged 18-39 years (P < 0.05); the incidence was higher in women than in men (P < 0.05). In men, a high level of fasting plasma glucose was obvious in gallstone disease (P < 0.05), and in women, hypertriglyceridemia or obesity were significant in gallstone disease (P < 0.05). CONCLUSION: We assume that age and sex are profoundly associated with the incidence of gallstone disease; the metabolic risk factors for gallstone disease were different between men and women. PMID:19370788

[Suicidal Behavior and Attention Decifit Hyperactivity Disorder in Adolescents of Medellin (Colombia), 2011-2012].

PubMed

Restrepo-Bernal, Diana; Bonfante-Olivares, Laura; Torres de Galvis, Yolanda; Berbesi-Fernández, Dedsy; Sierra-Hincapié, Gloria

2014-01-01

Suicide is a public health problem. In Colombia, teenagers are considered a group at high risk for suicidal behavior. To explore the possible association between suicidal behavior and attention deficit hyperactivity disorder in adolescents of Medellin. Observational, cross-sectional, analytical study. The Composite International Diagnostic Interview was applied to a total of 447 adolescents and the sociodemographic, clinical, familiar, and life event variables of interest were analyzed. The descriptive analysis of qualitative variables are presented as absolute values and frequencies, and the age was described with median [interquartile range]. A logistic regression model was constructed with explanatory variables that showed statistical association. Data were analyzed with SPSS® software version 21.0. Of the total, 59.1% were female, and the median age was 16 [14-18] years. Suicidal behavior was presented in 31% of females and 23% of males. Attention deficit was present in 6.3% of adolescents. The logistic regression analysis showed that the variables that best explained the suicidal behavior of adolescents were: female sex, post-traumatic stress disorder, panic disorder, and cocaine use. The diagnosis and early intervention of attention deficit hyperactivity disorder in children may be a useful strategy in the prevention of suicidal behavior in adolescents. Copyright © 2014 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.

Robust logistic regression to narrow down the winner's curse for rare and recessive susceptibility variants.

PubMed

Kesselmeier, Miriam; Lorenzo Bermejo, Justo

2017-11-01

Logistic regression is the most common technique used for genetic case-control association studies. A disadvantage of standard maximum likelihood estimators of the genotype relative risk (GRR) is their strong dependence on outlier subjects, for example, patients diagnosed at unusually young age. Robust methods are available to constrain outlier influence, but they are scarcely used in genetic studies. This article provides a non-intimidating introduction to robust logistic regression, and investigates its benefits and limitations in genetic association studies. We applied the bounded Huber and extended the R package 'robustbase' with the re-descending Hampel functions to down-weight outlier influence. Computer simulations were carried out to assess the type I error rate, mean squared error (MSE) and statistical power according to major characteristics of the genetic study and investigated markers. Simulations were complemented with the analysis of real data. Both standard and robust estimation controlled type I error rates. Standard logistic regression showed the highest power but standard GRR estimates also showed the largest bias and MSE, in particular for associated rare and recessive variants. For illustration, a recessive variant with a true GRR=6.32 and a minor allele frequency=0.05 investigated in a 1000 case/1000 control study by standard logistic regression resulted in power=0.60 and MSE=16.5. The corresponding figures for Huber-based estimation were power=0.51 and MSE=0.53. Overall, Hampel- and Huber-based GRR estimates did not differ much. Robust logistic regression may represent a valuable alternative to standard maximum likelihood estimation when the focus lies on risk prediction rather than identification of susceptibility variants. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

[Comparison of arterial stiffness in non-hypertensive and hypertensive population of various age groups].

PubMed

Zhang, Y J; Wu, S L; Li, H Y; Zhao, Q H; Ning, C H; Zhang, R Y; Yu, J X; Li, W; Chen, S H; Gao, J S

2018-01-24

Objective: To investigate the impact of blood pressure and age on arterial stiffness in general population. Methods: Participants who took part in 2010, 2012 and 2014 Kailuan health examination were included. Data of brachial ankle pulse wave velocity (baPWV) examination were analyzed. According to the WHO criteria of age, participants were divided into 3 age groups: 18-44 years group ( n= 11 608), 45-59 years group ( n= 12 757), above 60 years group ( n= 5 002). Participants were further divided into hypertension group and non-hypertension group according to the diagnostic criteria for hypertension (2010 Chinese guidelines for the managemengt of hypertension). Multiple linear regression analysis was used to analyze the association between systolic blood pressure (SBP) with baPWV in the total participants and then stratified by age groups. Multivariate logistic regression model was used to analyze the influence of blood pressure on arterial stiffness (baPWV≥1 400 cm/s) of various groups. Results: (1)The baseline characteristics of all participants: 35 350 participants completed 2010, 2012 and 2014 Kailuan examinations and took part in baPWV examination. 2 237 participants without blood pressure measurement values were excluded, 1 569 participants with history of peripheral artery disease were excluded, we also excluded 1 016 participants with history of cardiac-cerebral vascular disease. Data from 29 367 participants were analyzed. The age was (48.0±12.4) years old, 21 305 were males (72.5%). (2) Distribution of baPWV in various age groups: baPWV increased with aging. In non-hypertension population, baPWV in 18-44 years group, 45-59 years group, above 60 years group were as follows: 1 299.3, 1 428.7 and 1 704.6 cm/s, respectively. For hypertension participants, the respective values of baPWV were: 1 498.4, 1 640.7 and 1 921.4 cm/s. BaPWV was significantly higher in hypertension group than non-hypertension group of respective age groups ( P< 0.05). (3) Multiple linear regression analysis defined risk factors of baPWV: Multivariate linear regression analysis showed that baPWV was positively correlated with SBP( t= 39.30, P< 0.001), and same results were found in the sub-age groups ( t -value was 37.72, 27.30, 9.15, all P< 0.001, respectively) after adjustment for other confounding factors, including age, sex, pulse pressure(PP), body mass index (BMI), fasting blood glucose (FBG), total cholesterol (TC), smoking, drinking, physical exercise, antihypertensive medications, lipid-lowering medication. (4) Multivariate logistic regression analysis of baPWV-related factors: After adjustment for other confounding factors, including age, sex, PP, BMI, FBG, TC, smoking, drinking, physical exercise, antihypertensive medication, lipid-lowering medication, multivariate logistic regression analysis showed that risks for increased arterial stiffness in hypertension group were higher than those in non-hypertension group, the OR in participants with hypertension was 2.54 (2.35-2.74) in the total participants, and same results were also found in sub-age groups, the OR s were 3.22(2.86-3.63), 2.48(2.23-2.76), and 1.91(1.42-2.56), respectively, in each sub-age group. Conclusion: SBP is positively related to arterial stiffness in different age groups, and hypertension is a risk factor for increased arterial stiffness in different age groups. Clinical Trial Registry Chinese Clinical Trial Registry, ChiCTR-TNC-11001489.

Determination of Landslide and Driftwood Potentials by Fixed-wing UAV-Borne RGB and NIR images: A Case Study of Shenmu Area in Taiwan

NASA Astrophysics Data System (ADS)

Chen, Su-Chin; Hsiao, Yu-Shen; Chung, Ta-Hsien

2015-04-01

This study is aimed at determining the landslide and driftwood potentials at Shenmu area in Taiwan by Unmanned Aerial Vehicle (UAV). High-resolution orthomosaics and digital surface models (DSMs) are both obtained from several UAV practical surveys by using a red-green-blue(RGB) camera and a near-infrared(NIR) one, respectively. Couples of artificial aerial survey targets are used for ground control in photogrammtry. The algorithm for this study is based on Logistic regression. 8 main factors, which are elevations, terrain slopes, terrain aspects, terrain reliefs, terrain roughness, distances to roads, distances to rivers, land utilizations, are taken into consideration in our Logistic regression model. The related results from UAV are compared with those from traditional photogrammetry. Overall, the study is focusing on monitoring the distribution of the areas with high-risk landslide and driftwood potentials in Shenmu area by Fixed-wing UAV-Borne RGB and NIR images. We also further analyze the relationship between forests, landslides, disaster potentials and upper river areas.

Risk and protective factors of posttraumatic stress disorder among African American women living with HIV.

PubMed

Andu, Eaden; Wagenaar, Brad H; Kemp, Chris G; Nevin, Paul E; Simoni, Jane M; Andrasik, Michele; Cohn, Susan E; French, Audrey L; Rao, Deepa

2018-04-26

We sought to examine risk and protective factors for Posttraumatic Stress Disorder (PTSD) among African American women living with HIV. This is a cross-sectional analysis of baseline data from a randomized trial of an HIV stigma reduction intervention. We examined data from two-hundred and thirty-nine African American women living with HIV. We examined whether age, marital status, level of education, internalized HIV-related stigma, and social support as potential protective and risk factors for PTSD symptoms using logistic regression. We analyzed bi-variate associations between each variable and PTSD symptoms, and constructed a multivariate logistic regression model adjusting for all variables. We found 67% reported clinically significant PTSD symptoms at baseline. Our results suggest that age, education, and internalized stigma were found to be associated with PTSD symptoms (p < 0.001), with older age and more education as protective factors and stigma as a risk factor for PTSD. Therefore, understanding this relationship may help improve assessment and treatment through evidence- based and trauma-informed strategies.

[Transmission disequilibrium test for nonsyndromic cleft lip and palate and segment homeobox gene-1 gene].

PubMed

Wu, Ping-An; Li, Yun-Liang; Wu, Han-Jiang; Wang, Kai; Fan, Guo-Zheng

2007-09-01

To investigate the relationship between muscle segment homeobox gene-1 (MSX1) and the genetic susceptibility of nonsyndromic cleft lip and palate (NSCLP) in Hunan Hans. One microsatellite DNA marker CA repeat in MSX1 intron region was used as genetic marker. The genotypes of 387 members in 129 NSCLP nuclear family trios were analyzed by polymerase chain reaction (PCR) and denaturing polyacrylamide gel electrophoresis. Then transmission disequilibrium test (TDT) and Logistic regression analysis were used to conduct association analysis. TDT analysis confirmed that CA4 allele in CL/P and CPO groups preferentially transmitted to the affected offspring (P = 0.018, P = 0.041). Logistic regression analysis indicated that the recessive model of inheritance was supported, and CA4 itself or CA4 acting as a marker for a disease allele or haplotype was inherited in a recessive fashion (P = 0.009). MSX1 gene is associated with NSCLP, and MSX1 gene may be directly involved either in the etiology of NSCLP or in linkage disequilibrium with disease-predisposing sites.

Analyzing Whitebark Pine Distribution in the Northern Rocky Mountains in Support of Grizzly Bear Recovery

NASA Astrophysics Data System (ADS)

Lawrence, R.; Landenburger, L.; Jewett, J.

2007-12-01

Whitebark pine seeds have long been identified as the most significant vegetative food source for grizzly bears in the Greater Yellowstone Ecosystem (GYE) and, hence, a crucial element of suitable grizzly bear habitat. The overall health and status of whitebark pine in the GYE is currently threatened by mountain pine beetle infestations and the spread of whitepine blister rust. Whitebark pine distribution (presence/absence) was mapped for the GYE using Landsat 7 Enhanced Thematic Mapper (ETM+) imagery and topographic data as part of a long-term inter-agency monitoring program. Logistic regression was compared with classification tree analysis (CTA) with and without boosting. Overall comparative classification accuracies for the central portion of the GYE covering three ETM+ images along a single path ranged from 91.6% using logistic regression to 95.8% with See5's CTA algorithm with the maximum 99 boosts. The analysis is being extended to the entire northern Rocky Mountain Ecosystem and extended over decadal time scales. The analysis is being extended to the entire northern Rocky Mountain Ecosystem and extended over decadal time scales.

Risk factors for antepartum fetal death.

PubMed

Oron, T; Sheiner, E; Shoham-Vardi, I; Mazor, M; Katz, M; Hallak, M

2001-09-01

To determine the demographic, maternal, pregnancy-related and fetal risk factors for antepartum fetal death (APFD). From our perinatal database between the years 1990 and 1997, 68,870 singleton birth files were analyzed. Fetuses weighing < 1,000 g at birth and those with structural malformations and/or known chromosomal anomalies were excluded from the study. In order to determine independent factors contributing to APFD, a multiple logistic regression model was constructed. During the study period there were 246 cases of APFD (3.6 per 1,000 births). The following obstetric factors significantly correlated with APFD in a multiple logistic regression model: preterm deliveries: small size for gestational age (SGA), multiparity (> 5 deliveries), oligohydramnios, placental abruption, umbilical cord complications (cord around the neck and true knot of cord), pathologic presentations (nonvertex) and meconium-stained amniotic fluid. APFD was not significantly associated with advanced maternal age. APFD was significantly associated with several risk factors. Placental and umbilical cord pathologies might be the direct cause of death. Grand multiparity, oligohydramnios, meconium-stained amniotic fluid, pathologic presentations and suspected SGA should be carefully evaluated during pregnancy in order to decrease the incidence of APFD.

Association between domestic violence and women's quality of life 1

PubMed Central

de Lucena, Kerle Dayana Tavares; Vianna, Rodrigo Pinheiro de Toledo; do Nascimento, João Agnaldo; Campos, Hemílio Fernandes Coelho; Oliveira, Elaine Cristina Tôrres

2017-01-01

ABSTRACT Objective: to analyze the association between domestic violence against women and quality of life. Method: a cross-sectional population-based household survey conducted with women 18 years and older, using a stratified sample by neighborhoods. For analysis, prevalence of domestic violence and quality of life index was verified and logistic regression was used to determine associations, with a significance level of 5%. Results: 424 women who had a prevalence of domestic violence of 54.4% and a quality of life index of 61.59 participated in this study. It was verified, through logistic regression, that domestic violence is associated with women's quality of life (p=0,017). The observed variables that influence the occurrence of domestic violence were in the social relations domain (p=0,000), provision of medical treatment for women (p=0,019) and safety (p=0,006). Conclusion: the study confirmed the evidence of an association between domestic violence against women and quality of life, a situation that reaffirms the importance of constructing public policies focused on gender emancipation. PMID:28591305

Influence of child rearing by grandparent on the development of children aged six to twelve years.

PubMed

Nanthamongkolchai, Sutham; Munsawaengsub, Chokchai; Nanthamongkolchai, Chantira

2009-03-01

To investigate the influence of child rearing by grandparent on the development of children aged six to twelve years. A cross-sectional study was conducted in 320 children that were cared for by a parent and grandparent selected by cluster sampling. The data were collected between March 10 and April 8, 2006 by questionnaire about child and family factors. The TONI-III test was used to test the child development. Data were analyzed by frequency distribution, logistic regression, and multiple logistic regression. Child caregiver had a significant influence on child development (p-value < 0.05). Children reared by a grandparent had 2.0 times higher chance of having delayed development compared with those who were reared by the parent. In addition, significant family factors that had impact on the child development were child rearing and family income. Child rearing by a grandparent had 2.0 times higher chance of having delayed development than those reared by the parent. Therefore, family and health personnel should plan to ensure the development and learning process of children that are cared by the grandparent.

Frequency and clinical predictors of coronary artery disease in chronic renal failure renal transplant candidates.

PubMed

de Albuquerque Seixas, Emerson; Carmello, Beatriz Leone; Kojima, Christiane Akemi; Contti, Mariana Moraes; Modeli de Andrade, Luiz Gustavo; Maiello, José Roberto; Almeida, Fernando Antonio; Martin, Luis Cuadrado

2015-05-01

Cardiovascular diseases are major causes of mortality in chronic renal failure patients before and after renal transplantation. Among them, coronary disease presents a particular risk; however, risk predictors have been used to diagnose coronary heart disease. This study evaluated the frequency and importance of clinical predictors of coronary artery disease in chronic renal failure patients undergoing dialysis who were renal transplant candidates, and assessed a previously developed scoring system. Coronary angiographies conducted between March 2008 and April 2013 from 99 candidates for renal transplantation from two transplant centers in São Paulo state were analyzed for associations between significant coronary artery diseases (≥70% stenosis in one or more epicardial coronary arteries or ≥50% in the left main coronary artery) and clinical parameters. Univariate logistic regression analysis identified diabetes, angina, and/or previous infarction, clinical peripheral arterial disease and dyslipidemia as predictors of coronary artery disease. Multiple logistic regression analysis identified only diabetes and angina and/or previous infarction as independent predictors. The results corroborate previous studies demonstrating the importance of these factors when selecting patients for coronary angiography in clinical pretransplant evaluation.

Pregnancy and race/ethnicity as predictors of motivation for drug treatment.

PubMed

Mitchell, Mary M; Severtson, S Geoff; Latimer, William W

2008-01-01

While drug use during pregnancy represents substantial obstetrical risks to mother and baby, little research has examined motivation for drug treatment among pregnant women. We analyzed data collected between 2000 and 2007 from 149 drug-using women located in Baltimore, Maryland. We hypothesized that pregnant drug-using women would be more likely than non-pregnant drug-using women to express greater motivation for treatment. Also, we explored race/ethnicity differences in motivation for treatment. Propensity score analysis was used to match a sample of 49 pregnant drug-using women with 100 non-pregnant drug-using women. The first logistic regression model indicated that pregnant women were more than four times as likely as non-pregnant women to express greater motivation for treatment. The second logistic regression analysis indicated a significant interaction between pregnancy status and race/ethnicity, such that white pregnant women were nearly eight times as likely as African-American pregnant women to score higher on the motivation for treatment measure. These results suggest that African-American pregnant drug-using women should be targeted for interventions that increase their motivation for treatment.

Relationship between risk factors and activities of daily living using modified Shah Barthel Index in stroke patients

NASA Astrophysics Data System (ADS)

Kusumaningsih, W.; Rachmayanti, S.; Werdhani, R. A.

2017-08-01

Hypertension and diabetes mellitus are the most common risk factors of stroke. The study aimed to determine the relationship between hypertension and diabetes mellitus risk factors and dependence on assistance with activities of daily living in chronic stroke patients. The study used an analytical observational cross-sectional design. The study’s sample included 44 stroke patients selected using the quota sampling method. The relationship between the variables was analyzed using the bivariate chi-squared test and multivariate logistic regression. Based on the chi-squared test, the relationship between the Modified Shah Barthel Index (MSBI) score and hypertension and diabetes mellitus as stroke risk factors, were p = 0.122 and p = 0.002, respectively. The logistic regression results suggest that hypertension and diabetes mellitus are stroke risk factors related to the MSBI score: p = 0.076 (OR 4.076; CI 95% 0.861-19.297) and p = 0.007 (OR 22.690; CI 95% 2.332-220.722), respectively. Diabetes mellitus is the most prominent risk factor of severe dependency on assistance with activities of daily living in chronic stroke patients.

Vitamin D levels and their associations with survival and major disease outcomes in a large cohort of patients with chronic graft-vs-host disease

PubMed Central

Katić, Mašenjka; Pirsl, Filip; Steinberg, Seth M.; Dobbin, Marnie; Curtis, Lauren M.; Pulanić, Dražen; Desnica, Lana; Titarenko, Irina; Pavletic, Steven Z.

2016-01-01

Aim To identify the factors associated with vitamin D status in patients with chronic graft-vs-host disease (cGVHD) and evaluate the association between serum vitamin D (25(OH)D) levels and cGVHD characteristics and clinical outcomes defined by the National Institutes of Health (NIH) criteria. Methods 310 cGVHD patients enrolled in the NIH cGVHD natural history study (clinicaltrials.gov: NCT00092235) were analyzed. Univariate analysis and multiple logistic regression were used to determine the associations between various parameters and 25(OH)D levels, dichotomized into categorical variables: ≤20 and >20 ng/mL, and as a continuous parameter. Multiple logistic regression was used to develop a predictive model for low vitamin D. Survival analysis and association between cGVHD outcomes and 25(OH)D as a continuous as well as categorical variable: ≤20 and >20 ng/mL; <50 and ≥50 ng/mL, and among three ordered categories: ≤20, 20-50, and ≥50 ng/mL, was performed. PMID:27374829

Urban change analysis and future growth of Istanbul.

PubMed

Akın, Anıl; Sunar, Filiz; Berberoğlu, Süha

2015-08-01

This study is aimed at analyzing urban change within Istanbul and assessing the city's future growth potential using appropriate approach modeling for the year 2040. Urban growth is a major driving force of land-use change, and spatial and temporal components of urbanization can be identified through accurate spatial modeling. In this context, widely used urban modeling approaches, such as the Markov chain and logistic regression based on cellular automata (CA), were used to simulate urban growth within Istanbul. The distance from each pixel to the urban and road classes, elevation, and slope, together with municipality and land use maps (as an excluded layer), were identified as factors. Calibration data were obtained from remotely sensed data recorded in 1972, 1986, and 2013. Validation was performed by overlaying the simulated and actual 2013 urban maps, and a kappa index of agreement was derived. The results indicate that urban expansion will influence mainly forest areas during the time period of 2013-2040. The urban expansion was predicted as 429 and 327 km(2) with the Markov chain and logistic regression models, respectively.

Factors That Influence Surgical Margin State in Patients Undergoing Cold Knife Conization - A Single Center Experience.

PubMed

Aluloski, Igor; Tanturovski, Mile; Petrusevska, Gordana; Jovanovic, Rubens; Kostadinova-Kunovska, Slavica

2017-12-01

To evaluate the factors that influence the surgical margin state in patients undergoing cold knife conization at the University Clinic of Gynecology and Obstetrics in Skopje, Republic of Macedonia Materials and methods: We have retrospectively analyzed the medical records of all patients that underwent a cold knife conization at our Clinic in 2015. We cross-referenced the surgical margin state with the histopathological diagnosis (LSIL, HSIL or micro-invasive/invasive cancer), menopausal status of the patients, number of pregnancies, surgeon experience, operating time and cone depth. The data was analyzed with the Chi square test, Fisher's exact test for categorical data and Student's T test for continuous data and univariate and multivariate logistical regressions were performed. A total of 246 medical records have neen analyzed, out of which 29 (11.79%) patients had LSIL, 194 (78.86%) had HSIL and 23 (9.34%) patients suffered micro-invasive/invasive cervical cancer. The surgical margins were positive in 78 (31.7%) of the patients. The average age of the patients was 41.13 and 35 (14.23%) of the patients were menopausal. The multivariate logistic regression identified preoperative forceps biopsy of micro-invasive SCC, HSIL or higher cone specimen histology and shorter cone depth as independent predictors of surgical margin involvement in patients undergoing cold knife conization. In the current study, we have found no association between the inherent characteristics of the patient and the surgeon and the surgical margin state after a CKC. The most important predictors for positive margins were the severity of the lesion and the cone depth.

Risk factors for acute surgical site infections after lumbar surgery: a retrospective study.

PubMed

Lai, Qi; Song, Quanwei; Guo, Runsheng; Bi, Haidi; Liu, Xuqiang; Yu, Xiaolong; Zhu, Jianghao; Dai, Min; Zhang, Bin

2017-07-19

Currently, many scholars are concerned about the treatment of postoperative infection; however, few have completed multivariate analyses to determine factors that contribute to the risk of infection. Therefore, we conducted a multivariate analysis of a retrospectively collected database to analyze the risk factors for acute surgical site infection following lumbar surgery, including fracture fixation, lumbar fusion, and minimally invasive lumbar surgery. We retrospectively reviewed data from patients who underwent lumbar surgery between 2014 and 2016, including lumbar fusion, internal fracture fixation, and minimally invasive surgery in our hospital's spinal surgery unit. Patient demographics, procedures, and wound infection rates were analyzed using descriptive statistics, and risk factors were analyzed using logistic regression analyses. Twenty-six patients (2.81%) experienced acute surgical site infection following lumbar surgery in our study. The patients' mean body mass index, smoking history, operative time, blood loss, draining time, and drainage volume in the acute surgical site infection group were significantly different from those in the non-acute surgical site infection group (p < 0.05). Additionally, diabetes mellitus, chronic obstructive pulmonary disease, osteoporosis, preoperative antibiotics, type of disease, and operative type in the acute surgical site infection group were significantly different than those in the non-acute surgical site infection group (p < 0.05). Using binary logistic regression analyses, body mass index, smoking, diabetes mellitus, osteoporosis, preoperative antibiotics, fracture, operative type, operative time, blood loss, and drainage time were independent predictors of acute surgical site infection following lumbar surgery. In order to reduce the risk of infection following lumbar surgery, patients should be evaluated for the risk factors noted above.

Nonconvex Sparse Logistic Regression With Weakly Convex Regularization

NASA Astrophysics Data System (ADS)

Shen, Xinyue; Gu, Yuantao

2018-06-01

In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the $\\ell_0$ pseudo norm is able to better induce sparsity than the commonly used $\\ell_1$ norm. For a class of weakly convex sparsity inducing functions, we prove the nonconvexity of the corresponding sparse logistic regression problem, and study its local optimality conditions and the choice of the regularization parameter to exclude trivial solutions. Despite the nonconvexity, a method based on proximal gradient descent is used to solve the general weakly convex sparse logistic regression, and its convergence behavior is studied theoretically. Then the general framework is applied to a specific weakly convex function, and a necessary and sufficient local optimality condition is provided. The solution method is instantiated in this case as an iterative firm-shrinkage algorithm, and its effectiveness is demonstrated in numerical experiments by both randomly generated and real datasets.

A comparative study on entrepreneurial attitudes modeled with logistic regression and Bayes nets.

PubMed

López Puga, Jorge; García García, Juan

2012-11-01

Entrepreneurship research is receiving increasing attention in our context, as entrepreneurs are key social agents involved in economic development. We compare the success of the dichotomic logistic regression model and the Bayes simple classifier to predict entrepreneurship, after manipulating the percentage of missing data and the level of categorization in predictors. A sample of undergraduate university students (N = 1230) completed five scales (motivation, attitude towards business creation, obstacles, deficiencies, and training needs) and we found that each of them predicted different aspects of the tendency to business creation. Additionally, our results show that the receiver operating characteristic (ROC) curve is affected by the rate of missing data in both techniques, but logistic regression seems to be more vulnerable when faced with missing data, whereas Bayes nets underperform slightly when categorization has been manipulated. Our study sheds light on the potential entrepreneur profile and we propose to use Bayesian networks as an additional alternative to overcome the weaknesses of logistic regression when missing data are present in applied research.

Epidemiologic programs for computers and calculators. A microcomputer program for multiple logistic regression by unconditional and conditional maximum likelihood methods.

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

A frequent procedure in matched case-control studies is to report results from the multivariate unmatched analyses if they do not differ substantially from the ones obtained after conditioning on the matching variables. Although conceptually simple, this rule requires that an extensive series of logistic regression models be evaluated by both the conditional and unconditional maximum likelihood methods. Most computer programs for logistic regression employ only one maximum likelihood method, which requires that the analyses be performed in separate steps. This paper describes a Pascal microcomputer (IBM PC) program that performs multiple logistic regression by both maximum likelihood estimation methods, which obviates the need for switching between programs to obtain relative risk estimates from both matched and unmatched analyses. The program calculates most standard statistics and allows factoring of categorical or continuous variables by two distinct methods of contrast. A built-in, descriptive statistics option allows the user to inspect the distribution of cases and controls across categories of any given variable.

Comparison of cranial sex determination by discriminant analysis and logistic regression.

PubMed

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).

Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis

PubMed Central

Hill, Andrew; Loh, Po-Ru; Bharadwaj, Ragu B.; Pons, Pascal; Shang, Jingbo; Guinan, Eva; Lakhani, Karim; Kilty, Iain

2017-01-01

Abstract Background: The association of differing genotypes with disease-related phenotypic traits offers great potential to both help identify new therapeutic targets and support stratification of patients who would gain the greatest benefit from specific drug classes. Development of low-cost genotyping and sequencing has made collecting large-scale genotyping data routine in population and therapeutic intervention studies. In addition, a range of new technologies is being used to capture numerous new and complex phenotypic descriptors. As a result, genotype and phenotype datasets have grown exponentially. Genome-wide association studies associate genotypes and phenotypes using methods such as logistic regression. As existing tools for association analysis limit the efficiency by which value can be extracted from increasing volumes of data, there is a pressing need for new software tools that can accelerate association analyses on large genotype-phenotype datasets. Results: Using open innovation (OI) and contest-based crowdsourcing, the logistic regression analysis in a leading, community-standard genetics software package (PLINK 1.07) was substantially accelerated. OI allowed us to do this in <6 months by providing rapid access to highly skilled programmers with specialized, difficult-to-find skill sets. Through a crowd-based contest a combination of computational, numeric, and algorithmic approaches was identified that accelerated the logistic regression in PLINK 1.07 by 18- to 45-fold. Combining contest-derived logistic regression code with coarse-grained parallelization, multithreading, and associated changes to data initialization code further developed through distributed innovation, we achieved an end-to-end speedup of 591-fold for a data set size of 6678 subjects by 645 863 variants, compared to PLINK 1.07's logistic regression. This represents a reduction in run time from 4.8 hours to 29 seconds. Accelerated logistic regression code developed in this project has been incorporated into the PLINK2 project. Conclusions: Using iterative competition-based OI, we have developed a new, faster implementation of logistic regression for genome-wide association studies analysis. We present lessons learned and recommendations on running a successful OI process for bioinformatics. PMID:28327993

Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis.

PubMed

Hill, Andrew; Loh, Po-Ru; Bharadwaj, Ragu B; Pons, Pascal; Shang, Jingbo; Guinan, Eva; Lakhani, Karim; Kilty, Iain; Jelinsky, Scott A

2017-05-01

The association of differing genotypes with disease-related phenotypic traits offers great potential to both help identify new therapeutic targets and support stratification of patients who would gain the greatest benefit from specific drug classes. Development of low-cost genotyping and sequencing has made collecting large-scale genotyping data routine in population and therapeutic intervention studies. In addition, a range of new technologies is being used to capture numerous new and complex phenotypic descriptors. As a result, genotype and phenotype datasets have grown exponentially. Genome-wide association studies associate genotypes and phenotypes using methods such as logistic regression. As existing tools for association analysis limit the efficiency by which value can be extracted from increasing volumes of data, there is a pressing need for new software tools that can accelerate association analyses on large genotype-phenotype datasets. Using open innovation (OI) and contest-based crowdsourcing, the logistic regression analysis in a leading, community-standard genetics software package (PLINK 1.07) was substantially accelerated. OI allowed us to do this in <6 months by providing rapid access to highly skilled programmers with specialized, difficult-to-find skill sets. Through a crowd-based contest a combination of computational, numeric, and algorithmic approaches was identified that accelerated the logistic regression in PLINK 1.07 by 18- to 45-fold. Combining contest-derived logistic regression code with coarse-grained parallelization, multithreading, and associated changes to data initialization code further developed through distributed innovation, we achieved an end-to-end speedup of 591-fold for a data set size of 6678 subjects by 645 863 variants, compared to PLINK 1.07's logistic regression. This represents a reduction in run time from 4.8 hours to 29 seconds. Accelerated logistic regression code developed in this project has been incorporated into the PLINK2 project. Using iterative competition-based OI, we have developed a new, faster implementation of logistic regression for genome-wide association studies analysis. We present lessons learned and recommendations on running a successful OI process for bioinformatics. © The Author 2017. Published by Oxford University Press.

Easy and low-cost identification of metabolic syndrome in patients treated with second-generation antipsychotics: artificial neural network and logistic regression models.

PubMed

Lin, Chao-Cheng; Bai, Ya-Mei; Chen, Jen-Yeu; Hwang, Tzung-Jeng; Chen, Tzu-Ting; Chiu, Hung-Wen; Li, Yu-Chuan

2010-03-01

Metabolic syndrome (MetS) is an important side effect of second-generation antipsychotics (SGAs). However, many SGA-treated patients with MetS remain undetected. In this study, we trained and validated artificial neural network (ANN) and multiple logistic regression models without biochemical parameters to rapidly identify MetS in patients with SGA treatment. A total of 383 patients with a diagnosis of schizophrenia or schizoaffective disorder (DSM-IV criteria) with SGA treatment for more than 6 months were investigated to determine whether they met the MetS criteria according to the International Diabetes Federation. The data for these patients were collected between March 2005 and September 2005. The input variables of ANN and logistic regression were limited to demographic and anthropometric data only. All models were trained by randomly selecting two-thirds of the patient data and were internally validated with the remaining one-third of the data. The models were then externally validated with data from 69 patients from another hospital, collected between March 2008 and June 2008. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of all models. Both the final ANN and logistic regression models had high accuracy (88.3% vs 83.6%), sensitivity (93.1% vs 86.2%), and specificity (86.9% vs 83.8%) to identify MetS in the internal validation set. The mean +/- SD AUC was high for both the ANN and logistic regression models (0.934 +/- 0.033 vs 0.922 +/- 0.035, P = .63). During external validation, high AUC was still obtained for both models. Waist circumference and diastolic blood pressure were the common variables that were left in the final ANN and logistic regression models. Our study developed accurate ANN and logistic regression models to detect MetS in patients with SGA treatment. The models are likely to provide a noninvasive tool for large-scale screening of MetS in this group of patients. (c) 2010 Physicians Postgraduate Press, Inc.

Bayesian logistic regression in detection of gene-steroid interaction for cancer at PDLIM5 locus.

PubMed

Wang, Ke-Sheng; Owusu, Daniel; Pan, Yue; Xie, Changchun

2016-06-01

The PDZ and LIM domain 5 (PDLIM5) gene may play a role in cancer, bipolar disorder, major depression, alcohol dependence and schizophrenia; however, little is known about the interaction effect of steroid and PDLIM5 gene on cancer. This study examined 47 single-nucleotide polymorphisms (SNPs) within the PDLIM5 gene in the Marshfield sample with 716 cancer patients (any diagnosed cancer, excluding minor skin cancer) and 2848 noncancer controls. Multiple logistic regression model in PLINK software was used to examine the association of each SNP with cancer. Bayesian logistic regression in PROC GENMOD in SAS statistical software, ver. 9.4 was used to detect gene- steroid interactions influencing cancer. Single marker analysis using PLINK identified 12 SNPs associated with cancer (P< 0.05); especially, SNP rs6532496 revealed the strongest association with cancer (P = 6.84 × 10⁻³); while the next best signal was rs951613 (P = 7.46 × 10⁻³). Classic logistic regression in PROC GENMOD showed that both rs6532496 and rs951613 revealed strong gene-steroid interaction effects (OR=2.18, 95% CI=1.31-3.63 with P = 2.9 × 10⁻³ for rs6532496 and OR=2.07, 95% CI=1.24-3.45 with P = 5.43 × 10⁻³ for rs951613, respectively). Results from Bayesian logistic regression showed stronger interaction effects (OR=2.26, 95% CI=1.2-3.38 for rs6532496 and OR=2.14, 95% CI=1.14-3.2 for rs951613, respectively). All the 12 SNPs associated with cancer revealed significant gene-steroid interaction effects (P < 0.05); whereas 13 SNPs showed gene-steroid interaction effects without main effect on cancer. SNP rs4634230 revealed the strongest gene-steroid interaction effect (OR=2.49, 95% CI=1.5-4.13 with P = 4.0 × 10⁻⁴ based on the classic logistic regression and OR=2.59, 95% CI=1.4-3.97 from Bayesian logistic regression; respectively). This study provides evidence of common genetic variants within the PDLIM5 gene and interactions between PLDIM5 gene polymorphisms and steroid use influencing cancer.

An empirical comparison of methods for analyzing correlated data from a discrete choice survey to elicit patient preference for colorectal cancer screening

PubMed Central

2012-01-01

Background A discrete choice experiment (DCE) is a preference survey which asks participants to make a choice among product portfolios comparing the key product characteristics by performing several choice tasks. Analyzing DCE data needs to account for within-participant correlation because choices from the same participant are likely to be similar. In this study, we empirically compared some commonly-used statistical methods for analyzing DCE data while accounting for within-participant correlation based on a survey of patient preference for colorectal cancer (CRC) screening tests conducted in Hamilton, Ontario, Canada in 2002. Methods A two-stage DCE design was used to investigate the impact of six attributes on participants' preferences for CRC screening test and willingness to undertake the test. We compared six models for clustered binary outcomes (logistic and probit regressions using cluster-robust standard error (SE), random-effects and generalized estimating equation approaches) and three models for clustered nominal outcomes (multinomial logistic and probit regressions with cluster-robust SE and random-effects multinomial logistic model). We also fitted a bivariate probit model with cluster-robust SE treating the choices from two stages as two correlated binary outcomes. The rank of relative importance between attributes and the estimates of β coefficient within attributes were used to assess the model robustness. Results In total 468 participants with each completing 10 choices were analyzed. Similar results were reported for the rank of relative importance and β coefficients across models for stage-one data on evaluating participants' preferences for the test. The six attributes ranked from high to low as follows: cost, specificity, process, sensitivity, preparation and pain. However, the results differed across models for stage-two data on evaluating participants' willingness to undertake the tests. Little within-patient correlation (ICC ≈ 0) was found in stage-one data, but substantial within-patient correlation existed (ICC = 0.659) in stage-two data. Conclusions When small clustering effect presented in DCE data, results remained robust across statistical models. However, results varied when larger clustering effect presented. Therefore, it is important to assess the robustness of the estimates via sensitivity analysis using different models for analyzing clustered data from DCE studies. PMID:22348526

Deletion Diagnostics for Alternating Logistic Regressions

PubMed Central

Preisser, John S.; By, Kunthel; Perin, Jamie; Qaqish, Bahjat F.

2013-01-01

Deletion diagnostics are introduced for the regression analysis of clustered binary outcomes estimated with alternating logistic regressions, an implementation of generalized estimating equations (GEE) that estimates regression coefficients in a marginal mean model and in a model for the intracluster association given by the log odds ratio. The diagnostics are developed within an estimating equations framework that recasts the estimating functions for association parameters based upon conditional residuals into equivalent functions based upon marginal residuals. Extensions of earlier work on GEE diagnostics follow directly, including computational formulae for one-step deletion diagnostics that measure the influence of a cluster of observations on the estimated regression parameters and on the overall marginal mean or association model fit. The diagnostic formulae are evaluated with simulations studies and with an application concerning an assessment of factors associated with health maintenance visits in primary care medical practices. The application and the simulations demonstrate that the proposed cluster-deletion diagnostics for alternating logistic regressions are good approximations of their exact fully iterated counterparts. PMID:22777960

Estimating interaction on an additive scale between continuous determinants in a logistic regression model.

PubMed

Knol, Mirjam J; van der Tweel, Ingeborg; Grobbee, Diederick E; Numans, Mattijs E; Geerlings, Mirjam I

2007-10-01

To determine the presence of interaction in epidemiologic research, typically a product term is added to the regression model. In linear regression, the regression coefficient of the product term reflects interaction as departure from additivity. However, in logistic regression it refers to interaction as departure from multiplicativity. Rothman has argued that interaction estimated as departure from additivity better reflects biologic interaction. So far, literature on estimating interaction on an additive scale using logistic regression only focused on dichotomous determinants. The objective of the present study was to provide the methods to estimate interaction between continuous determinants and to illustrate these methods with a clinical example. and results From the existing literature we derived the formulas to quantify interaction as departure from additivity between one continuous and one dichotomous determinant and between two continuous determinants using logistic regression. Bootstrapping was used to calculate the corresponding confidence intervals. To illustrate the theory with an empirical example, data from the Utrecht Health Project were used, with age and body mass index as risk factors for elevated diastolic blood pressure. The methods and formulas presented in this article are intended to assist epidemiologists to calculate interaction on an additive scale between two variables on a certain outcome. The proposed methods are included in a spreadsheet which is freely available at: http://www.juliuscenter.nl/additive-interaction.xls.

[Analysis of risk factors for dry eye syndrome in visual display terminal workers].

PubMed

Zhu, Yong; Yu, Wen-lan; Xu, Ming; Han, Lei; Cao, Wen-dong; Zhang, Hong-bing; Zhang, Heng-dong

2013-08-01

To analyze the risk factors for dry eye syndrome in visual display terminal (VDT) workers and to provide a scientific basis for protecting the eye health of VDT workers. Questionnaire survey, Schirmer I test, tear break-up time test, and workshop microenvironment evaluation were performed in 185 VDT workers. Multivariate logistic regression analysis was performed to determine the risk factors for dry eye syndrome in VDT workers after adjustment for confounding factors. In the logistic regression model, the regression coefficients of daily mean time of exposure to screen, daily mean time of watching TV, parallel screen-eye angle, upward screen-eye angle, eye-screen distance of less than 20 cm, irregular breaks during screen-exposed work, age, and female gender on the results of Schirmer I test were 0.153, 0.548, 0.400, 0.796, 0.234, 0.516, 0.559, and -0.685, respectively; the regression coefficients of daily mean time of exposure to screen, parallel screen-eye angle, upward screen-eye angle, age, working years, and female gender on tear break-up time were 0.021, 0.625, 2.652, 0.749, 0.403, and 1.481, respectively. Daily mean time of exposure to screen, daily mean time of watching TV, parallel screen-eye angle, upward screen-eye angle, eye-screen distance of less than 20 cm, irregular breaks during screen-exposed work, age, and working years are risk factors for dry eye syndrome in VDT workers.

Assessing LULC changes over Chilika Lake watershed in Eastern India using Driving Force Analysis

NASA Astrophysics Data System (ADS)

Jadav, S.; Syed, T. H.

2017-12-01

Rapid population growth and industrial development has brought about significant changes in Land Use Land Cover (LULC) of many developing countries in the world. This study investigates LULC changes in the Chilika Lake watershed of Eastern India for the period of 1988 to 2016. The methodology involves pre-processing and classification of Landsat satellite images using support vector machine (SVM) supervised classification algorithm. Results reveal that `Cropland', `Emergent Vegetation' and `Settlement' has expanded over the study period by 284.61 km², 106.83 km² and 98.83 km² respectively. Contemporaneously, `Lake Area', `Vegetation' and `Scrub Land' have decreased by 121.62 km², 96.05 km² and 80.29 km² respectively. This study also analyzes five major driving force variables of socio-economic and climatological factors triggering LULC changes through a bivariate logistic regression model. The outcome gives credible relative operating characteristics (ROC) value of 0.76 that indicate goodness fit of logistic regression model. In addition, independent variables like distance to drainage network and average annual rainfall have negative regression coefficient values that represent decreased rate of dependent variable (changed LULC) whereas independent variables (population density, distance to road and distance to railway) have positive regression coefficient indicates increased rate of changed LULC . Results from this study will be crucial for planning and restoration of this vital lake water body that has major implications over the society and environment at large.

Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center

PubMed Central

Mehra, Tarun; Müller, Christian Thomas Benedikt; Volbracht, Jörk; Seifert, Burkhardt; Moos, Rudolf

2015-01-01

Principles Case weights of Diagnosis Related Groups (DRGs) are determined by the average cost of cases from a previous billing period. However, a significant amount of cases are largely over- or underfunded. We therefore decided to analyze earning outliers of our hospital as to search for predictors enabling a better grouping under SwissDRG. Methods 28,893 inpatient cases without additional private insurance discharged from our hospital in 2012 were included in our analysis. Outliers were defined by the interquartile range method. Predictors for deficit and profit outliers were determined with logistic regressions. Predictors were shortlisted with the LASSO regularized logistic regression method and compared to results of Random forest analysis. 10 of these parameters were selected for quantile regression analysis as to quantify their impact on earnings. Results Psychiatric diagnosis and admission as an emergency case were significant predictors for higher deficit with negative regression coefficients for all analyzed quantiles (p<0.001). Admission from an external health care provider was a significant predictor for a higher deficit in all but the 90% quantile (p<0.001 for Q10, Q20, Q50, Q80 and p = 0.0017 for Q90). Burns predicted higher earnings for cases which were favorably remunerated (p<0.001 for the 90% quantile). Osteoporosis predicted a higher deficit in the most underfunded cases, but did not predict differences in earnings for balanced or profitable cases (Q10 and Q20: p<0.00, Q50: p = 0.10, Q80: p = 0.88 and Q90: p = 0.52). ICU stay, mechanical and patient clinical complexity level score (PCCL) predicted higher losses at the 10% quantile but also higher profits at the 90% quantile (p<0.001). Conclusion We suggest considering psychiatric diagnosis, admission as an emergencay case and admission from an external health care provider as DRG split criteria as they predict large, consistent and significant losses. PMID:26517545

Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center.

PubMed

Mehra, Tarun; Müller, Christian Thomas Benedikt; Volbracht, Jörk; Seifert, Burkhardt; Moos, Rudolf

2015-01-01

Case weights of Diagnosis Related Groups (DRGs) are determined by the average cost of cases from a previous billing period. However, a significant amount of cases are largely over- or underfunded. We therefore decided to analyze earning outliers of our hospital as to search for predictors enabling a better grouping under SwissDRG. 28,893 inpatient cases without additional private insurance discharged from our hospital in 2012 were included in our analysis. Outliers were defined by the interquartile range method. Predictors for deficit and profit outliers were determined with logistic regressions. Predictors were shortlisted with the LASSO regularized logistic regression method and compared to results of Random forest analysis. 10 of these parameters were selected for quantile regression analysis as to quantify their impact on earnings. Psychiatric diagnosis and admission as an emergency case were significant predictors for higher deficit with negative regression coefficients for all analyzed quantiles (p<0.001). Admission from an external health care provider was a significant predictor for a higher deficit in all but the 90% quantile (p<0.001 for Q10, Q20, Q50, Q80 and p = 0.0017 for Q90). Burns predicted higher earnings for cases which were favorably remunerated (p<0.001 for the 90% quantile). Osteoporosis predicted a higher deficit in the most underfunded cases, but did not predict differences in earnings for balanced or profitable cases (Q10 and Q20: p<0.00, Q50: p = 0.10, Q80: p = 0.88 and Q90: p = 0.52). ICU stay, mechanical and patient clinical complexity level score (PCCL) predicted higher losses at the 10% quantile but also higher profits at the 90% quantile (p<0.001). We suggest considering psychiatric diagnosis, admission as an emergency case and admission from an external health care provider as DRG split criteria as they predict large, consistent and significant losses.

Logits and Tigers and Bears, Oh My! A Brief Look at the Simple Math of Logistic Regression and How It Can Improve Dissemination of Results

ERIC Educational Resources Information Center

Osborne, Jason W.

2012-01-01

Logistic regression is slowly gaining acceptance in the social sciences, and fills an important niche in the researcher's toolkit: being able to predict important outcomes that are not continuous in nature. While OLS regression is a valuable tool, it cannot routinely be used to predict outcomes that are binary or categorical in nature. These…

Breast density and parenchymal texture measures as potential risk factors for estrogen-receptor positive breast cancer

NASA Astrophysics Data System (ADS)

Keller, Brad M.; Chen, Jinbo; Conant, Emily F.; Kontos, Despina

2014-03-01

Accurate assessment of a woman's risk to develop specific subtypes of breast cancer is critical for appropriate utilization of chemopreventative measures, such as with tamoxifen in preventing estrogen-receptor positive breast cancer. In this context, we investigate quantitative measures of breast density and parenchymal texture, measures of glandular tissue content and tissue structure, as risk factors for estrogen-receptor positive (ER+) breast cancer. Mediolateral oblique (MLO) view digital mammograms of the contralateral breast from 106 women with unilateral invasive breast cancer were retrospectively analyzed. Breast density and parenchymal texture were analyzed via fully-automated software. Logistic regression with feature selection and was performed to predict ER+ versus ER- cancer status. A combined model considering all imaging measures extracted was compared to baseline models consisting of density-alone and texture-alone features. Area under the curve (AUC) of the receiver operating characteristic (ROC) and Delong's test were used to compare the models' discriminatory capacity for receptor status. The density-alone model had a discriminatory capacity of 0.62 AUC (p=0.05). The texture-alone model had a higher discriminatory capacity of 0.70 AUC (p=0.001), which was not significantly different compared to the density-alone model (p=0.37). In contrast the combined density-texture logistic regression model had a discriminatory capacity of 0.82 AUC (p<0.001), which was statistically significantly higher than both the density-alone (p<0.001) and texture-alone regression models (p=0.04). The combination of breast density and texture measures may have the potential to identify women specifically at risk for estrogen-receptor positive breast cancer and could be useful in triaging women into appropriate risk-reduction strategies.

[Analysis of the prevalence and risk factors of preoperative angiography confirmed coronary artery stenosis in patients with degenerative valvular heart disease].

PubMed

Xu, Z J; Pan, J; Zhou, Q; Wang, D J

2017-10-24

Objective: To estimate the prevalence and the risk factors of preoperative coronary angiography (CAG) confirmed coronary stenosis in patients with degenerative valvular heart disease. Methods: A total of 491 patients who underwent screening CAG before valvular surgery due to degenerative valvular heart disease were enrolled from January 2011 to September 2014 in our hospital, and clinical data were analyzed. According to CAG results, patients were divided into positive CAG result (PCAG) group or negative CAG (NCAG) group. Positive CAG result was defined as stenosis ≥50% of the diameter of the left main coronary artery or stenosis ≥70% of the diameter of left anterior descending, left circumflex artery, and right coronary artery.Risk factors of positive CAG result were analyzed by multivariable logistic regression analysis, and Bootstrap method was used to verify the results. Results: There were 47(9.57%)degenerative valvular heart disease patients with PCAG. Patients were older ((68.0±7.6)years vs.(62.6±7.1)years, P <0.001) and the prevalence of typical angina was significantly higher (14.89%(7/47)vs. 2.03%(9/444), P <0.001)in PCAG group than in NCAG group. Multivariable logistic regression analysis showed that age ( OR =1.118, 95% CI 1.067-1.172, P <0.001), typical angina ( OR =8.970, 95% CI 2.963-27.154, P <0.001), and serum concentration of apolipoprotein B ( OR =20.311, 95% CI 4.774-86.416, P <0.001) were the independent risk factors of PCAG in degenerative valvular heart disease patients. Bootstrap method revealed satisfactory repeatability of multivariable logistic regression analysis results (age: OR =1.118, 95% CI 1.068-1.178, P =0.001; typical angina: OR =8.970, 95% CI 2.338-35.891, P =0.001; serum concentration of apolipoprotein B: OR =20.311, 95% CI 4.639-91.977, P =0.001). Conclusions: A low prevalence of PCAG before valvular surgery is observed in degenerative valvular heart disease patients in this patient cohort. Age, typical angina, and serum concentration of apolipoprotein B are independent risk factors of PCAG in this patient cohort.

Using Cluster Bootstrapping to Analyze Nested Data With a Few Clusters.

PubMed

Huang, Francis L

2018-04-01

Cluster randomized trials involving participants nested within intact treatment and control groups are commonly performed in various educational, psychological, and biomedical studies. However, recruiting and retaining intact groups present various practical, financial, and logistical challenges to evaluators and often, cluster randomized trials are performed with a low number of clusters (~20 groups). Although multilevel models are often used to analyze nested data, researchers may be concerned of potentially biased results due to having only a few groups under study. Cluster bootstrapping has been suggested as an alternative procedure when analyzing clustered data though it has seen very little use in educational and psychological studies. Using a Monte Carlo simulation that varied the number of clusters, average cluster size, and intraclass correlations, we compared standard errors using cluster bootstrapping with those derived using ordinary least squares regression and multilevel models. Results indicate that cluster bootstrapping, though more computationally demanding, can be used as an alternative procedure for the analysis of clustered data when treatment effects at the group level are of primary interest. Supplementary material showing how to perform cluster bootstrapped regressions using R is also provided.

Predicting Social Trust with Binary Logistic Regression

ERIC Educational Resources Information Center

Adwere-Boamah, Joseph; Hufstedler, Shirley

2015-01-01

This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting…

Effect of folic acid on appetite in children: ordinal logistic and fuzzy logistic regressions.

PubMed

Namdari, Mahshid; Abadi, Alireza; Taheri, S Mahmoud; Rezaei, Mansour; Kalantari, Naser; Omidvar, Nasrin

2014-03-01

Reduced appetite and low food intake are often a concern in preschool children, since it can lead to malnutrition, a leading cause of impaired growth and mortality in childhood. It is occasionally considered that folic acid has a positive effect on appetite enhancement and consequently growth in children. The aim of this study was to assess the effect of folic acid on the appetite of preschool children 3 to 6 y old. The study sample included 127 children ages 3 to 6 who were randomly selected from 20 preschools in the city of Tehran in 2011. Since appetite was measured by linguistic terms, a fuzzy logistic regression was applied for modeling. The obtained results were compared with a statistical ordinal logistic model. After controlling for the potential confounders, in a statistical ordinal logistic model, serum folate showed a significantly positive effect on appetite. A small but positive effect of folate was detected by fuzzy logistic regression. Based on fuzzy regression, the risk for poor appetite in preschool children was related to the employment status of their mothers. In this study, a positive association was detected between the levels of serum folate and improved appetite. For further investigation, a randomized controlled, double-blind clinical trial could be helpful to address causality. Copyright © 2014 Elsevier Inc. All rights reserved.

Comparing Methods for Assessing Reliability Uncertainty Based on Pass/Fail Data Collected Over Time

DOE PAGES

Abes, Jeff I.; Hamada, Michael S.; Hills, Charles R.

2017-12-20

In this paper, we compare statistical methods for analyzing pass/fail data collected over time; some methods are traditional and one (the RADAR or Rationale for Assessing Degradation Arriving at Random) was recently developed. These methods are used to provide uncertainty bounds on reliability. We make observations about the methods' assumptions and properties. Finally, we illustrate the differences between two traditional methods, logistic regression and Weibull failure time analysis, and the RADAR method using a numerical example.

Logistically Sustaining Afloat-Staged Special Operations Forces through an LPD-17 Class Single-Ship Seabase

DTIC Science & Technology

2007-03-01

simulation are analyzed using regression, statistical and marginal benefit techniques to show how the MOEs are affected by varying levels of the...being supported by the seabase increases. A large marginal benefit is realized in reducing a unit’s frequency and time spent in a balk state by...units. SOF units operate within the range of sea-based helicopter assets; therefore the risk of a ‘ bingo ’ (i.e., near empty) fuel state is nearly

Comparing Methods for Assessing Reliability Uncertainty Based on Pass/Fail Data Collected Over Time

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

Abes, Jeff I.; Hamada, Michael S.; Hills, Charles R.

In this paper, we compare statistical methods for analyzing pass/fail data collected over time; some methods are traditional and one (the RADAR or Rationale for Assessing Degradation Arriving at Random) was recently developed. These methods are used to provide uncertainty bounds on reliability. We make observations about the methods' assumptions and properties. Finally, we illustrate the differences between two traditional methods, logistic regression and Weibull failure time analysis, and the RADAR method using a numerical example.

Fusion of multiscale wavelet-based fractal analysis on retina image for stroke prediction.

PubMed

Che Azemin, M Z; Kumar, Dinesh K; Wong, T Y; Wang, J J; Kawasaki, R; Mitchell, P; Arjunan, Sridhar P

2010-01-01

In this paper, we present a novel method of analyzing retinal vasculature using Fourier Fractal Dimension to extract the complexity of the retinal vasculature enhanced at different wavelet scales. Logistic regression was used as a fusion method to model the classifier for 5-year stroke prediction. The efficacy of this technique has been tested using standard pattern recognition performance evaluation, Receivers Operating Characteristics (ROC) analysis and medical prediction statistics, odds ratio. Stroke prediction model was developed using the proposed system.

Producing landslide susceptibility maps by utilizing machine learning methods. The case of Finikas catchment basin, North Peloponnese, Greece.

NASA Astrophysics Data System (ADS)

Tsangaratos, Paraskevas; Ilia, Ioanna; Loupasakis, Constantinos; Papadakis, Michalis; Karimalis, Antonios

2017-04-01

The main objective of the present study was to apply two machine learning methods for the production of a landslide susceptibility map in the Finikas catchment basin, located in North Peloponnese, Greece and to compare their results. Specifically, Logistic Regression and Random Forest were utilized, based on a database of 40 sites classified into two categories, non-landslide and landslide areas that were separated into a training dataset (70% of the total data) and a validation dataset (remaining 30%). The identification of the areas was established by analyzing airborne imagery, extensive field investigation and the examination of previous research studies. Six landslide related variables were analyzed, namely: lithology, elevation, slope, aspect, distance to rivers and distance to faults. Within the Finikas catchment basin most of the reported landslides were located along the road network and within the residential complexes, classified as rotational and translational slides, and rockfalls, mainly caused due to the physical conditions and the general geotechnical behavior of the geological formation that cover the area. Each landslide susceptibility map was reclassified by applying the Geometric Interval classification technique into five classes, namely: very low susceptibility, low susceptibility, moderate susceptibility, high susceptibility, and very high susceptibility. The comparison and validation of the outcomes of each model were achieved using statistical evaluation measures, the receiving operating characteristic and the area under the success and predictive rate curves. The computation process was carried out using RStudio an integrated development environment for R language and ArcGIS 10.1 for compiling the data and producing the landslide susceptibility maps. From the outcomes of the Logistic Regression analysis it was induced that the highest b coefficient is allocated to lithology and slope, which was 2.8423 and 1.5841, respectively. From the estimation of the mean decrease in Gini coefficient performed during the application of Random Forest and the mean decrease in accuracy the most important variable is slope followed by lithology, aspect, elevation, distance from river network, and distance from faults, while the most used variables during the training phase were the variable aspect (21.45%), slope (20.53%) and lithology (19.84%). The outcomes of the analysis are consistent with previous studies concerning the area of research, which have indicated the high influence of lithology and slope in the manifestation of landslides. High percentage of landslide occurrence has been observed in Plio-Pleistocene sediments, flysch formations, and Cretaceous limestone. Also the presences of landslides have been associated with the degree of weathering and fragmentation, the orientation of the discontinuities surfaces and the intense morphological relief. The most accurate model was Random Forest which identified correctly 92.00% of the instances during the training phase, followed by the Logistic Regression 89.00%. The same pattern of accuracy was calculated during the validation phase, in which the Random Forest achieved a classification accuracy of 93.00%, while the Logistic Regression model achieved an accuracy of 91.00%. In conclusion, the outcomes of the study could be a useful cartographic product to local authorities and government agencies during the implementation of successful decision-making and land use planning strategies. Keywords: Landslide Susceptibility, Logistic Regression, Random Forest, GIS, Greece.

Organizational Predictors and Use of Evidence-Based Practices in Adolescent Substance Abuse Treatment.

PubMed

Paino, Maria; Aletraris, Lydia; Roman, Paul M

2015-01-01

Adolescent substance abuse remains a significant problem in the United States, yet treatment centers do not always admit adolescent clients. In this paper, we first determine the extent to which treatment is available for adolescents in general and whether or not adolescent-specific (segregated) tracks are offered. Second, we examine the organizational characteristics associated with adolescent treatment. Third, we illuminate how the adolescent caseload in a treatment center is related to offering evidence-based practices (EBPs). Drawing upon a nationally representative sample of US treatment programs, we use logistic regression to assess how organizational characteristics are associated with the provision of adolescent treatment. Using ordinal logistic regression, we analyze how the treatment center's adolescent caseload and organizational characteristics affect the extent to which a treatment center offers medication-assisted treatment (MAT) and psychosocial treatment. Half (49.5%) of treatment programs admitted adolescents, and 41.8% offered an adolescent-specific track. Findings from the logistic regression suggested several organizational characteristics that were significantly associated with treating adolescents and/or having an adolescent-only track. Our findings from the ordinal models indicated a negative relationship between the percent of adolescents in a treatment center and the extent of MAT, and a positive relationship between the percent of adolescent clients and the extent of psychosocial treatment offered. This paper highlights organizational barriers to treatment entry for adolescents, who remain a small proportion of clients in treatment centers. When treatment centers serve adolescents, however, those adolescents are likely to receive care in adolescent-only tracks and/or services and in programs that offer several psychosocial EBPs. Finally, adolescents are less likely to receive treatment in centers that offer a variety of MAT.

An association between dietary habits and traffic accidents in patients with chronic liver disease: A data-mining analysis

PubMed Central

KAWAGUCHI, TAKUMI; SUETSUGU, TAKURO; OGATA, SHYOU; IMANAGA, MINAMI; ISHII, KUMIKO; ESAKI, NAO; SUGIMOTO, MASAKO; OTSUYAMA, JYURI; NAGAMATSU, AYU; TANIGUCHI, EITARO; ITOU, MINORU; ORIISHI, TETSUHARU; IWASAKI, SHOKO; MIURA, HIROKO; TORIMURA, TAKUJI

2016-01-01

The incidence of traffic accidents in patients with chronic liver disease (CLD) is high in the USA. However, the characteristics of patients, including dietary habits, differ between Japan and the USA. The present study investigated the incidence of traffic accidents in CLD patients and the clinical profiles associated with traffic accidents in Japan using a data-mining analysis. A cross-sectional study was performed and 256 subjects [148 CLD patients (CLD group) and 106 patients with other digestive diseases (disease control group)] were enrolled; 2 patients were excluded. The incidence of traffic accidents was compared between the two groups. Independent factors for traffic accidents were analyzed using logistic regression and decision-tree analyses. The incidence of traffic accidents did not differ between the CLD and disease control groups (8.8 vs. 11.3%). The results of the logistic regression analysis showed that yoghurt consumption was the only independent risk factor for traffic accidents (odds ratio, 0.37; 95% confidence interval, 0.16–0.85; P=0.0197). Similarly, the results of the decision-tree analysis showed that yoghurt consumption was the initial divergence variable. In patients who consumed yoghurt habitually, the incidence of traffic accidents was 6.6%, while that in patients who did not consume yoghurt was 16.0%. CLD was not identified as an independent factor in the logistic regression and decision-tree analyses. In conclusion, the difference in the incidence of traffic accidents in Japan between the CLD and disease control groups was insignificant. Furthermore, yoghurt consumption was an independent negative risk factor for traffic accidents in patients with digestive diseases, including CLD. PMID:27123257

An association between dietary habits and traffic accidents in patients with chronic liver disease: A data-mining analysis.

PubMed

Kawaguchi, Takumi; Suetsugu, Takuro; Ogata, Shyou; Imanaga, Minami; Ishii, Kumiko; Esaki, Nao; Sugimoto, Masako; Otsuyama, Jyuri; Nagamatsu, Ayu; Taniguchi, Eitaro; Itou, Minoru; Oriishi, Tetsuharu; Iwasaki, Shoko; Miura, Hiroko; Torimura, Takuji

2016-05-01

The incidence of traffic accidents in patients with chronic liver disease (CLD) is high in the USA. However, the characteristics of patients, including dietary habits, differ between Japan and the USA. The present study investigated the incidence of traffic accidents in CLD patients and the clinical profiles associated with traffic accidents in Japan using a data-mining analysis. A cross-sectional study was performed and 256 subjects [148 CLD patients (CLD group) and 106 patients with other digestive diseases (disease control group)] were enrolled; 2 patients were excluded. The incidence of traffic accidents was compared between the two groups. Independent factors for traffic accidents were analyzed using logistic regression and decision-tree analyses. The incidence of traffic accidents did not differ between the CLD and disease control groups (8.8 vs. 11.3%). The results of the logistic regression analysis showed that yoghurt consumption was the only independent risk factor for traffic accidents (odds ratio, 0.37; 95% confidence interval, 0.16-0.85; P=0.0197). Similarly, the results of the decision-tree analysis showed that yoghurt consumption was the initial divergence variable. In patients who consumed yoghurt habitually, the incidence of traffic accidents was 6.6%, while that in patients who did not consume yoghurt was 16.0%. CLD was not identified as an independent factor in the logistic regression and decision-tree analyses. In conclusion, the difference in the incidence of traffic accidents in Japan between the CLD and disease control groups was insignificant. Furthermore, yoghurt consumption was an independent negative risk factor for traffic accidents in patients with digestive diseases, including CLD.

Lithium and neuroleptics in combination: is there enhancement of neurotoxicity leading to permanent sequelae?

PubMed

Goldman, S A

1996-10-01

Neurotoxicity in relation to concomitant administration of lithium and neuroleptic drugs, particularly haloperidol, has been an ongoing issue. This study examined whether use of lithium with neuroleptic drugs enhances neurotoxicity leading to permanent sequelae. The Spontaneous Reporting System database of the United States Food and Drug Administration and extant literature were reviewed for spectrum cases of lithium/neuroleptic neurotoxicity. Groups taking lithium alone (Li), lithium/haloperidol (LiHal) and lithium/ nonhaloperidol neuroleptics (LiNeuro), each paired for recovery and sequelae, were established for 237 cases. Statistical analyses included pairwise comparisons of lithium levels using the Wilcoxon Rank Sum procedure and logistic regression to analyze the relationship between independent variables and development of sequelae. The Li and Li-Neuro groups showed significant statistical differences in median lithium levels between recovery and sequelae pairs, whereas the LiHal pair did not differ significantly. Lithium level was associated with sequelae development overall and within the Li and LiNeuro groups; no such association was evident in the LiHal group. On multivariable logistic regression analysis, lithium level and taking lithium/haloperidol were significant factors in the development of sequelae, with multiple possibly confounding factors (e.g., age, sex) not statistically significant. Multivariable logistic regression analyses with neuroleptic dose as five discrete dose ranges or actual dose did not show an association between development of sequelae and dose. Database limitations notwithstanding, the lack of apparent impact of serum lithium level on the development of sequelae in patients treated with haloperidol contrasts notably with results in the Li and LiNeuro groups. These findings may suggest a possible effect of pharmacodynamic factors in lithium/neuroleptic combination therapy.

Organizational Predictors and Use of Evidence-Based Practices in Adolescent Substance Abuse Treatment

PubMed Central

Paino, Maria; Aletraris, Lydia; Roman, Paul M.

2014-01-01

Background Adolescent substance abuse remains a significant problem in the United States, yet treatment centers do not always admit adolescent clients. In this article, we first determine the extent to which treatment is available for adolescents in general and whether or not adolescent-specific (segregated) tracks are offered. Second, we examine the organizational characteristics associated with adolescent treatment. Third, we illuminate how the adolescent caseload in a treatment center is related to offering evidence-based practices (EBPs). Methods Drawing upon a nationally representative sample of U.S. treatment programs, we use logistic regression to assess how organizational characteristics are associated with the provision of adolescent treatment. Using ordinal logistic regression, we analyze how the treatment center’s adolescent caseload and organizational characteristics affect the extent to which a treatment center offers medication-assisted treatment (MAT) and psychosocial treatment. Results Half (49.5%) of treatment programs admitted adolescents and 41.8% offered an adolescent-specific track. Findings from the logistic regression suggested several organizational characteristics that were significantly associated with treating adolescents and/or having an adolescent-only track. Our findings from the ordinal models indicated a negative relationship between the percent of adolescents in a treatment center and the extent of MAT, and a positive relationship between the percent of adolescent clients and the extent of psychosocial treatment offered. Conclusions This paper highlights organizational barriers to treatment entry for adolescents, who remain a small proportion of clients in treatment centers. When treatment centers serve adolescents, however, those adolescents are likely to receive care in adolescent-only tracks and/or services and in programs that offer several psychosocial EBPs. Finally, adolescents are less likely to receive treatment in centers that offer a variety of MAT. PMID:25257691

Landslide susceptibility mapping for a part of North Anatolian Fault Zone (Northeast Turkey) using logistic regression model

NASA Astrophysics Data System (ADS)

Demir, Gökhan; aytekin, mustafa; banu ikizler, sabriye; angın, zekai

2013-04-01

The North Anatolian Fault is know as one of the most active and destructive fault zone which produced many earthquakes with high magnitudes. Along this fault zone, the morphology and the lithological features are prone to landsliding. However, many earthquake induced landslides were recorded by several studies along this fault zone, and these landslides caused both injuiries and live losts. Therefore, a detailed landslide susceptibility assessment for this area is indispancable. In this context, a landslide susceptibility assessment for the 1445 km2 area in the Kelkit River valley a part of North Anatolian Fault zone (Eastern Black Sea region of Turkey) was intended with this study, and the results of this study are summarized here. For this purpose, geographical information system (GIS) and a bivariate statistical model were used. Initially, Landslide inventory maps are prepared by using landslide data determined by field surveys and landslide data taken from General Directorate of Mineral Research and Exploration. The landslide conditioning factors are considered to be lithology, slope gradient, slope aspect, topographical elevation, distance to streams, distance to roads and distance to faults, drainage density and fault density. ArcGIS package was used to manipulate and analyze all the collected data Logistic regression method was applied to create a landslide susceptibility map. Landslide susceptibility maps were divided into five susceptibility regions such as very low, low, moderate, high and very high. The result of the analysis was verified using the inventoried landslide locations and compared with the produced probability model. For this purpose, Area Under Curvature (AUC) approach was applied, and a AUC value was obtained. Based on this AUC value, the obtained landslide susceptibility map was concluded as satisfactory. Keywords: North Anatolian Fault Zone, Landslide susceptibility map, Geographical Information Systems, Logistic Regression Analysis.

Age-related risk factors with nonfatal traffic accidents in urban areas in Maringá, Paraná, Brazil.

PubMed

de Melo, Willian Augusto; Alarcão, Ana Carolina Jacinto; de Oliveira, Analice Paula Rocha; Pelloso, Sandra Marisa; Carvalho, Maria Dalva de Barros

2017-02-17

The present study aimed to analyze the factors associated with the occurrence of nonfatal traffic accidents regarding age. A retrospective, transversal, and analytical study was carried out in the municipality of Maringá, Paraná, Brazil, based on data from Boletins de Ocorrência de Acidente de Trânsito ("Police Occurrence Bulletins"; BOATs). Following probability sampling, the sociodemographic aspects, logistics, environmental conditions, and time of occurrence of 418 cases of accidents were analyzed. The age of the victims was considered to be the dependent variable. The data were analyzed using descriptive statistics and bivariate, multivariate, and variance analysis, considering a confidence interval of 95% and a significance level of 5% (P <.05). Results revealed that young people (15-29 years) were twice as likely to be hospitalized due to severe injuries. Young motorcyclists had a 2.5 times greater chance of suffering accidents (P <.001); the use of other vehicles such as cars, bicycles, buses, and trucks represented a protective factor for this group (P <.05). Multiple logistic regression revealed that the main predictors for the occurrence of accidents were being single, having over 8 years of education, having had a driver's license for less than 3 years, roads with low luminosity, and driving at night. Demographic, environmental, and logistical factors were associated with morbidity due to traffic accidents among young people. These results challenge society and policy makers to create more effective strategies to minimize this serious public health problem.

What are hierarchical models and how do we analyze them?

USGS Publications Warehouse

Royle, Andy

2016-01-01

In this chapter we provide a basic definition of hierarchical models and introduce the two canonical hierarchical models in this book: site occupancy and N-mixture models. The former is a hierarchical extension of logistic regression and the latter is a hierarchical extension of Poisson regression. We introduce basic concepts of probability modeling and statistical inference including likelihood and Bayesian perspectives. We go through the mechanics of maximizing the likelihood and characterizing the posterior distribution by Markov chain Monte Carlo (MCMC) methods. We give a general perspective on topics such as model selection and assessment of model fit, although we demonstrate these topics in practice in later chapters (especially Chapters 5, 6, 7, and 10 Chapter 5 Chapter 6 Chapter 7 Chapter 10)

Clustering performance comparison using K-means and expectation maximization algorithms.

PubMed

Jung, Yong Gyu; Kang, Min Soo; Heo, Jun

2014-11-14

Clustering is an important means of data mining based on separating data categories by similar features. Unlike the classification algorithm, clustering belongs to the unsupervised type of algorithms. Two representatives of the clustering algorithms are the K -means and the expectation maximization (EM) algorithm. Linear regression analysis was extended to the category-type dependent variable, while logistic regression was achieved using a linear combination of independent variables. To predict the possibility of occurrence of an event, a statistical approach is used. However, the classification of all data by means of logistic regression analysis cannot guarantee the accuracy of the results. In this paper, the logistic regression analysis is applied to EM clusters and the K -means clustering method for quality assessment of red wine, and a method is proposed for ensuring the accuracy of the classification results.

Racial/ethnic and educational differences in the estimated odds of recent nitrite use among adult household residents in the United States: an illustration of matching and conditional logistic regression.

PubMed

Delva, J; Spencer, M S; Lin, J K

2000-01-01

This article compares estimates of the relative odds of nitrite use obtained from weighted unconditional logistic regression with estimates obtained from conditional logistic regression after post-stratification and matching of cases with controls by neighborhood of residence. We illustrate these methods by comparing the odds associated with nitrite use among adults of four racial/ethnic groups, with and without a high school education. We used aggregated data from the 1994-B through 1996 National Household Survey on Drug Abuse (NHSDA). Difference between the methods and implications for analysis and inference are discussed.

Strategies for Testing Statistical and Practical Significance in Detecting DIF with Logistic Regression Models

ERIC Educational Resources Information Center

Fidalgo, Angel M.; Alavi, Seyed Mohammad; Amirian, Seyed Mohammad Reza

2014-01-01

This study examines three controversial aspects in differential item functioning (DIF) detection by logistic regression (LR) models: first, the relative effectiveness of different analytical strategies for detecting DIF; second, the suitability of the Wald statistic for determining the statistical significance of the parameters of interest; and…

Iterative Purification and Effect Size Use with Logistic Regression for Differential Item Functioning Detection

ERIC Educational Resources Information Center

French, Brian F.; Maller, Susan J.

2007-01-01

Two unresolved implementation issues with logistic regression (LR) for differential item functioning (DIF) detection include ability purification and effect size use. Purification is suggested to control inaccuracies in DIF detection as a result of DIF items in the ability estimate. Additionally, effect size use may be beneficial in controlling…

A Note on Three Statistical Tests in the Logistic Regression DIF Procedure

ERIC Educational Resources Information Center

Paek, Insu

2012-01-01

Although logistic regression became one of the well-known methods in detecting differential item functioning (DIF), its three statistical tests, the Wald, likelihood ratio (LR), and score tests, which are readily available under the maximum likelihood, do not seem to be consistently distinguished in DIF literature. This paper provides a clarifying…

"Let Me Count the Ways:" Fostering Reasons for Living among Low-Income, Suicidal, African American Women

ERIC Educational Resources Information Center

West, Lindsey M.; Davis, Telsie A.; Thompson, Martie P.; Kaslow, Nadine J.

2011-01-01

Protective factors for fostering reasons for living were examined among low-income, suicidal, African American women. Bivariate logistic regressions revealed that higher levels of optimism, spiritual well-being, and family social support predicted reasons for living. Multivariate logistic regressions indicated that spiritual well-being showed…

Comparison of Two Approaches for Handling Missing Covariates in Logistic Regression

ERIC Educational Resources Information Center

Peng, Chao-Ying Joanne; Zhu, Jin

2008-01-01

For the past 25 years, methodological advances have been made in missing data treatment. Most published work has focused on missing data in dependent variables under various conditions. The present study seeks to fill the void by comparing two approaches for handling missing data in categorical covariates in logistic regression: the…

Comparison of IRT Likelihood Ratio Test and Logistic Regression DIF Detection Procedures

ERIC Educational Resources Information Center

Atar, Burcu; Kamata, Akihito

2011-01-01

The Type I error rates and the power of IRT likelihood ratio test and cumulative logit ordinal logistic regression procedures in detecting differential item functioning (DIF) for polytomously scored items were investigated in this Monte Carlo simulation study. For this purpose, 54 simulation conditions (combinations of 3 sample sizes, 2 sample…

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…

Modeling Polytomous Item Responses Using Simultaneously Estimated Multinomial Logistic Regression Models

ERIC Educational Resources Information Center

Anderson, Carolyn J.; Verkuilen, Jay; Peyton, Buddy L.

2010-01-01

Survey items with multiple response categories and multiple-choice test questions are ubiquitous in psychological and educational research. We illustrate the use of log-multiplicative association (LMA) models that are extensions of the well-known multinomial logistic regression model for multiple dependent outcome variables to reanalyze a set of…

Propensity Score Estimation with Data Mining Techniques: Alternatives to Logistic Regression

ERIC Educational Resources Information Center

Keller, Bryan S. B.; Kim, Jee-Seon; Steiner, Peter M.

2013-01-01

Propensity score analysis (PSA) is a methodological technique which may correct for selection bias in a quasi-experiment by modeling the selection process using observed covariates. Because logistic regression is well understood by researchers in a variety of fields and easy to implement in a number of popular software packages, it has…

Two-factor logistic regression in pediatric liver transplantation

NASA Astrophysics Data System (ADS)

Uzunova, Yordanka; Prodanova, Krasimira; Spasov, Lyubomir

2017-12-01

Using a two-factor logistic regression analysis an estimate is derived for the probability of absence of infections in the early postoperative period after pediatric liver transplantation. The influence of both the bilirubin level and the international normalized ratio of prothrombin time of blood coagulation at the 5th postoperative day is studied.

Predictors of Placement Stability at the State Level: The Use of Logistic Regression to Inform Practice

ERIC Educational Resources Information Center

Courtney, Jon R.; Prophet, Retta

2011-01-01

Placement instability is often associated with a number of negative outcomes for children. To gain state level contextual knowledge of factors associated with placement stability/instability, logistic regression was applied to selected variables from the New Mexico Adoption and Foster Care Administrative Reporting System dataset. Predictors…

Classifying machinery condition using oil samples and binary logistic regression

NASA Astrophysics Data System (ADS)

Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.

2015-08-01

The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.

Length bias correction in gene ontology enrichment analysis using logistic regression.

PubMed

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.

Matched samples logistic regression in case-control studies with missing values: when to break the matches.

PubMed

Hansson, Lisbeth; Khamis, Harry J

2008-12-01

Simulated data sets are used to evaluate conditional and unconditional maximum likelihood estimation in an individual case-control design with continuous covariates when there are different rates of excluded cases and different levels of other design parameters. The effectiveness of the estimation procedures is measured by method bias, variance of the estimators, root mean square error (RMSE) for logistic regression and the percentage of explained variation. Conditional estimation leads to higher RMSE than unconditional estimation in the presence of missing observations, especially for 1:1 matching. The RMSE is higher for the smaller stratum size, especially for the 1:1 matching. The percentage of explained variation appears to be insensitive to missing data, but is generally higher for the conditional estimation than for the unconditional estimation. It is particularly good for the 1:2 matching design. For minimizing RMSE, a high matching ratio is recommended; in this case, conditional and unconditional logistic regression models yield comparable levels of effectiveness. For maximizing the percentage of explained variation, the 1:2 matching design with the conditional logistic regression model is recommended.

Label-noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study.

PubMed

Lee, Seokho; Shin, Hyejin; Lee, Sang Han

2016-12-01

Alzheimer's disease (AD) is usually diagnosed by clinicians through cognitive and functional performance test with a potential risk of misdiagnosis. Since the progression of AD is known to cause structural changes in the corpus callosum (CC), the CC thickness can be used as a functional covariate in AD classification problem for a diagnosis. However, misclassified class labels negatively impact the classification performance. Motivated by AD-CC association studies, we propose a logistic regression for functional data classification that is robust to misdiagnosis or label noise. Specifically, our logistic regression model is constructed by adopting individual intercepts to functional logistic regression model. This approach enables to indicate which observations are possibly mislabeled and also lead to a robust and efficient classifier. An effective algorithm using MM algorithm provides simple closed-form update formulas. We test our method using synthetic datasets to demonstrate its superiority over an existing method, and apply it to differentiating patients with AD from healthy normals based on CC from MRI. © 2016, The International Biometric Society.

The Effect of Latent Binary Variables on the Uncertainty of the Prediction of a Dichotomous Outcome Using Logistic Regression Based Propensity Score Matching.

PubMed

Szekér, Szabolcs; Vathy-Fogarassy, Ágnes

2018-01-01

Logistic regression based propensity score matching is a widely used method in case-control studies to select the individuals of the control group. This method creates a suitable control group if all factors affecting the output variable are known. However, if relevant latent variables exist as well, which are not taken into account during the calculations, the quality of the control group is uncertain. In this paper, we present a statistics-based research in which we try to determine the relationship between the accuracy of the logistic regression model and the uncertainty of the dependent variable of the control group defined by propensity score matching. Our analyses show that there is a linear correlation between the fit of the logistic regression model and the uncertainty of the output variable. In certain cases, a latent binary explanatory variable can result in a relative error of up to 70% in the prediction of the outcome variable. The observed phenomenon calls the attention of analysts to an important point, which must be taken into account when deducting conclusions.

Logistic regression for circular data

NASA Astrophysics Data System (ADS)

Al-Daffaie, Kadhem; Khan, Shahjahan

2017-05-01

This paper considers the relationship between a binary response and a circular predictor. It develops the logistic regression model by employing the linear-circular regression approach. The maximum likelihood method is used to estimate the parameters. The Newton-Raphson numerical method is used to find the estimated values of the parameters. A data set from weather records of Toowoomba city is analysed by the proposed methods. Moreover, a simulation study is considered. The R software is used for all computations and simulations.

Naval Research Logistics Quarterly. Volume 28. Number 3,

DTIC Science & Technology

1981-09-01

denotes component-wise maximum. f has antone (isotone) differences on C x D if for cl < c2 and d, < d2, NAVAL RESEARCH LOGISTICS QUARTERLY VOL. 28...or negative correlations and linear or nonlinear regressions. Given are the mo- ments to order two and, for special cases, (he regression function and...data sets. We designate this bnb distribution as G - B - N(a, 0, v). The distribution admits only of positive correlation and linear regressions

Regression approaches in the test-negative study design for assessment of influenza vaccine effectiveness.

PubMed

Bond, H S; Sullivan, S G; Cowling, B J

2016-06-01

Influenza vaccination is the most practical means available for preventing influenza virus infection and is widely used in many countries. Because vaccine components and circulating strains frequently change, it is important to continually monitor vaccine effectiveness (VE). The test-negative design is frequently used to estimate VE. In this design, patients meeting the same clinical case definition are recruited and tested for influenza; those who test positive are the cases and those who test negative form the comparison group. When determining VE in these studies, the typical approach has been to use logistic regression, adjusting for potential confounders. Because vaccine coverage and influenza incidence change throughout the season, time is included among these confounders. While most studies use unconditional logistic regression, adjusting for time, an alternative approach is to use conditional logistic regression, matching on time. Here, we used simulation data to examine the potential for both regression approaches to permit accurate and robust estimates of VE. In situations where vaccine coverage changed during the influenza season, the conditional model and unconditional models adjusting for categorical week and using a spline function for week provided more accurate estimates. We illustrated the two approaches on data from a test-negative study of influenza VE against hospitalization in children in Hong Kong which resulted in the conditional logistic regression model providing the best fit to the data.

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network

PubMed Central

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins. PMID:27418910

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network.

PubMed

Asghari, Mehdi Poursheikhali; Hayatshahi, Sayyed Hamed Sadat; Abdolmaleki, Parviz

2012-01-01

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Sequence parameters were consisted of 80 amino acid positional occurrences and 20 amino acid percentages in sequence. Among these parameters, the most significant ones which were selected by binary logistic regression model, were percentages of Gly, Ser and the occurrence of Asn in position i+2, respectively, in sequence. These significant parameters have the highest effect on the constitution of a β-turn sequence. A neural network model was then constructed and fed by the parameters selected by binary logistic regression to build a hybrid predictor. The networks have been trained and tested on a non-homologous dataset of 565 protein chains. With applying a nine fold cross-validation test on the dataset, the network reached an overall accuracy (Qtotal) of 74, which is comparable with results of the other β-turn prediction methods. In conclusion, this study proves that the parameter selection ability of binary logistic regression together with the prediction capability of neural networks lead to the development of more precise models for identifying β-turns in proteins.

Differential item functioning analysis with ordinal logistic regression techniques. DIFdetect and difwithpar.

PubMed

Crane, Paul K; Gibbons, Laura E; Jolley, Lance; van Belle, Gerald

2006-11-01

We present an ordinal logistic regression model for identification of items with differential item functioning (DIF) and apply this model to a Mini-Mental State Examination (MMSE) dataset. We employ item response theory ability estimation in our models. Three nested ordinal logistic regression models are applied to each item. Model testing begins with examination of the statistical significance of the interaction term between ability and the group indicator, consistent with nonuniform DIF. Then we turn our attention to the coefficient of the ability term in models with and without the group term. If including the group term has a marked effect on that coefficient, we declare that it has uniform DIF. We examined DIF related to language of test administration in addition to self-reported race, Hispanic ethnicity, age, years of education, and sex. We used PARSCALE for IRT analyses and STATA for ordinal logistic regression approaches. We used an iterative technique for adjusting IRT ability estimates on the basis of DIF findings. Five items were found to have DIF related to language. These same items also had DIF related to other covariates. The ordinal logistic regression approach to DIF detection, when combined with IRT ability estimates, provides a reasonable alternative for DIF detection. There appear to be several items with significant DIF related to language of test administration in the MMSE. More attention needs to be paid to the specific criteria used to determine whether an item has DIF, not just the technique used to identify DIF.

Use of generalized ordered logistic regression for the analysis of multidrug resistance data.

PubMed

Agga, Getahun E; Scott, H Morgan

2015-10-01

Statistical analysis of antimicrobial resistance data largely focuses on individual antimicrobial's binary outcome (susceptible or resistant). However, bacteria are becoming increasingly multidrug resistant (MDR). Statistical analysis of MDR data is mostly descriptive often with tabular or graphical presentations. Here we report the applicability of generalized ordinal logistic regression model for the analysis of MDR data. A total of 1,152 Escherichia coli, isolated from the feces of weaned pigs experimentally supplemented with chlortetracycline (CTC) and copper, were tested for susceptibilities against 15 antimicrobials and were binary classified into resistant or susceptible. The 15 antimicrobial agents tested were grouped into eight different antimicrobial classes. We defined MDR as the number of antimicrobial classes to which E. coli isolates were resistant ranging from 0 to 8. Proportionality of the odds assumption of the ordinal logistic regression model was violated only for the effect of treatment period (pre-treatment, during-treatment and post-treatment); but not for the effect of CTC or copper supplementation. Subsequently, a partially constrained generalized ordinal logistic model was built that allows for the effect of treatment period to vary while constraining the effects of treatment (CTC and copper supplementation) to be constant across the levels of MDR classes. Copper (Proportional Odds Ratio [Prop OR]=1.03; 95% CI=0.73-1.47) and CTC (Prop OR=1.1; 95% CI=0.78-1.56) supplementation were not significantly associated with the level of MDR adjusted for the effect of treatment period. MDR generally declined over the trial period. In conclusion, generalized ordered logistic regression can be used for the analysis of ordinal data such as MDR data when the proportionality assumptions for ordered logistic regression are violated. Published by Elsevier B.V.

[Biodiversity and depressive symptoms in Mexican adults: Exploration of beneficial environmental effects].

PubMed

Duarte-Tagles, Héctor; Salinas-Rodríguez, Aarón; Idrovo, Álvaro J; Búrquez, Alberto; Corral-Verdugo, Víctor

2015-08-01

Depression is a highly prevalent illness among adults, and it is the second most frequently reported mental disorder in urban settings in México. Exposure to natural environments and its components may improve the mental health of the population. To evaluate the association between biodiversity indicators and the prevalence of depressive symptoms among the adult population (20 to 65 years of age) in México. Information from the Encuesta Nacional de Salud y Nutrición 2006 (ENSANUT 2006) and the Compendio de Estadísticas Ambientales 2008 was analyzed. A biodiversity index was constructed based on the species richness and ecoregions in each state. A multilevel logistic regression model was built with random intercepts and a multiple logistic regression was generated with clustering by state. The factors associated with depressive symptoms were being female, self-perceived as indigenous, lower education level, not living with a partner, lack of steady paid work, having a chronic illness and drinking alcohol. The biodiversity index was found to be inversely associated with the prevalence of depressive symptoms when defined as a continuous variable, and the results from the regression were grouped by state (OR=0.71; 95% CI = 0.59-0.87). Although the design was cross-sectional, this study adds to the evidence of the potential benefits to mental health from contact with nature and its components.

Prevalence and correlates of cognitive impairment in kidney transplant recipients.

PubMed

Gupta, Aditi; Mahnken, Jonathan D; Johnson, David K; Thomas, Tashra S; Subramaniam, Dipti; Polshak, Tyler; Gani, Imran; John Chen, G; Burns, Jeffrey M; Sarnak, Mark J

2017-05-12

There is a high prevalence of cognitive impairment in dialysis patients. The prevalence of cognitive impairment after kidney transplantation is unknown. Study Design: Cross-sectional study. Single center study of prevalent kidney transplant recipients from a transplant clinic in a large academic center. Assessment of cognition using the Montreal Cognitive Assessment (MoCA). Demographic and clinical variables associated with cognitive impairment were also examined. Outcomes and Measurements: a) Prevalence of cognitive impairment defined by a MoCA score of <26. b) Multivariable linear and logistic regression to examine the association of demographic and clinical factors with cognitive impairment. Data from 226 patients were analyzed. Mean (SD) age was 54 (13.4) years, 73% were white, 60% were male, 37% had diabetes, 58% had an education level of college or above, and the mean (SD) time since kidney transplant was 3.4 (4.1) years. The prevalence of cognitive impairment was 58.0%. Multivariable linear regression demonstrated that older age, male gender and absence of diabetes were associated with lower MoCA scores (p < 0.01 for all). Estimated glomerular filtration rate (eGFR) was not associated with level of cognition. The logistic regression analysis confirmed the association of older age with cognitive impairment. Cognitive impairment is common in prevalent kidney transplant recipients, at a younger age compared to general population, and is associated with certain demographic variables, but not level of eGFR.

Amniotic fluid MMP-9 and neurotrophins in autism spectrum disorders: an exploratory study.

PubMed

Abdallah, Morsi W; Pearce, Brad D; Larsen, Nanna; Greaves-Lord, Kirstin; Nørgaard-Pedersen, Bent; Hougaard, David M; Mortensen, Erik L; Grove, Jakob

2012-12-01

Evidence suggests that some developmental disorders, such as autism spectrum disorders (ASDs), are caused by errors in brain plasticity. Given the important role of matrix metalloproteinases (MMPs) and neurotrophins (NTs) in neuroplasticity, amniotic fluid samples for 331 ASD cases and 698 frequency-matched controls were analyzed for levels of MMP-9, brain-derived neurotrophic factor, NT-4 and transforming growth factor-β utilizing a Danish historic birth cohort and Danish nationwide health registers. Laboratory measurements were performed using an in-house multiplex sandwich immunoassay Luminex xMAP method, and measurements were analyzed using tobit and logistic regression. Results showed elevated levels of MMP-9 in ASD cases compared with controls (crude and adjusted tobit regression P-values: 0.01 and 0.06). Our results highlight the importance of exploring the biologic impact of MMP-9 and potential therapeutic roles of its inhibitors in ASD and may indicate that neuroplastic impairments in ASD may present during pregnancy. © 2012 International Society for Autism Research, Wiley Periodicals, Inc.

Artificial neural networks predict the incidence of portosplenomesenteric venous thrombosis in patients with acute pancreatitis.

PubMed

Fei, Y; Hu, J; Li, W-Q; Wang, W; Zong, G-Q

2017-03-01

Essentials Predicting the occurrence of portosplenomesenteric vein thrombosis (PSMVT) is difficult. We studied 72 patients with acute pancreatitis. Artificial neural networks modeling was more accurate than logistic regression in predicting PSMVT. Additional predictive factors may be incorporated into artificial neural networks. Objective To construct and validate artificial neural networks (ANNs) for predicting the occurrence of portosplenomesenteric venous thrombosis (PSMVT) and compare the predictive ability of the ANNs with that of logistic regression. Methods The ANNs and logistic regression modeling were constructed using simple clinical and laboratory data of 72 acute pancreatitis (AP) patients. The ANNs and logistic modeling were first trained on 48 randomly chosen patients and validated on the remaining 24 patients. The accuracy and the performance characteristics were compared between these two approaches by SPSS17.0 software. Results The training set and validation set did not differ on any of the 11 variables. After training, the back propagation network training error converged to 1 × 10 -20 , and it retained excellent pattern recognition ability. When the ANNs model was applied to the validation set, it revealed a sensitivity of 80%, specificity of 85.7%, a positive predictive value of 77.6% and negative predictive value of 90.7%. The accuracy was 83.3%. Differences could be found between ANNs modeling and logistic regression modeling in these parameters (10.0% [95% CI, -14.3 to 34.3%], 14.3% [95% CI, -8.6 to 37.2%], 15.7% [95% CI, -9.9 to 41.3%], 11.8% [95% CI, -8.2 to 31.8%], 22.6% [95% CI, -1.9 to 47.1%], respectively). When ANNs modeling was used to identify PSMVT, the area under receiver operating characteristic curve was 0.849 (95% CI, 0.807-0.901), which demonstrated better overall properties than logistic regression modeling (AUC = 0.716) (95% CI, 0.679-0.761). Conclusions ANNs modeling was a more accurate tool than logistic regression in predicting the occurrence of PSMVT following AP. More clinical factors or biomarkers may be incorporated into ANNs modeling to improve its predictive ability. © 2016 International Society on Thrombosis and Haemostasis.

PREDICTION OF MALIGNANT BREAST LESIONS FROM MRI FEATURES: A COMPARISON OF ARTIFICIAL NEURAL NETWORK AND LOGISTIC REGRESSION TECHNIQUES

PubMed Central

McLaren, Christine E.; Chen, Wen-Pin; Nie, Ke; Su, Min-Ying

2009-01-01

Rationale and Objectives Dynamic contrast enhanced MRI (DCE-MRI) is a clinical imaging modality for detection and diagnosis of breast lesions. Analytical methods were compared for diagnostic feature selection and performance of lesion classification to differentiate between malignant and benign lesions in patients. Materials and Methods The study included 43 malignant and 28 benign histologically-proven lesions. Eight morphological parameters, ten gray level co-occurrence matrices (GLCM) texture features, and fourteen Laws’ texture features were obtained using automated lesion segmentation and quantitative feature extraction. Artificial neural network (ANN) and logistic regression analysis were compared for selection of the best predictors of malignant lesions among the normalized features. Results Using ANN, the final four selected features were compactness, energy, homogeneity, and Law_LS, with area under the receiver operating characteristic curve (AUC) = 0.82, and accuracy = 0.76. The diagnostic performance of these 4-features computed on the basis of logistic regression yielded AUC = 0.80 (95% CI, 0.688 to 0.905), similar to that of ANN. The analysis also shows that the odds of a malignant lesion decreased by 48% (95% CI, 25% to 92%) for every increase of 1 SD in the Law_LS feature, adjusted for differences in compactness, energy, and homogeneity. Using logistic regression with z-score transformation, a model comprised of compactness, NRL entropy, and gray level sum average was selected, and it had the highest overall accuracy of 0.75 among all models, with AUC = 0.77 (95% CI, 0.660 to 0.880). When logistic modeling of transformations using the Box-Cox method was performed, the most parsimonious model with predictors, compactness and Law_LS, had an AUC of 0.79 (95% CI, 0.672 to 0.898). Conclusion The diagnostic performance of models selected by ANN and logistic regression was similar. The analytic methods were found to be roughly equivalent in terms of predictive ability when a small number of variables were chosen. The robust ANN methodology utilizes a sophisticated non-linear model, while logistic regression analysis provides insightful information to enhance interpretation of the model features. PMID:19409817

Logistic regression analysis of factors associated with avascular necrosis of the femoral head following femoral neck fractures in middle-aged and elderly patients.

PubMed

Ai, Zi-Sheng; Gao, You-Shui; Sun, Yuan; Liu, Yue; Zhang, Chang-Qing; Jiang, Cheng-Hua

2013-03-01

Risk factors for femoral neck fracture-induced avascular necrosis of the femoral head have not been elucidated clearly in middle-aged and elderly patients. Moreover, the high incidence of screw removal in China and its effect on the fate of the involved femoral head require statistical methods to reflect their intrinsic relationship. Ninety-nine patients older than 45 years with femoral neck fracture were treated by internal fixation between May 1999 and April 2004. Descriptive analysis, interaction analysis between associated factors, single factor logistic regression, multivariate logistic regression, and detailed interaction analysis were employed to explore potential relationships among associated factors. Avascular necrosis of the femoral head was found in 15 cases (15.2 %). Age × the status of implants (removal vs. maintenance) and gender × the timing of reduction were interactive according to two-factor interactive analysis. Age, the displacement of fractures, the quality of reduction, and the status of implants were found to be significant factors in single factor logistic regression analysis. Age, age × the status of implants, and the quality of reduction were found to be significant factors in multivariate logistic regression analysis. In fine interaction analysis after multivariate logistic regression analysis, implant removal was the most important risk factor for avascular necrosis in 56-to-85-year-old patients, with a risk ratio of 26.00 (95 % CI = 3.076-219.747). The middle-aged and elderly have less incidence of avascular necrosis of the femoral head following femoral neck fractures treated by cannulated screws. The removal of cannulated screws can induce a significantly high incidence of avascular necrosis of the femoral head in elderly patients, while a high-quality reduction is helpful to reduce avascular necrosis.

Mortality in Children Under Five Receiving Nonphysician Clinician Emergency Care in Uganda.

PubMed

Rice, Brian; Periyanayagam, Usha; Chamberlain, Stacey; Dreifuss, Bradley; Hammerstedt, Heather; Nelson, Sara; Maling, Samuel; Bisanzo, Mark

2016-03-01

A nonphysician clinician (NPC) training program was started in Uganda in 2009. NPC care was initially supervised by a physician and subsequent care was independent. The mortality of children under 5 (U5) was analyzed to evaluate the impact of transitioning NPC care from physician-supervised to independent care. A retrospective review was performed of a quality assurance database including 3-day follow-up for all patients presenting to the emergency department (ED). Mortality rates were calculated and χ(2) tests used for significance of proportions. Multiple logistic regression was used to assess independent predictors of mortality. Overall, 68.8% of 4985 U5 patients were admitted and 28.6% were "severely ill." The overall mortality was significantly lower in physician-supervised versus independent NPC care (2.90% vs 5.04%, P = .05). No significant mortality difference was seen between supervised and unsupervised care (2.17% vs 3.01%, P = .43) for the majority of patients that were not severely ill. Severely ill patients analyzed separately showed a significant mortality difference (4.07% vs 10.3%, P = .01). Logistic regression revealed physician supervision significantly reduced mortality for patients overall (odds ratio = 0.52, P = .03), but not for nonseverely ill patients analyzed separately (odds ratio = 0.73, P = .47). Though physician supervision reduced mortality for the severely ill subset of patients, physicians are not available full-time in most EDs in Sub-Saharan Africa. Training NPCs in emergency care produced noninferior mortality outcomes for unsupervised NPC care compared with physician-supervised NPC care for the majority of U5 patients. Copyright © 2016 by the American Academy of Pediatrics.

Analysis of the relationship between interleukin polymorphisms within miRNA-binding regions and alcoholic liver disease.

PubMed

Novo-Veleiro, I; Cieza-Borrella, C; Pastor, I; González-Sarmiento, R; Laso, F-J; Marcos, M

2018-05-01

Alcohol consumption promotes inflammation through the Toll-like receptor 4 (TLR4)/nuclear factor (NF)-?B pathway, leading to organic damage. Some micro-RNA (miRNA) molecules modulate this inflammatory response by downregulating TLR4/NF-?B pathway mediators, like interleukins (ILs). Thus, polymorphisms within IL genes located near miRNA binding sites could modify the risk of ethanol-induced damage. The present study analyzed potential relationships between alcoholism or alcoholic liver disease (ALD) and IL12B 2124 G>T (rs1368439), IL16 5000 C>T (rs1131445), IL1R1 3114 C>T (rs3917328), and NFKB1 3400 A>G (rs4648143) polymorphisms. The study included 301 male alcoholic patients and 156 male healthy volunteers. Polymorphisms were genotyped using TaqMan ® PCR assays for allelic discrimination. Allele and genotype frequencies were compared between groups. Logistic regression analysis was performed to analyze the inheritance model. Analysis of the IL1R1 (rs3917328) polymorphism showed that the proportion of alleleT carriers (CT and TT genotypes) was higher in healthy controls (9.7%) than in alcoholic patients (6.5%; P=.042). However, multivariable logistic regression analyses did not yield a significant result. No differences between groups were found for other analyzed polymorphisms. Our study describes, for the first time, the expected frequencies of certain polymorphisms within miRNA-binding sites in alcoholic patients with and without ALD. Further studies should be developed to clarify the potential relevance of these polymorphisms in alcoholism and ALD development. Copyright © 2018 Elsevier España, S.L.U. and Sociedad Española de Medicina Interna (SEMI). All rights reserved.

Prevalence and associations of anisometropia with spherical ametropia, cylindrical power, age, and sex in refractive surgery candidates.

PubMed

Linke, Stephan J; Richard, Gisbert; Katz, Toam

2011-09-29

To analyze the prevalence and associations of anisometropia with spherical ametropia, astigmatism, age, and sex in a refractive surgery population. Medical records of 27,070 eyes of 13,535 refractive surgery candidates were reviewed. Anisometropia, defined as the absolute difference in mean spherical equivalent powers between right and left eyes, was analyzed for subjective (A(subj)) and cycloplegic refraction (A(cycl)). Correlations between anisometropia (>1 diopter) and spherical ametropia, cylindrical power, age, and sex, were analyzed using χ² and nonparametric Kruskal-Wallis or Mann-Whitney tests and binomial logistic regression analyses. Power vector analysis was applied for further analysis of cylindrical power. Prevalence of A(subj) was 18.5% and of A(cycl) was 19.3%. In hyperopes, logistic regression analysis revealed that only spherical refractive error (odds ratio [OR], 0.72) and age (OR, 0.97) were independently associated with anisometropia. A(subj) decreased with increasing spherical ametropia and advancing age. Cylindrical power and sex did not significantly affect A(subj). In myopes all explanatory variables (spherical power OR, 0.93; cylindrical power OR, 0.75; age OR, 1.02; sex OR, 0.8) were independently associated with anisometropia. Cylindrical power was most strongly associated with anisometropia. Advancing age and increasing spherical/cylindrical power correlated positively with increasing anisometropia in myopic subjects. Female sex was more closely associated with anisometropia. This large-scale retrospective analysis confirmed an independent association between anisometropia and both spherical ametropia and age in refractive surgery candidates. Notably, an inverse relationship between these parameters in hyperopes was observed. Cylindrical power and female sex were independently associated with anisometropia in myopes.

[Analyzing consumer preference by using the latest semantic model for verbal protocol].

PubMed

Tamari, Yuki; Takemura, Kazuhisa

2012-02-01

This paper examines consumers' preferences for competing brands by using a preference model of verbal protocols. Participants were 150 university students, who reported their opinions and feelings about McDonalds and Mos Burger (competing hamburger restaurants in Japan). Their verbal protocols were analyzed by using the singular value decomposition method, and the latent decision frames were estimated. The verbal protocols having a large value in the decision frames could be interpreted as showing attributes that consumers emphasize. Based on the estimated decision frames, we predicted consumers' preferences using the logistic regression analysis method. The results indicate that the decision frames projected from the verbal protocol data explained consumers' preferences effectively.

Breast cancer screening among shift workers: a nationwide population-based survey in Korea.

PubMed

Son, Heesook; Kang, Youngmi

2017-04-01

We aimed to examine the association between shift work types and participation in breast cancer screening (BCS) programs by comparing rates of participation for BCS among regular daytime workers and alternative shift workers using data from a nationally representative, population-based survey conducted in Korea. In addition, the results were analyzed according to sociodemographic factors, including occupation, education, income, private health insurance, age, and number of working hours a week. This secondary cross-sectional analysis used data from the 2012 Korean National Health and Nutritional Examination Survey. The target population included women aged ≥ 40 years who responded as to whether they had undergone BCS in the previous year. Accordingly, we analyzed survey data for a total of 1,193 women and used a multivariate logistic regression analysis to evaluate the differences in factors affecting BCS between regular daytime and alternative shift workers. A logistic regression analysis was performed considering private health insurance as a significant sociodemographic factor for BCS among regular daytime shift workers. In contrast, none of the tested variables could significantly predict adherence to BCS among alternative shift workers. The results of this study suggest the need for the development of comprehensive workplace breast cancer prevention programs by considering shift work types. More attention should be given to female workers with low education levels, those who are uninsured, and young workers to improve the participation rate for BCS at the workplace.

Radiographic assessment of lower third molar eruption in different anteroposterior skeletal patterns and age-related groups.

PubMed

Jakovljevic, Aleksandar; Lazic, Emira; Soldatovic, Ivan; Nedeljkovic, Nenad; Andric, Miroslav

2015-07-01

To analyze radiographic predictors for lower third molar eruption among subjects with different anteroposterior skeletal relations and of different age groups. In total, 300 lower third molars were recorded on diagnostic digital orthopantomograms (DPTs) and lateral cephalograms (LCs). The radiographs were grouped according to sagittal intermaxillary angle (ANB), subject age, and level of lower third molar eruption. The DPT was used to analyze retromolar space, mesiodistal crown width, space/width ratio, third and second molar angulation (α, γ), third molar inclination (β), and gonion angle. The LC was used to determine ANB, angles of maxillar and mandibular prognathism (SNA, SNB), mandibular plane angle (SN/MP), and mandibular lengths. A logistic regression model was created using the statistically significant predictors. The logistic regression analysis revealed a statistically significant impact of β angle and distance between gonion and gnathion (Go-Gn) on the level of lower third molar eruption (P < .001 and P < .015, respectively). The retromolar space was significantly increased in the adult subgroup for all skeletal classes. The lower third molar impaction rate was significantly higher in the adult subgroup with the Class II (62.3%) compared with Class III subjects (31.7%; P < .013). The most favorable values of linear and angular predictors of mandibular third molar eruption were measured in Class III subjects. For valid estimation of mandibular third molar eruption, certain linear and angular measures (β angle, Go-Gn), as well as the size of the retromolar space, need to be considered.

Does Influenza Vaccination Modify Influenza Severity? Data on Older Adults Hospitalized With Influenza During the 2012-2013 Season in the United States.

PubMed

Arriola, Carmen S; Anderson, Evan J; Baumbach, Joan; Bennett, Nancy; Bohm, Susan; Hill, Mary; Lindegren, Mary Lou; Lung, Krista; Meek, James; Mermel, Elizabeth; Miller, Lisa; Monroe, Maya L; Morin, Craig; Oni, Oluwakemi; Reingold, Arthur; Schaffner, William; Thomas, Ann; Zansky, Shelley M; Finelli, Lyn; Chaves, Sandra S

2015-10-15

Some studies suggest that influenza vaccination might be protective against severe influenza outcomes in vaccinated persons who become infected. We used data from a large surveillance network to further investigate the effect of influenza vaccination on influenza severity in adults aged ≥50 years who were hospitalized with laboratory-confirmed influenza. We analyzed influenza vaccination and influenza severity using Influenza Hospitalization Surveillance Network (FluSurv-NET) data for the 2012-2013 influenza season. Intensive care unit (ICU) admission, death, diagnosis of pneumonia, and hospital and ICU lengths of stay served as measures of disease severity. Data were analyzed by multivariable logistic regression, parametric survival models, and propensity score matching (PSM). Overall, no differences in severity were observed in the multivariable logistic regression model. Using PSM, adults aged 50-64 years (but not other age groups) who were vaccinated against influenza had a shorter length of ICU stay than those who were unvaccinated (hazard ratio for discharge, 1.84; 95% confidence interval, 1.12-3.01). Our findings show a modest effect of influenza vaccination on disease severity. Analysis of data from seasons with different predominant strains and higher estimates of vaccine effectiveness are needed. Published by Oxford University Press on behalf of the Infectious Diseases Society of America 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Serum metabolomics differentiating pancreatic cancer from new-onset diabetes

PubMed Central

He, Xiangyi; Zhong, Jie; Wang, Shuwei; Zhou, Yufen; Wang, Lei; Zhang, Yongping; Yuan, Yaozong

2017-01-01

To establish a screening strategy for pancreatic cancer (PC) based on new-onset diabetic mellitus (NO-DM), serum metabolomics analysis and a search for the metabolic pathways associated with PC related DM were performed. Serum samples from patients with NO-DM (n = 30) and patients with pancreatic cancer and NO-DM were examined by liquid chromatography-mass spectrometry. Data were analyzed using principal components analysis (PCA) and orthogonal projection to latent structures (OPLS) of the most significant metabolites. The diagnostic model was constructed using logistic regression analysis. Metabolic pathways were analyzed using the web-based tool MetPA. PC patients with NO-DM were older and had a lower BMI and shorter duration of DM than those with NO-DM. The metabolomic profiles of patients with PC and NO-DM were significantly different from those of patients with NO-DM in the PCA and OPLS models. Sixty two differential metabolites were identified by the OPLS model. The logistic regression model using a panel of two metabolites including N_Succinyl_L_diaminopimelic_acid and PE (18:2) had high sensitivity (93.3%) and specificity (93.1%) for PC. The top three metabolic pathways associated with PC related DM were valine, leucine and isoleucine biosynthesis and degradation, primary bile acid biosynthesis, and sphingolipid metabolism. In conclusion, screening for PC based on NO-DM using serum metabolomics in combination with clinic characteristics and CA19-9 is a potential useful strategy. Several metabolic pathways differed between PC related DM and type 2 DM. PMID:28418859

Is participation in labour market programmes related to mental health? Results from a 14-year follow-up of the Northern Swedish Cohort.

PubMed

Reine, Ieva; Novo, Mehmed; Hammarström, Anne

2011-02-01

There is a lack of empirical studies assessing the possible impact of active labour market programmes (ALMP) on health. The aim of this study was to analyze whether participation in ALMP, in contrast to being unemployed and not participating in ALMP (UNALMP), was related to mental health at different ages. The study was carried out in a medium-sized industrial town in the north of Sweden. The cohort, consisting of all 1,083 pupils who attended or should have attended the last year of compulsory school in 1981, was followed up at the ages of 16, 18, 21 and 30. Data on 381 individuals at age 21, and 281 at age 30 were used in the study. The main health measurement was psychological symptoms among participants of ALMP in contrast to UNALMP at ages 21 and 30, and was analyzed by propensity score matching method (PSM) and multivariate logistic regression. Generally, ALMP had higher scores of psychological symptoms than UNALMP. Nevertheless, participation in ALMP was not related to mental health. Due to methodological shortages our results have to be interpreted with caution. Adjustment for either all background selection variables or the propensity score in multivariate logistic regression showed similar associations, suggesting that propensity score could be used to adjust for background selection variables. There is a need for more well-designed studies, using a theoretical framework, within the field, that are based on larger samples.

Work disability in adults with cystic fibrosis.

PubMed

Gillen, M; Lallas, D; Brown, C; Yelin, E; Blanc, P

1995-07-01

Greater numbers of persons with cystic fibrosis (CF) reach adulthood and, therefore, actively participate in the labor force. In this study, we estimated labor force participation rates and determined risk factors for work disability among persons with CF. We recruited 49 (73%) of 67 adults followed at one of two hospital-based CF centers. We ascertained employment history and CF-attributed work disability by structured questionnaire. Independently, we reviewed medical records for demographics and illness severity indicators. We analyzed potential risk factors for work disability by logistic regression analysis. All of those studied reported past or present labor force participation, consistent with high work motivation. Complete cessation of work attributed to CF was reported by 17 (35%; 95% CI, 21 to 49%). Although 23 (47%; 95% CI, 32 to 60%) of those surveyed stated that CF had affected career choice, only nine respondents had ever received career counselling and 16 had ever discussed job choice with their physicians. After adjusting for standard measures of disease severity by multiple logistic regression, age, adult diagnosis of CF, female gender, and single marital status, analyzed as a group, provided significant additional explanatory power to a predictive model of disability risk (model chi square [4 d.f.] = 11.5, p < 0.05). Health care professionals who design interventions targeted at work disability among persons with CF should address demographic factors as well as illness severity and should assess the vocational needs of persons with CF and the potential benefit of career counselling.

Serological Survey and Associated Risk Factors of Visceral Leish-maniasis in Qom Province, Central Iran.

PubMed

Rakhshanpour, Arash; Mohebali, Mehdi; Akhondi, Behnaz; Rahimi, Mohammad Taghi; Rokni, Mohammad Bagher

2014-01-01

Visceral leishmaniasis (VL) or kala-azar is considered as a parasitic disease caused by the species of Leishmania donovani complex which is intracellular parasites. This systemic disease is endemic in some parts of provinc-es of Iran. The aim of this study was to determine the seroprevalence of VL in Qom Province, central Iran using di-rect agglutination test (DAT). Overall, 1564 serum samples (800 males and 764 females) were collected from selected subjects by random-ized cluster sampling in 2011-2012. Sera were tested and analyzed by DAT. Before sampling; a questionnaire was filled out for each case. Data were analyzed using Chi-square test and multivariate logistic regression for risk factors analysis. Of 1564 individuals, 53 cases (3.38%) showed Leishmania specific antibodies as follows: with 1:400 titer 16 cases (1.02%), with 1:800 titer 20 cases (1.27%), with 1:1600 titer 16 cases (1.02%) whereas only one subject (0.06%) showed titers of ≥ 1:3200. There was no significant association between VL seropositivity and gender, age group and occupation. Binary logistic regression showed that rural areas was 0.44 times at higher risk of infection than urban areas (OR= 0.44; %95 CI= 0.25- 0.78). Although the seroprevalence of VL is relatively low in Qom Province, yet due to the importance of the disease, the surveillance system should be monitored by health authorities.

Serological Survey and Associated Risk Factors of Visceral Leish-maniasis in Qom Province, Central Iran

PubMed Central

RAKHSHANPOUR, Arash; MOHEBALI, Mehdi; AKHONDI, Behnaz; RAHIMI, Mohammad Taghi; ROKNI, Mohammad Bagher

2014-01-01

Abstract Background Visceral leishmaniasis (VL) or kala-azar is considered as a parasitic disease caused by the species of Leishmania donovani complex which is intracellular parasites. This systemic disease is endemic in some parts of provinc-es of Iran. The aim of this study was to determine the seroprevalence of VL in Qom Province, central Iran using di-rect agglutination test (DAT). Methods Overall, 1564 serum samples (800 males and 764 females) were collected from selected subjects by random-ized cluster sampling in 2011-2012. Sera were tested and analyzed by DAT. Before sampling; a questionnaire was filled out for each case. Data were analyzed using Chi-square test and multivariate logistic regression for risk factors analysis. Results Of 1564 individuals, 53 cases (3.38%) showed Leishmania specific antibodies as follows: with 1:400 titer 16 cases (1.02%), with 1:800 titer 20 cases (1.27%), with 1:1600 titer 16 cases (1.02%) whereas only one subject (0.06%) showed titers of ≥ 1:3200. There was no significant association between VL seropositivity and gender, age group and occupation. Binary logistic regression showed that rural areas was 0.44 times at higher risk of infection than urban areas (OR= 0.44; %95 CI= 0.25- 0.78). Conclusion Although the seroprevalence of VL is relatively low in Qom Province, yet due to the importance of the disease, the surveillance system should be monitored by health authorities. PMID:26060679

Relational coordination among home healthcare professions and goal attainment in nursing care.

PubMed

Sakai, Mahiro; Naruse, Takashi; Nagata, Satoko

2016-07-01

To examine whether interprofessional coordination is related to goal attainment in home visit nursing care. Self-administered questionnaire surveys were administered to home visit nursing agencies in Chiba Prefecture, Japan, from July to December 2014. Nurses evaluated their interprofessional coordination with professional groups (nursing colleague and managers, home doctors, care managers, home care workers, visiting therapists, day service and day care professionals, visiting bath professionals, and short stay professionals) using the Japanese version of the Relational Coordination Scale (RCS-J). Goal attainment across all clients during the most recent 3 months was measured with a rating scale ranging from incompletely attained (0) to completely attained (10). Data were analyzed with multivariate logistic regression analysis. A total of 83 nurses in 14 agencies responded, and data from 74 nurses were analyzed. The mean RCS-J and goal attainment scores were 3.59 (standard deviation = 0.47) and 6.51 (1.40), respectively. The RCS-J scores of the low and high goal attainment groups were 3.41 (0.46) and 3.73 (0.42), respectively. Multivariate logistic regression analysis revealed that RCS-J scores were positively associated with goal attainment (odds ratio, 5.71; 95% confidence interval, 1.65-19.79). The finding of this study suggest that well-coordinated professionals may fulfill client needs better than poorly coordinated professionals do. Future research is needed to determine whether similar results are obtained in individual clients using a well-validated goal attainment scale. © 2016 Japan Academy of Nursing Science.

Discharge Disposition After Stroke in Patients With Liver Disease.

PubMed

Parikh, Neal S; Merkler, Alexander E; Schneider, Yecheskel; Navi, Babak B; Kamel, Hooman

2017-02-01

Liver disease is associated with both hemorrhagic and thrombotic processes, including an elevated risk of intracranial hemorrhage. We sought to assess the relationship between liver disease and outcomes after stroke, as measured by discharge disposition. Using administrative claims data, we identified a cohort of patients hospitalized with stroke in California, Florida, and New York from 2005 to 2013. The predictor variable was liver disease. All diagnoses were defined using validated diagnosis codes. Ordinal logistic regression was used to analyze the association between liver disease and worsening discharge disposition: home, nursing/rehabilitation facility, or death. Secondarily, multiple logistic regression was used to analyze the association between liver disease and in-hospital mortality. Models were adjusted for demographics, vascular risk factors, and comorbidities. We identified 121 428 patients with intracerebral hemorrhage and 703 918 with ischemic stroke. Liver disease was documented in 13 584 patients (1.7%). Liver disease was associated with worse discharge disposition after both intracerebral hemorrhage (global odds ratio, 1.28; 95% confidence interval, 1.19-1.38) and ischemic stroke (odds ratio, 1.23; 95% confidence interval, 1.17-1.29). Similarly, liver disease was associated with in-hospital death after both intracerebral hemorrhage (odds ratio, 1.33; 95% confidence interval, 1.23-1.44) and ischemic stroke (odds ratio, 1.60; 95% confidence interval, 1.51-1.71). Liver disease was associated with worse hospital discharge disposition and in-hospital mortality after stroke, suggesting worse functional outcomes. © 2016 American Heart Association, Inc.

Prevalence of dry eye syndrome after a three-year exposure to a clean room.

PubMed

Cho, Hyun A; Cheon, Jae Jung; Lee, Jong Seok; Kim, Soo Young; Chang, Seong Sil

2014-01-01

To measure the prevalence of dry eye syndrome (DES) among clean room (relative humidity ≤1%) workers from 2011 to 2013. Three annual DES examinations were performed completely in 352 clean room workers aged 20-40 years who were working at a secondary battery factory. Each examination comprised the tear-film break-up test (TFBUT), Schirmer's test I, slit-lamp microscopic examination, and McMonnies questionnaire. DES grades were measured using the Delphi approach. The annual examination results were analyzed using a general linear model and post-hoc analysis with repeated-ANOVA (Tukey). Multiple logistic regression was performed using the examination results from 2013 (dependent variable) to analyze the effect of years spent working in the clean room (independent variable). The prevalence of DES among these workers was 14.8% in 2011, 27.1% in 2012, and 32.8% in 2013. The TFBUT and McMonnies questionnaire showed that DES grades worsened over time. Multiple logistic regression analysis indicated that the odds ratio for having dry eyes was 1.130 (95% CI 1.012-1.262) according to the findings of the McMonnies questionnaire. This 3-year trend suggests that the increased prevalence of DES was associated with longer working hours. To decrease the prevalence of DES, employees should be assigned reasonable working hours with shift assignments that include appropriate break times. Workers should also wear protective eyewear, subdivide their working process to minimize exposure, and utilize preservative-free eye drops.

The time dependent association of adrenaline administration and survival from out-of-hospital cardiac arrest.

PubMed

Ewy, Gordon A; Bobrow, Bentley J; Chikani, Vatsal; Sanders, Arthur B; Otto, Charles W; Spaite, Daniel W; Kern, Karl B

2015-11-01

Recommended for decades, the therapeutic value of adrenaline (epinephrine) in the resuscitation of patients with out-of-hospital cardiac arrest (OHCA) is controversial. To investigate the possible time-dependent outcomes associated with adrenaline administration by Emergency Medical Services personnel (EMS). A retrospective analysis of prospectively collected data from a near statewide cardiac resuscitation database between 1 January 2005 and 30 November 2013. Multivariable logistic regression was used to analyze the effect of the time interval between EMS dispatch and the initial dose of adrenaline on survival. The primary endpoints were survival to hospital discharge and favourable neurologic outcome. Data from 3469 patients with witnessed OHCA were analyzed. Their mean age was 66.3 years and 69% were male. An initially shockable rhythm was present in 41.8% of patients. Based on a multivariable logistic regression model with initial adrenaline administration time interval (AATI) from EMS dispatch as the covariate, survival was greatest when adrenaline was administered very early but decreased rapidly with increasing (AATI); odds ratio 0.94 (95% Confidence Interval (CI) 0.92-0.97). The AATI had no significant effect on good neurological outcome (OR=0.96, 95% CI=0.90-1.02). In patients with OHCA, survival to hospital discharge was greater in those treated early with adrenaline by EMS especially in the subset of patients with a shockable rhythm. However survival rapidly decreased with increasing adrenaline administration time intervals (AATI). Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Association between microbiological and serological prevalence of human pathogenic Yersinia spp. in pigs and pig batches.

PubMed

Vanantwerpen, Gerty; Berkvens, Dirk; De Zutter, Lieven; Houf, Kurt

2015-07-09

Pigs are the main reservoir of human pathogenic Y. enterocolitica, and the microbiological and serological prevalence of this pathogen differs between pig farms. The infection status of pig batches at moment of slaughter is unknown while it is a possibility to classify batches. A relation between the presence of human pathogenic Yersinia spp. and the presence of antibodies could help to predict the infection of the pigs prior to slaughter. Pigs from 100 different batches were sampled. Tonsils and pieces of diaphragm were collected from 7047 pigs (on average 70 pigs per batch). The tonsils were analyzed using a direct plating method and the meat juice collected from the pieces of diaphragm was analyzed by Enzyme Linked ImmunoSorbent Assay. The microbiological and serological results were compared using a mixed-effects logistic regression at pig and batch level. Yersinia spp. were found in 2031 (28.8%) pigs, antibodies were present in 4692 (66.6%) pigs. According to the logistic regression, there was no relation at pig level between the presence of Yersinia spp. in tonsils and the presence of antibodies. Contrarily, at batch level, a mean activity value of 37 Optical Density (OD)% indicated a Yersinia spp. positive farm and the microbiological prevalence in pig batches could be estimated before shipment to the slaughterhouse. This offers the opportunity to classify batches based on their potential risk to contaminate carcasses with human pathogenic Yersinia spp. Copyright © 2015 Elsevier B.V. All rights reserved.

Rank-Optimized Logistic Matrix Regression toward Improved Matrix Data Classification.

PubMed

Zhang, Jianguang; Jiang, Jianmin

2018-02-01

While existing logistic regression suffers from overfitting and often fails in considering structural information, we propose a novel matrix-based logistic regression to overcome the weakness. In the proposed method, 2D matrices are directly used to learn two groups of parameter vectors along each dimension without vectorization, which allows the proposed method to fully exploit the underlying structural information embedded inside the 2D matrices. Further, we add a joint [Formula: see text]-norm on two parameter matrices, which are organized by aligning each group of parameter vectors in columns. This added co-regularization term has two roles-enhancing the effect of regularization and optimizing the rank during the learning process. With our proposed fast iterative solution, we carried out extensive experiments. The results show that in comparison to both the traditional tensor-based methods and the vector-based regression methods, our proposed solution achieves better performance for matrix data classifications.

Elevated Fasting Blood Glucose Is Predictive of Poor Outcome in Non-Diabetic Stroke Patients: A Sub-Group Analysis of SMART.

PubMed

Yao, Ming; Ni, Jun; Zhou, Lixin; Peng, Bin; Zhu, Yicheng; Cui, Liying

2016-01-01

Although increasing evidence suggests that hyperglycemia following acute stroke adversely affects clinical outcome, whether the association between glycaemia and functional outcome varies between stroke patients with\\without pre-diagnosed diabetes remains controversial. We aimed to investigate the relationship between the fasting blood glucose (FBG) and the 6-month functional outcome in a subgroup of SMART cohort and further to assess whether this association varied based on the status of pre-diagnosed diabetes. Data of 2862 patients with acute ischemic stroke (629 with pre-diagnosed diabetics) enrolled from SMART cohort were analyzed. Functional outcome at 6-month post-stroke was measured by modified Rankin Scale (mRS) and categorized as favorable (mRS:0-2) or poor (mRS:3-5). Binary logistic regression model, adjusting for age, gender, educational level, history of hypertension and stroke, baseline NIHSS and treatment group, was used in the whole cohort to evaluate the association between admission FBG and functional outcome. Stratified logistic regression analyses were further performed based on the presence/absence of pre-diabetes history. In the whole cohort, multivariable logistical regression showed that poor functional outcome was associated with elevated FBG (OR1.21 (95%CI 1.07-1.37), p = 0.002), older age (OR1.64 (95% CI1.38-1.94), p<0.001), higher NIHSS (OR2.90 (95%CI 2.52-3.33), p<0.001) and hypertension (OR1.42 (95%CI 1.13-1.98), p = 0.04). Stratified logistical regression analysis showed that the association between FBG and functional outcome remained significant only in patients without pre-diagnosed diabetes (OR1.26 (95%CI 1.03-1.55), p = 0.023), but not in those with premorbid diagnosis of diabetes (p = 0.885). The present results demonstrate a significant association between elevated FBG after stroke and poor functional outcome in patients without pre-diagnosed diabetes, but not in diabetics. This finding confirms the importance of glycemic control during acute phase of ischemic stroke especially in patients without pre-diagnosed diabetes. Further investigation for developing optimal strategies to control blood glucose level in hyperglycemic setting is therefore of great importance. ClinicalTrials.gov NCT00664846.

Detecting DIF in Polytomous Items Using MACS, IRT and Ordinal Logistic Regression

ERIC Educational Resources Information Center

Elosua, Paula; Wells, Craig

2013-01-01

The purpose of the present study was to compare the Type I error rate and power of two model-based procedures, the mean and covariance structure model (MACS) and the item response theory (IRT), and an observed-score based procedure, ordinal logistic regression, for detecting differential item functioning (DIF) in polytomous items. A simulation…

Accuracy of Bayes and Logistic Regression Subscale Probabilities for Educational and Certification Tests

ERIC Educational Resources Information Center

Rudner, Lawrence

2016-01-01

In the machine learning literature, it is commonly accepted as fact that as calibration sample sizes increase, Naïve Bayes classifiers initially outperform Logistic Regression classifiers in terms of classification accuracy. Applied to subtests from an on-line final examination and from a highly regarded certification examination, this study shows…

Comparing Linear Discriminant Function with Logistic Regression for the Two-Group Classification Problem.

ERIC Educational Resources Information Center

Fan, Xitao; Wang, Lin

The Monte Carlo study compared the performance of predictive discriminant analysis (PDA) and that of logistic regression (LR) for the two-group classification problem. Prior probabilities were used for classification, but the cost of misclassification was assumed to be equal. The study used a fully crossed three-factor experimental design (with…

Effects of Social Class and School Conditions on Educational Enrollment and Achievement of Boys and Girls in Rural Viet Nam

ERIC Educational Resources Information Center

Nguyen, Phuong L.

2006-01-01

This study examines the effects of parental SES, school quality, and community factors on children's enrollment and achievement in rural areas in Viet Nam, using logistic regression and ordered logistic regression. Multivariate analysis reveals significant differences in educational enrollment and outcomes by level of household expenditures and…

School Exits in the Milwaukee Parental Choice Program: Evidence of a Marketplace?

ERIC Educational Resources Information Center

Ford, Michael

2011-01-01

This article examines whether the large number of school exits from the Milwaukee school voucher program is evidence of a marketplace. Two logistic regression and multinomial logistic regression models tested the relation between the inability to draw large numbers of voucher students and the ability for a private school to remain viable. Data on…

An empirical study of statistical properties of variance partition coefficients for multi-level logistic regression models

USGS Publications Warehouse

Li, Ji; Gray, B.R.; Bates, D.M.

2008-01-01

Partitioning the variance of a response by design levels is challenging for binomial and other discrete outcomes. Goldstein (2003) proposed four definitions for variance partitioning coefficients (VPC) under a two-level logistic regression model. In this study, we explicitly derived formulae for multi-level logistic regression model and subsequently studied the distributional properties of the calculated VPCs. Using simulations and a vegetation dataset, we demonstrated associations between different VPC definitions, the importance of methods for estimating VPCs (by comparing VPC obtained using Laplace and penalized quasilikehood methods), and bivariate dependence between VPCs calculated at different levels. Such an empirical study lends an immediate support to wider applications of VPC in scientific data analysis.

Model building strategy for logistic regression: purposeful selection.

PubMed

Zhang, Zhongheng

2016-03-01

Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.

[Calf circumference and its association with gait speed in elderly participants at Peruvian Naval Medical Center].

PubMed

Díaz Villegas, Gregory Mishell; Runzer Colmenares, Fernando

2015-01-01

To evaluate the association between calf circumference and gait speed in elderly patients 65 years or older at Geriatric day clinic at Peruvian Centro Médico Naval. Cross-sectional, retrospective study. We assessed 139 participants, 65 years or older at Peruvian Centro Médico Naval including calf circumference, gait speed and Short Physical Performance Battery. With bivariate analyses and logistic regression model we search for association between variables. The age mean was 79.37 years old (SD: 8.71). 59.71% were male, the 30.97% had a slow walking speed and the mean calf circumference was 33.42cm (SD: 5.61). After a bivariate analysis, we found a calf circumference mean of 30.35cm (SD: 3.74) in the slow speed group and, in normal gait group, a mean of 33.51cm (SD: 3.26) with significantly differences. We used logistic regression to analyze association with slow gait speed, founding statistically significant results adjusting model by disability and age. Low calf circumference is associated with slow speed walk in population over 65 years old. Copyright © 2014. Published by Elsevier Espana.

Sample size adjustments for varying cluster sizes in cluster randomized trials with binary outcomes analyzed with second-order PQL mixed logistic regression.

PubMed

Candel, Math J J M; Van Breukelen, Gerard J P

2010-06-30

Adjustments of sample size formulas are given for varying cluster sizes in cluster randomized trials with a binary outcome when testing the treatment effect with mixed effects logistic regression using second-order penalized quasi-likelihood estimation (PQL). Starting from first-order marginal quasi-likelihood (MQL) estimation of the treatment effect, the asymptotic relative efficiency of unequal versus equal cluster sizes is derived. A Monte Carlo simulation study shows this asymptotic relative efficiency to be rather accurate for realistic sample sizes, when employing second-order PQL. An approximate, simpler formula is presented to estimate the efficiency loss due to varying cluster sizes when planning a trial. In many cases sampling 14 per cent more clusters is sufficient to repair the efficiency loss due to varying cluster sizes. Since current closed-form formulas for sample size calculation are based on first-order MQL, planning a trial also requires a conversion factor to obtain the variance of the second-order PQL estimator. In a second Monte Carlo study, this conversion factor turned out to be 1.25 at most. (c) 2010 John Wiley & Sons, Ltd.

Organochlorine pesticides accumulation and breast cancer: A hospital-based case-control study.

PubMed

He, Ting-Ting; Zuo, An-Jun; Wang, Ji-Gang; Zhao, Peng

2017-05-01

The aim of this study is to detect the accumulation status of organochlorine pesticides in breast cancer patients and to explore the relationship between organochlorine pesticides contamination and breast cancer development. We conducted a hospital-based case-control study in 56 patients with breast cancer and 46 patients with benign breast disease. We detected the accumulation level of several organochlorine pesticides products (β-hexachlorocyclohexane, γ-hexachlorocyclohexane, polychlorinated biphenyls-28, polychlorinated biphenyls-52, pentachlorothioanisole, and pp'-dichlorodiphenyldichloroethane) in breast adipose tissues of all 102 patients using gas chromatography. Thereafter, we examined the expression status of estrogen receptor, progesterone receptor, human epidermal growth factor receptor-2 (HER2), and Ki-67 in 56 breast cancer cases by immunohistochemistry. In addition, we analyzed the risk of breast cancer in those patients with organochlorine pesticides contamination using a logistic regression model. Our data showed that breast cancer patients suffered high accumulation levels of pp'-dichlorodiphenyldichloroethane and polychlorinated biphenyls-52. However, the concentrations of pp'-dichlorodiphenyldichloroethane and polychlorinated biphenyls-52 were not related to clinicopathologic parameters of breast cancer. Further logistic regression analysis showed polychlorinated biphenyls-52 and pp'-dichlorodiphenyldichloroethane were risk factors for breast cancer. Our results provide new evidence on etiology of breast cancer.

Internal exposure levels of typical POPs and their associations with childhood asthma in Shanghai, China.

PubMed

Meng, Ge; Feng, Yan; Nie, Zhiqing; Wu, Xiaomeng; Wei, Hongying; Wu, Shaowei; Yin, Yong; Wang, Yan

2016-04-01

Polybrominated diphenyl ethers (PBDEs), polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) are common persistent organic pollutants (POPs) that may be associated with childhood asthma. The concentrations of PBDEs, PCBs and OCPs were analyzed in pooled serum samples from both asthmatic and non-asthmatic children. The differences in the internal exposure levels between the case and control groups were tested (p value <0.0012). The associations between the internal exposure concentrations of the POPs and childhood asthma were estimated based on the odds ratios (ORs) calculated using logistic regression models. There were significant differences in three PBDEs, 26 PCBs and seven OCPs between the two groups, with significantly higher levels in the cases. The multiple logistic regression models demonstrated that the internal exposure concentrations of a number of the POPs (23 PCBs, p,p'-DDE and α-HCH) were positively associated with childhood asthma. Some synergistic effects were observed when the children were co-exposed to the chemicals. BDE-209 was positively associated with asthma aggravation. This study indicates the potential relationships between the internal exposure concentrations of particular POPs and the development of childhood asthma. Copyright © 2015 Elsevier Inc. All rights reserved.

Socioeconomic factors affecting infant sleep-related deaths in St. Louis.

PubMed

Hogan, Cathy

2014-01-01

Though the Back to Sleep Campaign that began in 1994 caused an overall decrease in sudden infant death syndrome (SIDS) rates, racial disparity has continued to increase in St. Louis. Though researchers have analyzed and described various sociodemographic characteristics of SIDS and infant deaths by unintentional suffocation in St. Louis, they have not simultaneously controlled for contributory risk factors to racial disparity such as race, poverty, maternal education, and number of children born to each mother (parity). To determine whether there is a relationship between maternal socioeconomic factors and sleep-related infant death. This quantitative case-control study used secondary data collected by the Missouri Department of Health and Senior Services between 2005 and 2009. The sample includes matched birth/death certificates and living birth certificates of infants who were born/died within time frame. Descriptive analysis, Chi-square, and logistic regression. The controls were birth records of infants who lived more than 1 year. Chi-square and logistic regression analyses confirmed that race and poverty have significant relationships with infant sleep-related deaths. The social significance of this study is that the results may lead to population-specific modifications of prevention messages that will reduce infant sleep-related deaths. © 2013 Wiley Periodicals, Inc.

Factors associated with heavy alcohol use among students in Brazilian capitals.

PubMed

Galduróz, José Carlos F; Sanchez, Zila van der Meer; Opaleye, Emérita Sátiro; Noto, Ana Regina; Fonseca, Arilton Martins; Gomes, Paulo Leonardo Sirimarco; Carlini, Elisaldo Araújo

2010-04-01

To evaluate the association between heavy use of alcohol among students and family, personal and social factors. Cross-sectional study including public school students aged ten to 18 from 27 Brazilian capital cities in 2004. Data was collected using an anonymous, self-report questionnaire that was adapted from a World Health Organization instrument. A representative sample comprising 48,155 students was stratified by census tracts and clusters (schools). The associations between heavy alcohol use and the factors studied were analyzed using logistic regression at a 5% significance level. Of all students, 4,286 (8.9%) reported heavy alcohol use in the month prior to the interview. The logistic regression analysis showed an association between fair or poor relationship with the father (OR = 1.46) and the mother (OR = 1.61) and heavy use of alcohol. Following a religion (OR = 0.83) was inversely associated with heavy alcohol consumption. Sports practice and mother perceived as a 'liberal' person had no significance in the model. However, a higher prevalence of heavy use of alcohol was seen among working students. Stronger family ties and religion may help preventing alcohol abuse among students.

Supply and demand analysis for flood insurance by using logistic regression model: case study at Citarum watershed in South Bandung, West Java, Indonesia

NASA Astrophysics Data System (ADS)

Sidi, P.; Mamat, M.; Sukono; Supian, S.

2017-01-01

Floods have always occurred in the Citarum river basin. The adverse effects caused by floods can cover all their property, including the destruction of houses. The impact due to damage to residential buildings is usually not small. Indeed, each of flooding, the government and several social organizations providing funds to repair the building. But the donations are given very limited, so it cannot cover the entire cost of repair was necessary. The presence of insurance products for property damage caused by the floods is considered very important. However, if its presence is also considered necessary by the public or not? In this paper, the factors that affect the supply and demand of insurance product for damaged building due to floods are analyzed. The method used in this analysis is the ordinal logistic regression. Based on the analysis that the factors that affect the supply and demand of insurance product for damaged building due to floods, it is included: age, economic circumstances, family situations, insurance motivations, and lifestyle. Simultaneously that the factors affecting supply and demand of insurance product for damaged building due to floods mounted to 65.7%.

Factors Influencing Cecal Intubation Time during Retrograde Approach Single-Balloon Enteroscopy

PubMed Central

Chen, Peng-Jen; Shih, Yu-Lueng; Huang, Hsin-Hung; Hsieh, Tsai-Yuan

2014-01-01

Background and Aim. The predisposing factors for prolonged cecal intubation time (CIT) during colonoscopy have been well identified. However, the factors influencing CIT during retrograde SBE have not been addressed. The aim of this study was to determine the factors influencing CIT during retrograde SBE. Methods. We investigated patients who underwent retrograde SBE at a medical center from January 2011 to March 2014. The medical charts and SBE reports were reviewed. The patients' characteristics and procedure-associated data were recorded. These data were analyzed with univariate analysis as well as multivariate logistic regression analysis to identify the possible predisposing factors. Results. We enrolled 66 patients into this study. The median CIT was 17.4 minutes. With univariate analysis, there was no statistical difference in age, sex, BMI, or history of abdominal surgery, except for bowel preparation (P = 0.021). Multivariate logistic regression analysis showed that inadequate bowel preparation (odds ratio 30.2, 95% confidence interval 4.63–196.54; P < 0.001) was the independent predisposing factors for prolonged CIT during retrograde SBE. Conclusions. For experienced endoscopist, inadequate bowel preparation was the independent predisposing factor for prolonged CIT during retrograde SBE. PMID:25505904

Relation between serum creatinine and postoperative results of open-heart surgery.

PubMed

Ezeldin, Tamer H

2013-10-01

To determine the impact of preoperative serum creatinine level in non-dialyzable patients on postoperative morbidity and mortality. This is a prospective study, where serum creatinine was used to give primary assessment on renal function status preoperatively. This study includes 1,033 patients, who underwent coronary artery bypass grafting, or valve(s) operations. The study took place at Al-Hada Military Hospital, Taif, Kingdom of Saudi between May 2008 and January 2012. Data were statistically analyzed using Chi square (x2) test and multivariable logistic regression, to evaluate the postoperative morbidity and mortality risks associated with low serum creatinine levels. Postoperative mortality increased with high serum creatinine level >1.8 mg/dL (p

Laboratory test variables useful for distinguishing upper from lower gastrointestinal bleeding.

PubMed

Tomizawa, Minoru; Shinozaki, Fuminobu; Hasegawa, Rumiko; Shirai, Yoshinori; Motoyoshi, Yasufumi; Sugiyama, Takao; Yamamoto, Shigenori; Ishige, Naoki

2015-05-28

To distinguish upper from lower gastrointestinal (GI) bleeding. Patient records between April 2011 and March 2014 were analyzed retrospectively (3296 upper endoscopy, and 1520 colonoscopy). Seventy-six patients had upper GI bleeding (Upper group) and 65 had lower GI bleeding (Lower group). Variables were compared between the groups using one-way analysis of variance. Logistic regression was performed to identify variables significantly associated with the diagnosis of upper vs lower GI bleeding. Receiver-operator characteristic (ROC) analysis was performed to determine the threshold value that could distinguish upper from lower GI bleeding. Hemoglobin (P = 0.023), total protein (P = 0.0002), and lactate dehydrogenase (P = 0.009) were significantly lower in the Upper group than in the Lower group. Blood urea nitrogen (BUN) was higher in the Upper group than in the Lower group (P = 0.0065). Logistic regression analysis revealed that BUN was most strongly associated with the diagnosis of upper vs lower GI bleeding. ROC analysis revealed a threshold BUN value of 21.0 mg/dL, with a specificity of 93.0%. The threshold BUN value for distinguishing upper from lower GI bleeding was 21.0 mg/dL.

Laboratory test variables useful for distinguishing upper from lower gastrointestinal bleeding

PubMed Central

Tomizawa, Minoru; Shinozaki, Fuminobu; Hasegawa, Rumiko; Shirai, Yoshinori; Motoyoshi, Yasufumi; Sugiyama, Takao; Yamamoto, Shigenori; Ishige, Naoki

2015-01-01

AIM: To distinguish upper from lower gastrointestinal (GI) bleeding. METHODS: Patient records between April 2011 and March 2014 were analyzed retrospectively (3296 upper endoscopy, and 1520 colonoscopy). Seventy-six patients had upper GI bleeding (Upper group) and 65 had lower GI bleeding (Lower group). Variables were compared between the groups using one-way analysis of variance. Logistic regression was performed to identify variables significantly associated with the diagnosis of upper vs lower GI bleeding. Receiver-operator characteristic (ROC) analysis was performed to determine the threshold value that could distinguish upper from lower GI bleeding. RESULTS: Hemoglobin (P = 0.023), total protein (P = 0.0002), and lactate dehydrogenase (P = 0.009) were significantly lower in the Upper group than in the Lower group. Blood urea nitrogen (BUN) was higher in the Upper group than in the Lower group (P = 0.0065). Logistic regression analysis revealed that BUN was most strongly associated with the diagnosis of upper vs lower GI bleeding. ROC analysis revealed a threshold BUN value of 21.0 mg/dL, with a specificity of 93.0%. CONCLUSION: The threshold BUN value for distinguishing upper from lower GI bleeding was 21.0 mg/dL. PMID:26034359

Factors associated with vocal fold pathologies in teachers.

PubMed

Souza, Carla Lima de; Carvalho, Fernando Martins; Araújo, Tânia Maria de; Reis, Eduardo José Farias Borges Dos; Lima, Verônica Maria Cadena; Porto, Lauro Antonio

2011-10-01

To analyze factors associated with the prevalence of the medical diagnosis of vocal fold pathologies in teachers. A census-based epidemiological, cross-sectional study was conducted with 4,495 public primary and secondary school teachers in the city of Salvador, Northeastern Brazil, between March and April 2006. The dependent variable was the self-reported medical diagnosis of vocal fold pathologies and the independent variables were sociodemographic characteristics; professional activity; work organization/interpersonal relationships; physical work environment characteristics; frequency of common mental disorders, measured by the Self-Reporting Questionnaire-20 (SRQ-20 >7); and general health conditions. Descriptive statistical, bivariate and multiple logistic regression analysis techniques were used. The prevalence of self-reported medical diagnosis of vocal fold pathologies was 18.9%. In the logistic regression analysis, the variables that remained associated with this medical diagnosis were as follows: being female, having worked as a teacher for more than seven years, excessive voice use, reporting more than five unfavorable physical work environment characteristics and presence of common mental disorders. The presence of self-reported vocal fold pathologies was associated with factors that point out the need of actions that promote teachers' vocal health and changes in their work structure and organization.

Poor anaerobic power/capability and static balance predicted prospective musculoskeletal injuries among Soldiers of the 101st Airborne (Air Assault) Division.

PubMed

Nagai, Takashi; Lovalekar, Mita; Wohleber, Meleesa F; Perlsweig, Katherine A; Wirt, Michael D; Beals, Kim

2017-11-01

Musculoskeletal injuries have negatively impacted tactical readiness. The identification of prospective and modifiable risk factors of preventable musculoskeletal injuries can guide specific injury prevention strategies for Soldiers and health care providers. To analyze physiological and neuromuscular characteristics as predictors of preventable musculoskeletal injuries. Prospective-cohort study. A total of 491 Soldiers were enrolled and participated in the baseline laboratory testing, including body composition, aerobic capacity, anaerobic power/capacity, muscular strength, flexibility, static balance, and landing biomechanics. After reviewing their medical charts, 275 male Soldiers who met the criteria were divided into two groups: with injuries (INJ) and no injuries (NOI). Simple and multiple logistic regression analyses were used to calculate the odds ratio (OR) and significant predictors of musculoskeletal injuries (p<0.05). The final multiple logistic regression model included the static balance with eyes-closed and peak anaerobic power as predictors of future injuries (p<0.001). The current results highlighted the importance of anaerobic power/capacity and static balance. High intensity training and balance exercise should be incorporated in their physical training as countermeasures. Copyright © 2017 Sports Medicine Australia. All rights reserved.

The likelihood of achieving quantified road safety targets: a binary logistic regression model for possible factors.

PubMed

Sze, N N; Wong, S C; Lee, C Y

2014-12-01

In past several decades, many countries have set quantified road safety targets to motivate transport authorities to develop systematic road safety strategies and measures and facilitate the achievement of continuous road safety improvement. Studies have been conducted to evaluate the association between the setting of quantified road safety targets and road fatality reduction, in both the short and long run, by comparing road fatalities before and after the implementation of a quantified road safety target. However, not much work has been done to evaluate whether the quantified road safety targets are actually achieved. In this study, we used a binary logistic regression model to examine the factors - including vehicle ownership, fatality rate, and national income, in addition to level of ambition and duration of target - that contribute to a target's success. We analyzed 55 quantified road safety targets set by 29 countries from 1981 to 2009, and the results indicate that targets that are in progress and with lower level of ambitions had a higher likelihood of eventually being achieved. Moreover, possible interaction effects on the association between level of ambition and the likelihood of success are also revealed. Copyright © 2014 Elsevier Ltd. All rights reserved.

Assessing landslide susceptibility by statistical data analysis and GIS: the case of Daunia (Apulian Apennines, Italy)

NASA Astrophysics Data System (ADS)

Ceppi, C.; Mancini, F.; Ritrovato, G.

2009-04-01

This study aim at the landslide susceptibility mapping within an area of the Daunia (Apulian Apennines, Italy) by a multivariate statistical method and data manipulation in a Geographical Information System (GIS) environment. Among the variety of existing statistical data analysis techniques, the logistic regression was chosen to produce a susceptibility map all over an area where small settlements are historically threatened by landslide phenomena. By logistic regression a best fitting between the presence or absence of landslide (dependent variable) and the set of independent variables is performed on the basis of a maximum likelihood criterion, bringing to the estimation of regression coefficients. The reliability of such analysis is therefore due to the ability to quantify the proneness to landslide occurrences by the probability level produced by the analysis. The inventory of dependent and independent variables were managed in a GIS, where geometric properties and attributes have been translated into raster cells in order to proceed with the logistic regression by means of SPSS (Statistical Package for the Social Sciences) package. A landslide inventory was used to produce the bivariate dependent variable whereas the independent set of variable concerned with slope, aspect, elevation, curvature, drained area, lithology and land use after their reductions to dummy variables. The effect of independent parameters on landslide occurrence was assessed by the corresponding coefficient in the logistic regression function, highlighting a major role played by the land use variable in determining occurrence and distribution of phenomena. Once the outcomes of the logistic regression are determined, data are re-introduced in the GIS to produce a map reporting the proneness to landslide as predicted level of probability. As validation of results and regression model a cell-by-cell comparison between the susceptibility map and the initial inventory of landslide events was performed and an agreement at 75% level achieved.

Determination of riverbank erosion probability using Locally Weighted Logistic Regression

NASA Astrophysics Data System (ADS)

Ioannidou, Elena; Flori, Aikaterini; Varouchakis, Emmanouil A.; Giannakis, Georgios; Vozinaki, Anthi Eirini K.; Karatzas, George P.; Nikolaidis, Nikolaos

2015-04-01

Riverbank erosion is a natural geomorphologic process that affects the fluvial environment. The most important issue concerning riverbank erosion is the identification of the vulnerable locations. An alternative to the usual hydrodynamic models to predict vulnerable locations is to quantify the probability of erosion occurrence. This can be achieved by identifying the underlying relations between riverbank erosion and the geomorphological or hydrological variables that prevent or stimulate erosion. Thus, riverbank erosion can be determined by a regression model using independent variables that are considered to affect the erosion process. The impact of such variables may vary spatially, therefore, a non-stationary regression model is preferred instead of a stationary equivalent. Locally Weighted Regression (LWR) is proposed as a suitable choice. This method can be extended to predict the binary presence or absence of erosion based on a series of independent local variables by using the logistic regression model. It is referred to as Locally Weighted Logistic Regression (LWLR). Logistic regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (e.g. binary response) based on one or more predictor variables. The method can be combined with LWR to assign weights to local independent variables of the dependent one. LWR allows model parameters to vary over space in order to reflect spatial heterogeneity. The probabilities of the possible outcomes are modelled as a function of the independent variables using a logistic function. Logistic regression measures the relationship between a categorical dependent variable and, usually, one or several continuous independent variables by converting the dependent variable to probability scores. Then, a logistic regression is formed, which predicts success or failure of a given binary variable (e.g. erosion presence or absence) for any value of the independent variables. The erosion occurrence probability can be calculated in conjunction with the model deviance regarding the independent variables tested. The most straightforward measure for goodness of fit is the G statistic. It is a simple and effective way to study and evaluate the Logistic Regression model efficiency and the reliability of each independent variable. The developed statistical model is applied to the Koiliaris River Basin on the island of Crete, Greece. Two datasets of river bank slope, river cross-section width and indications of erosion were available for the analysis (12 and 8 locations). Two different types of spatial dependence functions, exponential and tricubic, were examined to determine the local spatial dependence of the independent variables at the measurement locations. The results show a significant improvement when the tricubic function is applied as the erosion probability is accurately predicted at all eight validation locations. Results for the model deviance show that cross-section width is more important than bank slope in the estimation of erosion probability along the Koiliaris riverbanks. The proposed statistical model is a useful tool that quantifies the erosion probability along the riverbanks and can be used to assist managing erosion and flooding events. Acknowledgements This work is part of an on-going THALES project (CYBERSENSORS - High Frequency Monitoring System for Integrated Water Resources Management of Rivers). The project has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES. Investing in knowledge society through the European Social Fund.

Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey)

NASA Astrophysics Data System (ADS)

Yilmaz, Işık

2009-06-01

The purpose of this study is to compare the landslide susceptibility mapping methods of frequency ratio (FR), logistic regression and artificial neural networks (ANN) applied in the Kat County (Tokat—Turkey). Digital elevation model (DEM) was first constructed using GIS software. Landslide-related factors such as geology, faults, drainage system, topographical elevation, slope angle, slope aspect, topographic wetness index (TWI) and stream power index (SPI) were used in the landslide susceptibility analyses. Landslide susceptibility maps were produced from the frequency ratio, logistic regression and neural networks models, and they were then compared by means of their validations. The higher accuracies of the susceptibility maps for all three models were obtained from the comparison of the landslide susceptibility maps with the known landslide locations. However, respective area under curve (AUC) values of 0.826, 0.842 and 0.852 for frequency ratio, logistic regression and artificial neural networks showed that the map obtained from ANN model is more accurate than the other models, accuracies of all models can be evaluated relatively similar. The results obtained in this study also showed that the frequency ratio model can be used as a simple tool in assessment of landslide susceptibility when a sufficient number of data were obtained. Input process, calculations and output process are very simple and can be readily understood in the frequency ratio model, however logistic regression and neural networks require the conversion of data to ASCII or other formats. Moreover, it is also very hard to process the large amount of data in the statistical package.

Understanding the non-stationary associations between distrust of the health care system, health conditions, and self-rated health in the elderly: A geographically weighted regression approach

PubMed Central

Yang, Tse-Chuan; Matthews, Stephen A.

2012-01-01

The goals of this study are to explore whether health condition is an antecedent extraneous factor for the relationship between health care system distrust and self-rated health among the elderly, and to investigate if the associations among these variables are place-specific. We used logistic geographically weighted regression to analyze data on an elderly sample residents in the Philadelphia metropolitan area. We found that the health conditions of the elderly account for the association between high distrust and poor/fair self-rated health and that the distrust/self-rated health relationship varied spatially. This finding suggests that a place-centered perspective can inform distrust/self-rated health research. PMID:22321903

A Comparison of Logistic Regression, Neural Networks, and Classification Trees Predicting Success of Actuarial Students

ERIC Educational Resources Information Center

Schumacher, Phyllis; Olinsky, Alan; Quinn, John; Smith, Richard

2010-01-01

The authors extended previous research by 2 of the authors who conducted a study designed to predict the successful completion of students enrolled in an actuarial program. They used logistic regression to determine the probability of an actuarial student graduating in the major or dropping out. They compared the results of this study with those…

Logistic regression accuracy across different spatial and temporal scales for a wide-ranging species, the marbled murrelet

Treesearch

Carolyn B. Meyer; Sherri L. Miller; C. John Ralph

2004-01-01

The scale at which habitat variables are measured affects the accuracy of resource selection functions in predicting animal use of sites. We used logistic regression models for a wide-ranging species, the marbled murrelet, (Brachyramphus marmoratus) in a large region in California to address how much changing the spatial or temporal scale of...

Odds Ratio, Delta, ETS Classification, and Standardization Measures of DIF Magnitude for Binary Logistic Regression

ERIC Educational Resources Information Center

Monahan, Patrick O.; McHorney, Colleen A.; Stump, Timothy E.; Perkins, Anthony J.

2007-01-01

Previous methodological and applied studies that used binary logistic regression (LR) for detection of differential item functioning (DIF) in dichotomously scored items either did not report an effect size or did not employ several useful measures of DIF magnitude derived from the LR model. Equations are provided for these effect size indices.…

A Generalized Logistic Regression Procedure to Detect Differential Item Functioning among Multiple Groups

ERIC Educational Resources Information Center

Magis, David; Raiche, Gilles; Beland, Sebastien; Gerard, Paul

2011-01-01

We present an extension of the logistic regression procedure to identify dichotomous differential item functioning (DIF) in the presence of more than two groups of respondents. Starting from the usual framework of a single focal group, we propose a general approach to estimate the item response functions in each group and to test for the presence…

Risk Factors of Falls in Community-Dwelling Older Adults: Logistic Regression Tree Analysis

ERIC Educational Resources Information Center

Yamashita, Takashi; Noe, Douglas A.; Bailer, A. John

2012-01-01

Purpose of the Study: A novel logistic regression tree-based method was applied to identify fall risk factors and possible interaction effects of those risk factors. Design and Methods: A nationally representative sample of American older adults aged 65 years and older (N = 9,592) in the Health and Retirement Study 2004 and 2006 modules was used.…

Estimation of Logistic Regression Models in Small Samples. A Simulation Study Using a Weakly Informative Default Prior Distribution

ERIC Educational Resources Information Center

Gordovil-Merino, Amalia; Guardia-Olmos, Joan; Pero-Cebollero, Maribel

2012-01-01

In this paper, we used simulations to compare the performance of classical and Bayesian estimations in logistic regression models using small samples. In the performed simulations, conditions were varied, including the type of relationship between independent and dependent variable values (i.e., unrelated and related values), the type of variable…

Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA

USGS Publications Warehouse

Ohlmacher, G.C.; Davis, J.C.

2003-01-01

Landslides in the hilly terrain along the Kansas and Missouri rivers in northeastern Kansas have caused millions of dollars in property damage during the last decade. To address this problem, a statistical method called multiple logistic regression has been used to create a landslide-hazard map for Atchison, Kansas, and surrounding areas. Data included digitized geology, slopes, and landslides, manipulated using ArcView GIS. Logistic regression relates predictor variables to the occurrence or nonoccurrence of landslides within geographic cells and uses the relationship to produce a map showing the probability of future landslides, given local slopes and geologic units. Results indicated that slope is the most important variable for estimating landslide hazard in the study area. Geologic units consisting mostly of shale, siltstone, and sandstone were most susceptible to landslides. Soil type and aspect ratio were considered but excluded from the final analysis because these variables did not significantly add to the predictive power of the logistic regression. Soil types were highly correlated with the geologic units, and no significant relationships existed between landslides and slope aspect. ?? 2003 Elsevier Science B.V. All rights reserved.

A Method for Calculating the Probability of Successfully Completing a Rocket Propulsion Ground Test

NASA Technical Reports Server (NTRS)

Messer, Bradley

2007-01-01

Propulsion ground test facilities face the daily challenge of scheduling multiple customers into limited facility space and successfully completing their propulsion test projects. Over the last decade NASA s propulsion test facilities have performed hundreds of tests, collected thousands of seconds of test data, and exceeded the capabilities of numerous test facility and test article components. A logistic regression mathematical modeling technique has been developed to predict the probability of successfully completing a rocket propulsion test. A logistic regression model is a mathematical modeling approach that can be used to describe the relationship of several independent predictor variables X(sub 1), X(sub 2),.., X(sub k) to a binary or dichotomous dependent variable Y, where Y can only be one of two possible outcomes, in this case Success or Failure of accomplishing a full duration test. The use of logistic regression modeling is not new; however, modeling propulsion ground test facilities using logistic regression is both a new and unique application of the statistical technique. Results from this type of model provide project managers with insight and confidence into the effectiveness of rocket propulsion ground testing.

Predicting risk for portal vein thrombosis in acute pancreatitis patients: A comparison of radical basis function artificial neural network and logistic regression models.

PubMed

Fei, Yang; Hu, Jian; Gao, Kun; Tu, Jianfeng; Li, Wei-Qin; Wang, Wei

2017-06-01

To construct a radical basis function (RBF) artificial neural networks (ANNs) model to predict the incidence of acute pancreatitis (AP)-induced portal vein thrombosis. The analysis included 353 patients with AP who had admitted between January 2011 and December 2015. RBF ANNs model and logistic regression model were constructed based on eleven factors relevant to AP respectively. Statistical indexes were used to evaluate the value of the prediction in two models. The predict sensitivity, specificity, positive predictive value, negative predictive value and accuracy by RBF ANNs model for PVT were 73.3%, 91.4%, 68.8%, 93.0% and 87.7%, respectively. There were significant differences between the RBF ANNs and logistic regression models in these parameters (P<0.05). In addition, a comparison of the area under receiver operating characteristic curves of the two models showed a statistically significant difference (P<0.05). The RBF ANNs model is more likely to predict the occurrence of PVT induced by AP than logistic regression model. D-dimer, AMY, Hct and PT were important prediction factors of approval for AP-induced PVT. Copyright © 2017 Elsevier Inc. All rights reserved.

Risk Factors Associated with Mortality and Increased Drug Costs in Nonvariceal Upper Gastrointestinal Bleeding.

PubMed

Lu, Mingliang; Sun, Gang; Zhang, Xiu-li; Zhang, Xiao-mei; Liu, Qing-sen; Huang, Qi-yang; Lau, James W Y; Yang, Yun-sheng

2015-06-01

To determine risk factors associated with mortality and increased drug costs in patients with nonvariceal upper gastrointestinal bleeding. We retrospectively analyzed data from patients hospitalized with nonvariceal upper gastrointestinal bleeding between January 2001-December 2011. Demographic and clinical characteristics and drug costs were documented. Univariate analysis determined possible risk factors for mortality. Statistically significant variables were analyzed using a logistic regression model. Multiple linear regression analyzed factors influencing drug costs. p < 0.05 was considered statistically significant. The study included data from 627 patients. Risk factors associated with increased mortality were age > 60, systolic blood pressure<100 mmHg, lack of endoscopic examination, comorbidities, blood transfusion, and rebleeding. Drug costs were higher in patients with rebleeding, blood transfusion, and prolonged hospital stay. In this patient cohort, re-bleeding rate is 11.20% and mortality is 5.74%. The mortality risk in patients with comorbidities was higher than in patients without comorbidities, and was higher in patients requiring blood transfusion than in patients not requiring transfusion. Rebleeding was associ-ated with mortality. Rebleeding, blood transfusion, and prolonged hospital stay were associated with increased drug costs, whereas bleeding from lesions in the esophagus and duodenum was associated with lower drug costs.

EXpectation Propagation LOgistic REgRession (EXPLORER): Distributed Privacy-Preserving Online Model Learning

PubMed Central

Wang, Shuang; Jiang, Xiaoqian; Wu, Yuan; Cui, Lijuan; Cheng, Samuel; Ohno-Machado, Lucila

2013-01-01

We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection etc.) as the traditional frequentist Logistic Regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications. PMID:23562651

Dietary consumption patterns and laryngeal cancer risk.

PubMed

Vlastarakos, Petros V; Vassileiou, Andrianna; Delicha, Evie; Kikidis, Dimitrios; Protopapas, Dimosthenis; Nikolopoulos, Thomas P

2016-06-01

We conducted a case-control study to investigate the effect of diet on laryngeal carcinogenesis. Our study population was made up of 140 participants-70 patients with laryngeal cancer (LC) and 70 controls with a non-neoplastic condition that was unrelated to diet, smoking, or alcohol. A food-frequency questionnaire determined the mean consumption of 113 different items during the 3 years prior to symptom onset. Total energy intake and cooking mode were also noted. The relative risk, odds ratio (OR), and 95% confidence interval (CI) were estimated by multiple logistic regression analysis. We found that the total energy intake was significantly higher in the LC group (p < 0.001), and that the difference remained statistically significant after logistic regression analysis (p < 0.001; OR: 118.70). Notably, meat consumption was higher in the LC group (p < 0.001), and the difference remained significant after logistic regression analysis (p = 0.029; OR: 1.16). LC patients also consumed significantly more fried food (p = 0.036); this difference also remained significant in the logistic regression model (p = 0.026; OR: 5.45). The LC group also consumed significantly more seafood (p = 0.012); the difference persisted after logistic regression analysis (p = 0.009; OR: 2.48), with the consumption of shrimp proving detrimental (p = 0.049; OR: 2.18). Finally, the intake of zinc was significantly higher in the LC group before and after logistic regression analysis (p = 0.034 and p = 0.011; OR: 30.15, respectively). Cereal consumption (including pastas) was also higher among the LC patients (p = 0.043), with logistic regression analysis showing that their negative effect was possibly associated with the sauces and dressings that traditionally accompany pasta dishes (p = 0.006; OR: 4.78). Conversely, a higher consumption of dairy products was found in controls (p < 0.05); logistic regression analysis showed that calcium appeared to be protective at the micronutrient level (p < 0.001; OR: 0.27). We found no difference in the overall consumption of fruits and vegetables between the LC patients and controls; however, the LC patients did have a greater consumption of cooked tomatoes and cooked root vegetables (p = 0.039 for both), and the controls had more consumption of leeks (p = 0.042) and, among controls younger than 65 years, cooked beans (p = 0.037). Lemon (p = 0.037), squeezed fruit juice (p = 0.032), and watermelon (p = 0.018) were also more frequently consumed by the controls. Other differences at the micronutrient level included greater consumption by the LC patients of retinol (p = 0.044), polyunsaturated fats (p = 0.041), and linoleic acid (p = 0.008); LC patients younger than 65 years also had greater intake of riboflavin (p = 0.045). We conclude that the differences in dietary consumption patterns between LC patients and controls indicate a possible role for lifestyle modifications involving nutritional factors as a means of decreasing the risk of laryngeal cancer.

A Comparison of the Logistic Regression and Contingency Table Methods for Simultaneous Detection of Uniform and Nonuniform DIF

ERIC Educational Resources Information Center

Guler, Nese; Penfield, Randall D.

2009-01-01

In this study, we investigate the logistic regression (LR), Mantel-Haenszel (MH), and Breslow-Day (BD) procedures for the simultaneous detection of both uniform and nonuniform differential item functioning (DIF). A simulation study was used to assess and compare the Type I error rate and power of a combined decision rule (CDR), which assesses DIF…

The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression: Examination of Further Refinements

ERIC Educational Resources Information Center

Le, Huy; Marcus, Justin

2012-01-01

This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…

Predicting Student Success on the Texas Chemistry STAAR Test: A Logistic Regression Analysis

ERIC Educational Resources Information Center

Johnson, William L.; Johnson, Annabel M.; Johnson, Jared

2012-01-01

Background: The context is the new Texas STAAR end-of-course testing program. Purpose: The authors developed a logistic regression model to predict who would pass-or-fail the new Texas chemistry STAAR end-of-course exam. Setting: Robert E. Lee High School (5A) with an enrollment of 2700 students, Tyler, Texas. Date of the study was the 2011-2012…

Using ROC curves to compare neural networks and logistic regression for modeling individual noncatastrophic tree mortality

Treesearch

Susan L. King

2003-01-01

The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. The output of the classifier is usually a continuous number between 0 and 1. A threshold is selected between 0 and 1 and all of the trees below the threshold are classified as...

Logistic regression trees for initial selection of interesting loci in case-control studies

PubMed Central

Nickolov, Radoslav Z; Milanov, Valentin B

2007-01-01

Modern genetic epidemiology faces the challenge of dealing with hundreds of thousands of genetic markers. The selection of a small initial subset of interesting markers for further investigation can greatly facilitate genetic studies. In this contribution we suggest the use of a logistic regression tree algorithm known as logistic tree with unbiased selection. Using the simulated data provided for Genetic Analysis Workshop 15, we show how this algorithm, with incorporation of multifactor dimensionality reduction method, can reduce an initial large pool of markers to a small set that includes the interesting markers with high probability. PMID:18466557

Using Logistic Regression to Predict the Probability of Debris Flows in Areas Burned by Wildfires, Southern California, 2003-2006

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.; Michael, John A.; Helsel, Dennis R.

2008-01-01

Logistic regression was used to develop statistical models that can be used to predict the probability of debris flows in areas recently burned by wildfires by using data from 14 wildfires that burned in southern California during 2003-2006. Twenty-eight independent variables describing the basin morphology, burn severity, rainfall, and soil properties of 306 drainage basins located within those burned areas were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows soon after the 2003 to 2006 fires were delineated from data in the National Elevation Dataset using a geographic information system; (2) Data describing the basin morphology, burn severity, rainfall, and soil properties were compiled for each basin. These data were then input to a statistics software package for analysis using logistic regression; and (3) Relations between the occurrence or absence of debris flows and the basin morphology, burn severity, rainfall, and soil properties were evaluated, and five multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combinations produced the most effective models, and the multivariate models that best predicted the occurrence of debris flows were identified. Percentage of high burn severity and 3-hour peak rainfall intensity were significant variables in all models. Soil organic matter content and soil clay content were significant variables in all models except Model 5. Soil slope was a significant variable in all models except Model 4. The most suitable model can be selected from these five models on the basis of the availability of independent variables in the particular area of interest and field checking of probability maps. The multivariate logistic regression models can be entered into a geographic information system, and maps showing the probability of debris flows can be constructed in recently burned areas of southern California. This study demonstrates that logistic regression is a valuable tool for developing models that predict the probability of debris flows occurring in recently burned landscapes.

Polymorphism Thr160Thr in SRD5A1, involved in the progesterone metabolism, modifies postmenopausal breast cancer risk associated with menopausal hormone therapy.

PubMed

Hein, R; Abbas, S; Seibold, P; Salazar, R; Flesch-Janys, D; Chang-Claude, J

2012-01-01

Menopausal hormone therapy (MHT) is associated with an increased breast cancer risk in postmenopausal women, with combined estrogen-progestagen therapy posing a greater risk than estrogen monotherapy. However, few studies focused on potential effect modification of MHT-associated breast cancer risk by genetic polymorphisms in the progesterone metabolism. We assessed effect modification of MHT use by five coding single nucleotide polymorphisms (SNPs) in the progesterone metabolizing enzymes AKR1C3 (rs7741), AKR1C4 (rs3829125, rs17134592), and SRD5A1 (rs248793, rs3736316) using a two-center population-based case-control study from Germany with 2,502 postmenopausal breast cancer patients and 4,833 matched controls. An empirical-Bayes procedure that tests for interaction using a weighted combination of the prospective and the retrospective case-control estimators as well as standard prospective logistic regression were applied to assess multiplicative statistical interaction between polymorphisms and duration of MHT use with regard to breast cancer risk assuming a log-additive mode of inheritance. No genetic marginal effects were observed. Breast cancer risk associated with duration of combined therapy was significantly modified by SRD5A1_rs3736316, showing a reduced risk elevation in carriers of the minor allele (p (interaction,empirical-Bayes) = 0.006 using the empirical-Bayes method, p (interaction,logistic regression) = 0.013 using logistic regression). The risk associated with duration of use of monotherapy was increased by AKR1C3_rs7741 in minor allele carriers (p (interaction,empirical-Bayes) = 0.083, p (interaction,logistic regression) = 0.029) and decreased in minor allele carriers of two SNPs in AKR1C4 (rs3829125: p (interaction,empirical-Bayes) = 0.07, p (interaction,logistic regression) = 0.021; rs17134592: p (interaction,empirical-Bayes) = 0.101, p (interaction,logistic regression) = 0.038). After Bonferroni correction for multiple testing only SRD5A1_rs3736316 assessed using the empirical-Bayes method remained significant. Postmenopausal breast cancer risk associated with combined therapy may be modified by genetic variation in SRD5A1. Further well-powered studies are, however, required to replicate our finding.

[Investigation of health literacy and enterprise provided health service utilization among migrants in construction site].

PubMed

Jiang, Ying; Zeng, Qingqi; Ji, Ying; Wang, Yanling; Zheng, Yunting; Chang, Chun

2015-01-01

To investigate health literacy and enterprise provided health service utilization among migrants in construction sites and explore the influencing factors of enterprise provided health service utilization. All 652 migrants in 10 construction sites in Xi'an and Tongchuan were selected using stratified cluster sampling method, and health literacy level, occupational health awareness and enterprise provided health service utilization of migrants were investigated in 2013 April to June.Score and pass rate was used to describe status of health literacy and occupational health awareness of migrants. Chi-square was used to analyze the difference of occupational health awareness and enterprise provided health service utilization between migrants of different levels of health literacy. And logistic regression was used to analyze the influencing factors of enterprise provided health service utilization. Average score of health literacy among migrants in construction site was (3.75 ± 2.17) (9 score totally). Migrants who knew enterprise should provide health training, physical examination, safety training, occupational protection and pay health insurance for workers accounted for 28.2% (174/616), 43.5% (268/616), 52.8% (325/616), 54.9% (338/616) and 37.7% (230/616) respectively, and the percentage of migrants who thought there were noise and dust in their working environment were 46.4% (201/627) and 44.8% (281/627) respectively.61.1% (373/610) received none of health training, occupational training, physical examination and first-aid kit, and only 0.8% (5/610) had utilized all of the above health service in workplace. And logistic regression showed that migrants whose health literacy score was higher than 5 had 1.819 times probability to utilize enterprise provided health service (OR = 1.82, 95%CI:1.13-2.92) , and migrants who were educated for more than 13 years had 3.812 times probability to utilize enterprise provided health service than those who were educated for less than 6 years (OR = 3.81, 95%CI:1.75-8.31) .However, occupational health awareness had no significant influence to the utility of enterprise provided health service utilization in logistic regression (χ(2) = 3.50, P = 0.061). Occupational health awareness and enterprise provided health service utilization were both low among migrants in construction site, level of health literacy and school years were the main factors that influence enterprise provided health service utilization.

When and why a colonoscopist should discontinue colonoscopy by himself?

PubMed

Gan, Tao; Yang, Jin-Lin; Wu, Jun-Chao; Wang, Yi-Ping; Yang, Li

2015-07-07

To investigate when and why a colonoscopist should discontinue incomplete colonoscopy by himself. In this cross-sectional study, 517 difficult colonoscope insertions (Grade C, Kudo's difficulty classification) screened from 37800 colonoscopy insertions were collected from April 2004 to June 2014 by three 4(th)-level (Kudo's classification) colonoscopists. The following common factors for the incomplete insertion were excluded: structural obstruction of the colon or rectum, insufficient colon cleansing, discontinuation due to patient's discomfort or pain, severe colon disease with a perforation risk (e.g., severe ischemic colonopathy). All the excluded patients were re-scheduled if permission was obtained from the patients whose intubation had failed. If the repeat intubations were still a failure because of the difficult operative techniques, those patients were also included in this study. The patient's age, sex, anesthesia and colonoscope type were recorded before colonoscopy. During the colonoscopic examination, the influencing factors of fixation, tortuosity, laxity and redundancy of the colon were assessed, and the insertion time (> 10 min or ≤ 10 min) were registered. The insertion time was analyzed by t-test, and other factors were analyzed by univariate and multivariate logistic regression. Three hundred and twenty-two (62.3%) of the 517 insertions were complete in the colonoscope insertion into the ileocecum, but 195 (37.7%) failed in the insertion. Fixation, tortuosity, laxity or redundancy occurred during the colonoscopic examination. Multivariate logistic regression analysis revealed that fixation (OR = 0.06, 95%CI: 0.03-0.16, P < 0.001) and tortuosity (OR = 0.04, 95%CI: 0.02-0.08, P < 0.001) were significantly related to the insertion into the ileocecum in the left hemicolon; multivariate logistic regression analysis also revealed that fixation (OR = 0.16, 95%CI: 0.06-0.39, P < 0.001), tortuosity (OR 0.23, 95%CI: 0.13-0.43, P < 0.001), redundancy (OR = 0.12, 95%CI: 0.05-0.26, P < 0.001) and sex (OR = 0.35, 95%CI: 0.20-0.63, P < 0.001) were significantly related to the insertion into the ileocecum in the right hemicolon. Prolonged insertion time (> 10 min) was an unfavorable factor for the insertion into the ileocecum. Colonoscopy should be discontinued if freedom of the colonoscope body's insertion and rotation is completely lost, and the insertion time is prolonged over 30 min.

Reproductive factors, nutritional status and serum 25(OH)D levels in women with breast cancer: A case control study.

PubMed

Sofi, Nighat Y; Jain, Monika; Kapil, Umesh; Seenu, Vuthaluru; R, Lakshmy; Yadav, Chander P; Pandey, Ravindra M; Sareen, Neha

2018-01-01

The study was conducted with an objective to investigate the association between reproductive factors, nutritional status and serum 25(OH)D levels among women diagnosed with breast cancer (BC). A total of 200 women with BC attending a tertiary healthcare institute of Delhi, India matched with 200 healthy women for age (±2years) and socio economic status were included in the study. Data was collected on socio-demographic profile, reproductive factors, physical activity and dietary intake (24h dietary recall and food frequency questionnaire) using interviewer administered structured questionnaires and standard tools. Non fasting blood samples (5ml) were collected for the biochemical estimation of serum 25(OH)D and calcium levels by chemiluminescent immunoassay and colorimetric assay technique. Data was analyzed by univariable conditional logistic regression and significant variables with (p<0.05), were analyzed in final model by conditional multivariable logistic regression analysis. The mean age of patients at diagnosis of BC was 45±10years. Results of multivariable conditional logistic regression analysis revealed significantly higher odds of BC for reproductive factors like age at marriage (more than 23 years), number of abortions, history or current use of oral contraceptive pills (OCP), with [OR (95% CI)] of [2.4 (1.2-4.9)], [4.0 (1.6-12.6)], [2.4 (1.2-5.0)]. Women with physically light activities and occasional consumption of eggs were found to have higher odds of BC [4.6 (1.6-13.0)] and [3.2 (1.6-6.3)]. Women with serum 25(OH)D levels less than 20ng/ml and calcium levels less than 10.5mg/dl had higher odds of having BC [2.4 (1.2-5.1)] and [3.7 (1.5-8.8)]. A protective effect of urban areas as place of residence and energy intake greater than 50% of Recommended Dietary Allowance (RDA) per day against BC was observed (p<0.05). The findings of the present study revealed a significant association of reproductive and dietary factors in addition to sedentary physical activity and low serum 25(OH)D levels in women diagnosed with BC. Copyright © 2017 Elsevier Ltd. All rights reserved.

High stress, lack of sleep, low school performance, and suicide attempts are associated with high energy drink intake in adolescents.

PubMed

Kim, So Young; Sim, Songyong; Choi, Hyo Geun

2017-01-01

Although an association between energy drinks and suicide has been suggested, few prior studies have considered the role of emotional factors including stress, sleep, and school performance in adolescents. This study aimed to evaluate the association of energy drinks with suicide, independent of possible confounders including stress, sleep, and school performance. In total, 121,106 adolescents with 13-18 years olds from the 2014 and 2015 Korea Youth Risk Behavior Web-based Survey were surveyed for age, sex, region of residence, economic level, paternal and maternal education level, sleep time, stress level, school performance, frequency of energy drink intake, and suicide attempts. Subjective stress levels were classified into severe, moderate, mild, a little, and no stress. Sleep time was divided into 6 groups: < 6 h; 6 ≤ h < 7; 7 ≤ h < 8; 8 ≤ h < 9; and ≥ 9 h. School performance was classified into 5 levels: A (highest), B (middle, high), C (middle), D (middle, low), and E (lowest). Frequency of energy drink consumption was divided into 3 groups: ≥ 3, 1-2, and 0 times a week. The associations of sleep time, stress level, and school performance with suicide attempts and the frequency of energy drink intake were analyzed using multiple and ordinal logistic regression analysis, respectively, with complex sampling. The relationship between frequency of energy drink intake and suicide attempts was analyzed using multiple logistic regression analysis with complex sampling. Higher stress levels, lack of sleep, and low school performance were significantly associated with suicide attempts (each P < 0.001). These variables of high stress level, abnormal sleep time, and low school performance were also proportionally related with higher energy drink intake (P < 0.001). Frequent energy drink intake was significantly associated with suicide attempts in multiple logistic regression analyses (AOR for frequency of energy intake ≥ 3 times a week = 3.03, 95% CI = 2.64-3.49, P < 0.001). Severe stress, inadequate sleep, and low school performance were related with more energy drink intake and suicide attempts in Korean adolescents. Frequent energy drink intake was positively related with suicide attempts, even after adjusting for stress, sleep time, and school performance.

High stress, lack of sleep, low school performance, and suicide attempts are associated with high energy drink intake in adolescents

PubMed Central

Kim, So Young; Sim, Songyong

2017-01-01

Objective Although an association between energy drinks and suicide has been suggested, few prior studies have considered the role of emotional factors including stress, sleep, and school performance in adolescents. This study aimed to evaluate the association of energy drinks with suicide, independent of possible confounders including stress, sleep, and school performance. Methods In total, 121,106 adolescents with 13–18 years olds from the 2014 and 2015 Korea Youth Risk Behavior Web-based Survey were surveyed for age, sex, region of residence, economic level, paternal and maternal education level, sleep time, stress level, school performance, frequency of energy drink intake, and suicide attempts. Subjective stress levels were classified into severe, moderate, mild, a little, and no stress. Sleep time was divided into 6 groups: < 6 h; 6 ≤ h < 7; 7 ≤ h < 8; 8 ≤ h < 9; and ≥ 9 h. School performance was classified into 5 levels: A (highest), B (middle, high), C (middle), D (middle, low), and E (lowest). Frequency of energy drink consumption was divided into 3 groups: ≥ 3, 1–2, and 0 times a week. The associations of sleep time, stress level, and school performance with suicide attempts and the frequency of energy drink intake were analyzed using multiple and ordinal logistic regression analysis, respectively, with complex sampling. The relationship between frequency of energy drink intake and suicide attempts was analyzed using multiple logistic regression analysis with complex sampling. Results Higher stress levels, lack of sleep, and low school performance were significantly associated with suicide attempts (each P < 0.001). These variables of high stress level, abnormal sleep time, and low school performance were also proportionally related with higher energy drink intake (P < 0.001). Frequent energy drink intake was significantly associated with suicide attempts in multiple logistic regression analyses (AOR for frequency of energy intake ≥ 3 times a week = 3.03, 95% CI = 2.64–3.49, P < 0.001). Conclusion Severe stress, inadequate sleep, and low school performance were related with more energy drink intake and suicide attempts in Korean adolescents. Frequent energy drink intake was positively related with suicide attempts, even after adjusting for stress, sleep time, and school performance. PMID:29135989

[Risk factor analysis of the patients with solitary pulmonary nodules and establishment of a prediction model for the probability of malignancy].

PubMed

Wang, X; Xu, Y H; Du, Z Y; Qian, Y J; Xu, Z H; Chen, R; Shi, M H

2018-02-23

Objective: This study aims to analyze the relationship among the clinical features, radiologic characteristics and pathological diagnosis in patients with solitary pulmonary nodules, and establish a prediction model for the probability of malignancy. Methods: Clinical data of 372 patients with solitary pulmonary nodules who underwent surgical resection with definite postoperative pathological diagnosis were retrospectively analyzed. In these cases, we collected clinical and radiologic features including gender, age, smoking history, history of tumor, family history of cancer, the location of lesion, ground-glass opacity, maximum diameter, calcification, vessel convergence sign, vacuole sign, pleural indentation, speculation and lobulation. The cases were divided to modeling group (268 cases) and validation group (104 cases). A new prediction model was established by logistic regression analying the data from modeling group. Then the data of validation group was planned to validate the efficiency of the new model, and was compared with three classical models(Mayo model, VA model and LiYun model). With the calculated probability values for each model from validation group, SPSS 22.0 was used to draw the receiver operating characteristic curve, to assess the predictive value of this new model. Results: 112 benign SPNs and 156 malignant SPNs were included in modeling group. Multivariable logistic regression analysis showed that gender, age, history of tumor, ground -glass opacity, maximum diameter, and speculation were independent predictors of malignancy in patients with SPN( P <0.05). We calculated a prediction model for the probability of malignancy as follow: p =e(x)/(1+ e(x)), x=-4.8029-0.743×gender+ 0.057×age+ 1.306×history of tumor+ 1.305×ground-glass opacity+ 0.051×maximum diameter+ 1.043×speculation. When the data of validation group was added to the four-mathematical prediction model, The area under the curve of our mathematical prediction model was 0.742, which is greater than other models (Mayo 0.696, VA 0.634, LiYun 0.681), while the differences between any two of the four models were not significant ( P >0.05). Conclusions: Age of patient, gender, history of tumor, ground-glass opacity, maximum diameter and speculation are independent predictors of malignancy in patients with solitary pulmonary nodule. This logistic regression prediction mathematic model is not inferior to those classical models in estimating the prognosis of SPNs.

Explicit criteria for prioritization of cataract surgery

PubMed Central

Ma Quintana, José; Escobar, Antonio; Bilbao, Amaia

2006-01-01

Background Consensus techniques have been used previously to create explicit criteria to prioritize cataract extraction; however, the appropriateness of the intervention was not included explicitly in previous studies. We developed a prioritization tool for cataract extraction according to the RAND method. Methods Criteria were developed using a modified Delphi panel judgment process. A panel of 11 ophthalmologists was assembled. Ratings were analyzed regarding the level of agreement among panelists. We studied the effect of all variables on the final panel score using general linear and logistic regression models. Priority scoring systems were developed by means of optimal scaling and general linear models. The explicit criteria developed were summarized by means of regression tree analysis. Results Eight variables were considered to create the indications. Of the 310 indications that the panel evaluated, 22.6% were considered high priority, 52.3% intermediate priority, and 25.2% low priority. Agreement was reached for 31.9% of the indications and disagreement for 0.3%. Logistic regression and general linear models showed that the preoperative visual acuity of the cataractous eye, visual function, and anticipated visual acuity postoperatively were the most influential variables. Alternative and simple scoring systems were obtained by optimal scaling and general linear models where the previous variables were also the most important. The decision tree also shows the importance of the previous variables and the appropriateness of the intervention. Conclusion Our results showed acceptable validity as an evaluation and management tool for prioritizing cataract extraction. It also provides easy algorithms for use in clinical practice. PMID:16512893

Development of a Bayesian model to estimate health care outcomes in the severely wounded

PubMed Central

Stojadinovic, Alexander; Eberhardt, John; Brown, Trevor S; Hawksworth, Jason S; Gage, Frederick; Tadaki, Douglas K; Forsberg, Jonathan A; Davis, Thomas A; Potter, Benjamin K; Dunne, James R; Elster, E A

2010-01-01

Background: Graphical probabilistic models have the ability to provide insights as to how clinical factors are conditionally related. These models can be used to help us understand factors influencing health care outcomes and resource utilization, and to estimate morbidity and clinical outcomes in trauma patient populations. Study design: Thirty-two combat casualties with severe extremity injuries enrolled in a prospective observational study were analyzed using step-wise machine-learned Bayesian belief network (BBN) and step-wise logistic regression (LR). Models were evaluated using 10-fold cross-validation to calculate area-under-the-curve (AUC) from receiver operating characteristics (ROC) curves. Results: Our BBN showed important associations between various factors in our data set that could not be developed using standard regression methods. Cross-validated ROC curve analysis showed that our BBN model was a robust representation of our data domain and that LR models trained on these findings were also robust: hospital-acquired infection (AUC: LR, 0.81; BBN, 0.79), intensive care unit length of stay (AUC: LR, 0.97; BBN, 0.81), and wound healing (AUC: LR, 0.91; BBN, 0.72) showed strong AUC. Conclusions: A BBN model can effectively represent clinical outcomes and biomarkers in patients hospitalized after severe wounding, and is confirmed by 10-fold cross-validation and further confirmed through logistic regression modeling. The method warrants further development and independent validation in other, more diverse patient populations. PMID:21197361

Food and Drug Administration tobacco regulation and product judgments.

PubMed

Kaufman, Annette R; Finney Rutten, Lila J; Parascandola, Mark; Blake, Kelly D; Augustson, Erik M

2015-04-01

The Family Smoking Prevention and Tobacco Control Act granted the Food and Drug Administration (FDA) the authority to regulate tobacco products in the U.S. However, little is known about how regulation may be related to judgments about tobacco product-related risks. To understand how FDA tobacco regulation beliefs are associated with judgments about tobacco product-related risks. The Health Information National Trends Survey is a national survey of the U.S. adult population. Data used in this analysis were collected from October 2012 through January 2013 (N=3,630) by mailed questionnaire and analyzed in 2013. Weighted bivariate chi-square analyses were used to assess associations among FDA regulation belief, tobacco harm judgments, sociodemographics, and smoking status. A weighted multinomial logistic regression was conducted where FDA regulation belief was regressed on tobacco product judgments, controlling for sociodemographic variables and smoking status. About 41% believed that the FDA regulates tobacco products in the U.S., 23.6% reported the FDA does not, and 35.3% did not know. Chi-square analyses showed that smoking status was significantly related to harm judgments about electronic cigarettes (p<0.0001). The multinomial logistic regression revealed that uncertainty about FDA regulation was associated with tobacco product harm judgment uncertainty. Tobacco product harm perceptions are associated with beliefs about tobacco product regulation by the FDA. These findings suggest the need for increased public awareness and understanding of the role of tobacco product regulation in protecting public health. Copyright © 2015. Published by Elsevier Inc.

Applications of statistics to medical science, III. Correlation and regression.

PubMed

Watanabe, Hiroshi

2012-01-01

In this third part of a series surveying medical statistics, the concepts of correlation and regression are reviewed. In particular, methods of linear regression and logistic regression are discussed. Arguments related to survival analysis will be made in a subsequent paper.

Filtering data from the collaborative initial glaucoma treatment study for improved identification of glaucoma progression.

PubMed

Schell, Greggory J; Lavieri, Mariel S; Stein, Joshua D; Musch, David C

2013-12-21

Open-angle glaucoma (OAG) is a prevalent, degenerate ocular disease which can lead to blindness without proper clinical management. The tests used to assess disease progression are susceptible to process and measurement noise. The aim of this study was to develop a methodology which accounts for the inherent noise in the data and improve significant disease progression identification. Longitudinal observations from the Collaborative Initial Glaucoma Treatment Study (CIGTS) were used to parameterize and validate a Kalman filter model and logistic regression function. The Kalman filter estimates the true value of biomarkers associated with OAG and forecasts future values of these variables. We develop two logistic regression models via generalized estimating equations (GEE) for calculating the probability of experiencing significant OAG progression: one model based on the raw measurements from CIGTS and another model based on the Kalman filter estimates of the CIGTS data. Receiver operating characteristic (ROC) curves and associated area under the ROC curve (AUC) estimates are calculated using cross-fold validation. The logistic regression model developed using Kalman filter estimates as data input achieves higher sensitivity and specificity than the model developed using raw measurements. The mean AUC for the Kalman filter-based model is 0.961 while the mean AUC for the raw measurements model is 0.889. Hence, using the probability function generated via Kalman filter estimates and GEE for logistic regression, we are able to more accurately classify patients and instances as experiencing significant OAG progression. A Kalman filter approach for estimating the true value of OAG biomarkers resulted in data input which improved the accuracy of a logistic regression classification model compared to a model using raw measurements as input. This methodology accounts for process and measurement noise to enable improved discrimination between progression and nonprogression in chronic diseases.

Computing group cardinality constraint solutions for logistic regression problems.

PubMed

Zhang, Yong; Kwon, Dongjin; Pohl, Kilian M

2017-01-01

We derive an algorithm to directly solve logistic regression based on cardinality constraint, group sparsity and use it to classify intra-subject MRI sequences (e.g. cine MRIs) of healthy from diseased subjects. Group cardinality constraint models are often applied to medical images in order to avoid overfitting of the classifier to the training data. Solutions within these models are generally determined by relaxing the cardinality constraint to a weighted feature selection scheme. However, these solutions relate to the original sparse problem only under specific assumptions, which generally do not hold for medical image applications. In addition, inferring clinical meaning from features weighted by a classifier is an ongoing topic of discussion. Avoiding weighing features, we propose to directly solve the group cardinality constraint logistic regression problem by generalizing the Penalty Decomposition method. To do so, we assume that an intra-subject series of images represents repeated samples of the same disease patterns. We model this assumption by combining series of measurements created by a feature across time into a single group. Our algorithm then derives a solution within that model by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The minimum to the smooth and convex logistic regression problem is determined via gradient descent while we derive a closed form solution for finding a sparse approximation of that minimum. We apply our method to cine MRI of 38 healthy controls and 44 adult patients that received reconstructive surgery of Tetralogy of Fallot (TOF) during infancy. Our method correctly identifies regions impacted by TOF and generally obtains statistically significant higher classification accuracy than alternative solutions to this model, i.e., ones relaxing group cardinality constraints. Copyright © 2016 Elsevier B.V. All rights reserved.

Influential factors of red-light running at signalized intersection and prediction using a rare events logistic regression model.

PubMed

Ren, Yilong; Wang, Yunpeng; Wu, Xinkai; Yu, Guizhen; Ding, Chuan

2016-10-01

Red light running (RLR) has become a major safety concern at signalized intersection. To prevent RLR related crashes, it is critical to identify the factors that significantly impact the drivers' behaviors of RLR, and to predict potential RLR in real time. In this research, 9-month's RLR events extracted from high-resolution traffic data collected by loop detectors from three signalized intersections were applied to identify the factors that significantly affect RLR behaviors. The data analysis indicated that occupancy time, time gap, used yellow time, time left to yellow start, whether the preceding vehicle runs through the intersection during yellow, and whether there is a vehicle passing through the intersection on the adjacent lane were significantly factors for RLR behaviors. Furthermore, due to the rare events nature of RLR, a modified rare events logistic regression model was developed for RLR prediction. The rare events logistic regression method has been applied in many fields for rare events studies and shows impressive performance, but so far none of previous research has applied this method to study RLR. The results showed that the rare events logistic regression model performed significantly better than the standard logistic regression model. More importantly, the proposed RLR prediction method is purely based on loop detector data collected from a single advance loop detector located 400 feet away from stop-bar. This brings great potential for future field applications of the proposed method since loops have been widely implemented in many intersections and can collect data in real time. This research is expected to contribute to the improvement of intersection safety significantly. Copyright © 2016 Elsevier Ltd. All rights reserved.

Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery.

PubMed

Engoren, Milo; Habib, Robert H; Dooner, John J; Schwann, Thomas A

2013-08-01

As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction group and 216 (8.0 %) of the 2,711 patient Validation group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p < .001). Artificial neural nets were less accurate with AU ROC = 0.597 ± .001 in the Construction group. Predictive accuracy of all three techniques fell in the Validation group. However, the accuracy of genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate.

Artificial neural network, genetic algorithm, and logistic regression applications for predicting renal colic in emergency settings.

PubMed

Eken, Cenker; Bilge, Ugur; Kartal, Mutlu; Eray, Oktay

2009-06-03

Logistic regression is the most common statistical model for processing multivariate data in the medical literature. Artificial intelligence models like an artificial neural network (ANN) and genetic algorithm (GA) may also be useful to interpret medical data. The purpose of this study was to perform artificial intelligence models on a medical data sheet and compare to logistic regression. ANN, GA, and logistic regression analysis were carried out on a data sheet of a previously published article regarding patients presenting to an emergency department with flank pain suspicious for renal colic. The study population was composed of 227 patients: 176 patients had a diagnosis of urinary stone, while 51 ultimately had no calculus. The GA found two decision rules in predicting urinary stones. Rule 1 consisted of being male, pain not spreading to back, and no fever. In rule 2, pelvicaliceal dilatation on bedside ultrasonography replaced no fever. ANN, GA rule 1, GA rule 2, and logistic regression had a sensitivity of 94.9, 67.6, 56.8, and 95.5%, a specificity of 78.4, 76.47, 86.3, and 47.1%, a positive likelihood ratio of 4.4, 2.9, 4.1, and 1.8, and a negative likelihood ratio of 0.06, 0.42, 0.5, and 0.09, respectively. The area under the curve was found to be 0.867, 0.720, 0.715, and 0.713 for all applications, respectively. Data mining techniques such as ANN and GA can be used for predicting renal colic in emergency settings and to constitute clinical decision rules. They may be an alternative to conventional multivariate analysis applications used in biostatistics.

Application of logistic regression for landslide susceptibility zoning of Cekmece Area, Istanbul, Turkey

NASA Astrophysics Data System (ADS)

Duman, T. Y.; Can, T.; Gokceoglu, C.; Nefeslioglu, H. A.; Sonmez, H.

2006-11-01

As a result of industrialization, throughout the world, cities have been growing rapidly for the last century. One typical example of these growing cities is Istanbul, the population of which is over 10 million. Due to rapid urbanization, new areas suitable for settlement and engineering structures are necessary. The Cekmece area located west of the Istanbul metropolitan area is studied, because the landslide activity is extensive in this area. The purpose of this study is to develop a model that can be used to characterize landslide susceptibility in map form using logistic regression analysis of an extensive landslide database. A database of landslide activity was constructed using both aerial-photography and field studies. About 19.2% of the selected study area is covered by deep-seated landslides. The landslides that occur in the area are primarily located in sandstones with interbedded permeable and impermeable layers such as claystone, siltstone and mudstone. About 31.95% of the total landslide area is located at this unit. To apply logistic regression analyses, a data matrix including 37 variables was constructed. The variables used in the forwards stepwise analyses are different measures of slope, aspect, elevation, stream power index (SPI), plan curvature, profile curvature, geology, geomorphology and relative permeability of lithological units. A total of 25 variables were identified as exerting strong influence on landslide occurrence, and included by the logistic regression equation. Wald statistics values indicate that lithology, SPI and slope are more important than the other parameters in the equation. Beta coefficients of the 25 variables included the logistic regression equation provide a model for landslide susceptibility in the Cekmece area. This model is used to generate a landslide susceptibility map that correctly classified 83.8% of the landslide-prone areas.

Does Group-Level Commitment Predict Employee Well-Being?: A Prospective Analysis.

PubMed

Clausen, Thomas; Christensen, Karl Bang; Nielsen, Karina

2015-11-01

To investigate the links between group-level affective organizational commitment (AOC) and individual-level psychological well-being, self-reported sickness absence, and sleep disturbances. A total of 5085 care workers from 301 workgroups in the Danish eldercare services participated in both waves of the study (T1 [2005] and T2 [2006]). The three outcomes were analyzed using linear multilevel regression analysis, multilevel Poisson regression analysis, and multilevel logistic regression analysis, respectively. Group-level AOC (T1) significantly predicted individual-level psychological well-being, self-reported sickness absence, and sleep disturbances (T2). The association between group-level AOC (T1) and psychological well-being (T2) was fully mediated by individual-level AOC (T1), and the associations between group-level AOC (T1) and self-reported sickness absence and sleep disturbances (T2) were partially mediated by individual-level AOC (T1). Group-level AOC is an important predictor of employee well-being in contemporary health care organizations.

Comparative study of contrast-enhanced ultrasound qualitative and quantitative analysis for identifying benign and malignant breast tumor lumps.

PubMed

Liu, Jian; Gao, Yun-Hua; Li, Ding-Dong; Gao, Yan-Chun; Hou, Ling-Mi; Xie, Ting

2014-01-01

To compare the value of contrast-enhanced ultrasound (CEUS) qualitative and quantitative analysis in the identification of breast tumor lumps. Qualitative and quantitative indicators of CEUS for 73 cases of breast tumor lumps were retrospectively analyzed by univariate and multivariate approaches. Logistic regression was applied and ROC curves were drawn for evaluation and comparison. The CEUS qualitative indicator-generated regression equation contained three indicators, namely enhanced homogeneity, diameter line expansion and peak intensity grading, which demonstrated prediction accuracy for benign and malignant breast tumor lumps of 91.8%; the quantitative indicator-generated regression equation only contained one indicator, namely the relative peak intensity, and its prediction accuracy was 61.5%. The corresponding areas under the ROC curve for qualitative and quantitative analyses were 91.3% and 75.7%, respectively, which exhibited a statistically significant difference by the Z test (P<0.05). The ability of CEUS qualitative analysis to identify breast tumor lumps is better than with quantitative analysis.

Problem-gambling severity, suicidality and DSM-IV Axis II personality disorders.

PubMed

Ronzitti, Silvia; Kraus, Shane W; Hoff, Rani A; Clerici, Massimo; Potenza, Marc N

2018-07-01

Despite the strong associations between personality disorders and problem/pathological gambling, few studies have investigated the relationships between personality disorders, problem-gambling severity and suicidal thoughts/behaviors. We examined the relationships between problem-gambling severity and personality disorders among individuals with differing levels of suicidality (none, thoughts alone, attempts). We analyzed data from 13,543 participants of the National Epidemiologic Survey of Alcohol and Related Conditions (NESARC) study. First, differences in sociodemographic characteristics and prevalence of personality disorders were analyzed according to problem-gambling severity and suicidality status. Second, we performed a logistic regression to assess among the relationship between problem-gambling severity and DSM-IV Axis II psychopathology according to suicidality level. At-risk or problem/pathological gambling groups showed higher rates of a wide range of personality disorders compared to non-gamblers. Logistic regression showed that at-risk pathological gamblers had a higher odds ratio for any personality disorder in the group with no history of suicidality, particularly for cluster-B personality disorders. Odds ratio interaction analysis identified the relationship between problem-gambling severity and personality disorders, particularly those in cluster B, differ according to suicidality status. Our findings suggest a complex relationship between suicidality, problem-gambling severity and personality disorders. The stronger relationship between problem-gambling severity and personality disorders in people with no suicidality as compared to some suicidality suggests that some of the relationship between greater problem-gambling severity and Axis II psychopathology is accounted for by increased suicidality. The findings have implications for clinical interventions targeting suicidality in individuals with gambling disorders. Published by Elsevier Ltd.

[Correlation factors of lymph node metastasis in patients with clinical stage T1a non-small cell lung cancer].

PubMed

Ruochuan, Zang; Shugeng, Guo; Jie, He; Yousheng, Mao; Qi, Xue; Dali, Wang; Juwei, Mu; Jun, Zhao; Yonggang, Wang; Xiangyang, Liu; Fengwei, Tan; Gefei, Zhao; Qian, Zhang; Moyan, Zhang; Peng, Song

2015-04-01

To explore the relationship between the lymph node metastasis and clinicopathological features in patients with clinical stage T1a non-small cell lung cancer (NSCLC). Clinicopathological data of a total of 418 patients who underwent lobectomy and systematic lymph node dissection were retrospectively analyzed. Logistic regression was used to analyze the relationship between lymph node metastasis and clinicopathological features. Lymph node metastasis was observed in 25 patients. There were 122 patients who were diagnosed as ground glass opacity with no lymph node metastasis. 399 patients had subcarinal dissection, among them 7 patients were found to have lymph node metastasis. Univariate analysis showed that gender, smoking history, diameter of lymph node, ground glass opacity (GGO), differentiation of the tumor and tumor site were the factors affecting lymph node metastasis (all P < 0.05). Logistic regression analysis showed that diameter of lymph node, differentiation of the tumor and the site of lesion were independent risk factors for lymph node metastasis of NSCLC. Tumor in the left lung, poor differentiation, and diameter of lymph nodes ≥ 1 cm on the preoperative CT image are independent risk factors for lymph node metastasis of NSCLC, hence we should pay attention before surgery and systematic lymph node dissection should be done. For patients with poor differentiation and lymph nodes ≥ 1 cm, subcarinal lymph nodes dissection is recommended for the sake of higher possibility of lymph node metastasis. For patients with ground glass opacity ≤ 2 cm, the lymph node metastasis is extremely rare, therefore, selective lymph node dissection is reconmmended.

Clinical and sonographic risk factors and complications of shoulder dystocia - a case-control study with parity and gestational age matched controls.

PubMed

Parantainen, Jukka; Palomäki, Outi; Talola, Nina; Uotila, Jukka

2014-06-01

To examine the clinical risk factors and complications of shoulder dystocia today and to evaluate ultrasound methods predicting it. Retrospective, matched case-control study at a University Hospital with 5000 annual deliveries. The study population consisted of 152 deliveries complicated by shoulder dystocia over a period of 8.5 years (January 2004-June 2012) and 152 controls matched for gestational age and parity. The data was collected from the medical records of mothers and children and analyzed by conditional logistic regression. Incidences and odds ratios were calculated for risk factors and complications. Antenatal ultrasound data was analyzed when available by conditional logistic regression to test for significant differences between study groups. Birthweight (OR 12.1 for ≥4000 g; 95% CI 4.18-35.0) and vacuum extraction (OR 3.98; 95% CI 1.25-12.7) remained the most significant clinical risk factors. Only a trend of an association of pregestational or gestational diabetes was noticed (OR 1.87; 95% CI 0.997-3.495, probability of type II error 51%). Of the complications of shoulder dystocia the incidence of brachial plexus palsies was high (40%). Antenatal ultrasound method based on the difference between abdominal and biparietal diameters had a significant difference between cases and controls. The impact of diabetes as a risk factor has diminished, which may reflect improved screening and treatment. Antenatal ultrasound methods are showing some promise, but the predictive value of ultrasound alone is probably low. Copyright © 2014. Published by Elsevier Ireland Ltd.

Factors associated with internet addiction among school-going adolescents in Vadodara.

PubMed

Prabhakaran, M C Anusha; Patel, V Rajvee; Ganjiwale, D Jaishree; Nimbalkar, M Somashekhar

2016-01-01

The internet is an important modern means of obtaining information and communicating with others which has converted the world into a global village. At the same time, increasing internet use among adolescents is also likely to pose a major public health concern that is internet addiction (IA). The aim was to assess the prevalence of IA among school-going adolescents and factors associated with IA. A cross-sectional study was designed to survey adolescents studying in 8th to 11th standard of five schools of Vadodara. Information regarding sociodemography and various patterns of internet use were obtained using survey forms. IA test (IAT) was used to screen for IA. Descriptive analysis, univariate analysis, and logistic regression were done to analyze the data. Seven hundred and twenty-four participants that completed IAT were analyzed. Internet use prevalence was 98.9%. Prevalence of IA was 8.7%. Male gender, owning a personal device, hours of internet use/day, use of smartphones, permanent login status, use of internet for chatting, making online friends, shopping, watching movies, online gaming, searching information online and instant messaging were found to be associated significantly with IA in univariate analysis. Internet use for online friendships was found to be a significant predictor of IA (odds ratio [OR] =2.4), and internet use for searching information was found to be protective (OR = 0.20) against IA on logistic regression. IA is prevalent in the adolescent population and requires awareness and intervention. Characteristics of internet usage found to be associated with IA needs to be considered while developing strategies for interventions.

Urinary 8-iso-prostaglandin F2α as a marker of metabolic risks in the general Japanese population: The ROAD study.

PubMed

Mure, Kanae; Yoshimura, Noriko; Hashimoto, Marowa; Muraki, Shigeyuki; Oka, Hiroyuki; Tanaka, Sakae; Kawaguchi, Hiroshi; Nakamura, Kozo; Akune, Toru; Takeshita, Tatsuya

2015-07-01

To determine whether 8-iso-prostaglandin F2α (8-iso-PGF2α) is a reliable biomarker of the accumulation of metabolic risks [e.g., overweight, hypertension, impaired glucose tolerance (IGT), and dyslipidemia]. This was a cross-sectional study of the baseline characteristics of a Japanese general population cohort study: Research on Osteoarthritis/Osteoporosis Against Disability (ROAD). Of 1,690 participants, 1,527 fulfilled all questionnaires and examinations. Free and conjugated urinary 8-iso-PGF2α levels and metabolic syndrome (MetS) components including blood pressure, HbA1c, total cholesterol, high-density lipoprotein cholesterol (HDL-C), and non-HDL-C were analyzed. The data were analyzed by ANCOVA, multiple regression analysis, and multinomial logistic analysis. 8-iso-PGF2α was significantly associated with HbA1c and significantly inversely associated with total cholesterol and non-HDL-C. Notably, IGT with an HbA1c cut-off of 5.5% was significantly associated with 8-iso-PGF2α level in participants aged ≤50 years. Multinomial logistic regression analysis revealed 8-iso-PGF2α level was significantly associated with a greater number of MetS risks present; this association was stronger in younger participants. In participants aged ≥71 years, 8-iso-PGF2α was significantly associated with a greater number of MetS risks with higher IGT cut-offs. Urinary 8-iso-PGF2α can be a reliable marker of IGT and the accumulation of MetS risks, especially in younger people. © 2015 The Obesity Society.

Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression.

PubMed

Zhang, Xinyan; Li, Bingzong; Han, Huiying; Song, Sha; Xu, Hongxia; Hong, Yating; Yi, Nengjun; Zhuang, Wenzhuo

2018-05-10

Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.

Factors associated with internet addiction among school-going adolescents in Vadodara

PubMed Central

Prabhakaran, M. C. Anusha; Patel, V. Rajvee; Ganjiwale, D. Jaishree; Nimbalkar, M. Somashekhar

2016-01-01

Introduction: The internet is an important modern means of obtaining information and communicating with others which has converted the world into a global village. At the same time, increasing internet use among adolescents is also likely to pose a major public health concern that is internet addiction (IA). The aim was to assess the prevalence of IA among school-going adolescents and factors associated with IA. Methods: A cross-sectional study was designed to survey adolescents studying in 8th to 11th standard of five schools of Vadodara. Information regarding sociodemography and various patterns of internet use were obtained using survey forms. IA test (IAT) was used to screen for IA. Descriptive analysis, univariate analysis, and logistic regression were done to analyze the data. Results: Seven hundred and twenty-four participants that completed IAT were analyzed. Internet use prevalence was 98.9%. Prevalence of IA was 8.7%. Male gender, owning a personal device, hours of internet use/day, use of smartphones, permanent login status, use of internet for chatting, making online friends, shopping, watching movies, online gaming, searching information online and instant messaging were found to be associated significantly with IA in univariate analysis. Internet use for online friendships was found to be a significant predictor of IA (odds ratio [OR] =2.4), and internet use for searching information was found to be protective (OR = 0.20) against IA on logistic regression. Conclusions: IA is prevalent in the adolescent population and requires awareness and intervention. Characteristics of internet usage found to be associated with IA needs to be considered while developing strategies for interventions. PMID:28348987

Identification of complications in mastectomy with immediate reconstruction using tissue expanders and permanent implants for breast cancer patients.

PubMed

Ota, Daisuke; Fukuuchi, Atsushi; Iwahira, Yoshiko; Kato, Takao; Takeuchi, Masashi; Okamoto, Joji; Nishi, Tsunehiro

2016-05-01

Since complications of postmastectomy breast reconstruction may reduce patient satisfaction, we investigated complications of reconstruction with tissue expanders (TEs), particularly surgical site infections requiring TE/permanent implant (PI) removal. A retrospective review was performed of 234 primary breast cancer patients undergoing 239 postmastectomy breast reconstructions with TEs/PIs from 1997 to 2009. Clinicopathological findings and postoperative complications, particularly infections, were analyzed. Data were analyzed by the Chi-square test and a multivariate logistic regression model. TE infection risk factors considered for model inclusion were excisional biopsy, (neo) adjuvant chemotherapy, lymph node resection, body mass index (BMI), simultaneous bilateral reconstructions, and seroma aspiration. Removal of TEs/PIs was observed in 15.5% (37/239) of reconstructions, and 18/37 underwent re-reconstructions. Of the 19/37 reconstructions that were not achieved completely, the most frequent reason was TE infection (11 reconstructions). The completion rate was 92% (220/239 reconstructions) and it was significantly higher in reconstructions without TE infection than with infection (96 vs. 54%, p < 0.0001). Patients with BMI ≥ 25 kg/m² and seroma aspiration were more likely to develop TE infections (p = 0.0019, p < 0.001, respectively). By multivariate logistic regression analysis, seroma aspiration was a significant independent risk factor for TE infection (odds ratio 28.75, 95% confidence interval 5.71-40.03, p < 0.0001). To improve completion rates of breast reconstruction, prevention of TE infection plays a key role. We should reduce unnecessary seroma aspirations and delay elevation/exercise of the ipsilateral arm.

Factors associated with negative T-SPOT.TB results among smear-negative tuberculosis patients in China.

PubMed

Kang, Wanli; Wu, Meiying; Yang, Kunyun; Ertai, A; Wu, Shucai; Geng, Shujun; Li, Zhihui; Li, Mingwu; Pang, Yu; Tang, Shenjie

2018-03-09

We compared the positive rates of T-SPOT.TB and bacterial culture in the smear-negative PTB, and analyzed the factors affecting the results of negative T-SPOT.TB and bacterial culture. Retrospective evaluation of data from smear-negative PTB patients who underwent T-SPOT.TB and bacterial culture were done. The agreement and concordance were analyzed between T-SPOT.TB and bacterial culture. Multivariable logistic regression analysis was used to explore the factors associated with positive results of T-SPOT.TB and bacterial culture in smear-negative PTB. 858 eligible smear-negative PTB patients were included in the study. The agreement rate was 25.6% (22.7~28.5%) between T-SPOT.TB and bacterial culture in smear- negative PTB patients. The positive rate of T-SPOT.TB was higher than that of bacterial culture in smear-negative PTB patients (p < 0.001). There were nearly no concordance between T-SPOT.TB and bacterial culture (p > 0.05). Using multivariable logistic regression analysis we found that older age ≥ 60 years (OR = 0.469, 95% CI: 0.287-0.768) and decreased albumin (OR = 0.614, 95% CI: 0.380-0.992) were associated with negative diagnostic results of T-SPOT.TB in smear-negative PTB patients. Female (OR = 0.654, 95% CI: 0.431-0.992) were associated with negative diagnostic results of bacteria culture in smear-negative PTB patients. Our results indicated that the older age and decreased albumin were independently associated with negative T-SPOT.TB responses.

Prevalence of Dry Eye Syndrome after a Three-Year Exposure to a Clean Room

PubMed Central

2014-01-01

Objective To measure the prevalence of dry eye syndrome (DES) among clean room (relative humidity ≤1%) workers from 2011 to 2013. Methods Three annual DES examinations were performed completely in 352 clean room workers aged 20–40 years who were working at a secondary battery factory. Each examination comprised the tear-film break-up test (TFBUT), Schirmer’s test I, slit-lamp microscopic examination, and McMonnies questionnaire. DES grades were measured using the Delphi approach. The annual examination results were analyzed using a general linear model and post-hoc analysis with repeated-ANOVA (Tukey). Multiple logistic regression was performed using the examination results from 2013 (dependent variable) to analyze the effect of years spent working in the clean room (independent variable). Results The prevalence of DES among these workers was 14.8% in 2011, 27.1% in 2012, and 32.8% in 2013. The TFBUT and McMonnies questionnaire showed that DES grades worsened over time. Multiple logistic regression analysis indicated that the odds ratio for having dry eyes was 1.130 (95% CI 1.012–1.262) according to the findings of the McMonnies questionnaire. Conclusions This 3-year trend suggests that the increased prevalence of DES was associated with longer working hours. To decrease the prevalence of DES, employees should be assigned reasonable working hours with shift assignments that include appropriate break times. Workers should also wear protective eyewear, subdivide their working process to minimize exposure, and utilize preservative-free eye drops. PMID:25339991

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

PubMed

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

2018-04-01

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

[The related factors of head and neck mocosal melanoma with lymph node metastasis].

PubMed

Yin, G F; Guo, W; Chen, X H; Huang, Z G

2017-12-05

Objective: To investigate the related factors of mucosal melanoma of head and neck with lymph node metastasis for early diagnosis and further treatments. Method: A retrospective analysis of 117 cases of head and neck mucosal malignant melanoma patients which received surgical treatment was performed. Eleven cases of patients with pathologically confirmed lymph node metastasis and 33 cases without lymph node metastasis (1∶3) were randomly selected to analyze. The related factors of lymph node metastasis of head and neck mucosal melanoma patients including age, gender, whether the existence of recurrence, bone invasion, lesion location were analyzed. The single factor and logistic regression analysis were performed, P <0.05 difference was statistically significant. Result: The lymph node metastasis rate of head and neck mucosal melanoma was 9.40%(11/117), the single factor analysis showed that there were 3 factors to be associated with lymph node metastasis, which was recurrence ( P =0.0000), bone invasion ( P =0.001), primary position ( P =0.007). Recurrence ( P =0.021) was a risk factor for lymph node metastasis according to the Logistic regression analysis, and the impact of bone invasion ( P =0.487) and primary location ( P =0.367) remained to be further explored. Conclusion: The patients of head and neck mucosal melanoma with the presence of recurrent usually accompanied by a further progression of the disease, such as lymph node metastasis, so for recurrent patients should pay special attention to the situation of lymph node and choose the reasonable treatment. Copyright© by the Editorial Department of Journal of Clinical Otorhinolaryngology Head and Neck Surgery.

Tobacco advertising, environmental smoking bans, and smoking in Chinese urban areas.

PubMed

Yang, Tingzhong; Rockett, Ian R H; Li, Mu; Xu, Xiaochao; Gu, Yaming

2012-07-01

To evaluate whether cigarette smoking in Chinese urban areas was respectively associated with exposure to tobacco advertising and smoking bans in households, workplaces, and public places. Participants were 4735 urban residents aged 15 years and older, who were identified through multi-stage quota-sampling conducted in six Chinese cities. Data were collected on individual sociodemographics and smoking status, and regional tobacco control measures. The sample was characterized in terms of smoking prevalence, and multilevel logistic models were employed to analyze the association between smoking and tobacco advertising and environmental smoking restrictions, respectively. Smoking prevalence was 30%. Multilevel logistic regression analysis showed that smoking was positively associated with exposure to tobacco advertising, and negatively associated with workplace and household smoking bans. The association of smoking with both tobacco advertising and environmental smoking bans further justifies implementation of comprehensive smoking interventions and tobacco control programs in China. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Zero-Inflated Poisson Modeling of Fall Risk Factors in Community-Dwelling Older Adults.

PubMed

Jung, Dukyoo; Kang, Younhee; Kim, Mi Young; Ma, Rye-Won; Bhandari, Pratibha

2016-02-01

The aim of this study was to identify risk factors for falls among community-dwelling older adults. The study used a cross-sectional descriptive design. Self-report questionnaires were used to collect data from 658 community-dwelling older adults and were analyzed using logistic and zero-inflated Poisson (ZIP) regression. Perceived health status was a significant factor in the count model, and fall efficacy emerged as a significant predictor in the logistic models. The findings suggest that fall efficacy is important for predicting not only faller and nonfaller status but also fall counts in older adults who may or may not have experienced a previous fall. The fall predictors identified in this study--perceived health status and fall efficacy--indicate the need for fall-prevention programs tailored to address both the physical and psychological issues unique to older adults. © The Author(s) 2014.

The effect of obesity on pathological complete response and survival in breast cancer patients receiving uncapped doses of neoadjuvant anthracycline-taxane-based chemotherapy.

PubMed

Farr, Alex; Stolz, Myriam; Baumann, Lukas; Bago-Horvath, Zsuzsanna; Oppolzer, Elisabeth; Pfeiler, Georg; Seifert, Michael; Singer, Christian F

2017-06-01

The effect of obesity in breast cancer patients undergoing neoadjuvant chemotherapy (NAC) remains controversial. The aim of this study was to determine the obesity-related effect on pathological complete response (pCR) and survival in women receiving full uncapped doses of NAC. We retrospectively analyzed the data of all consecutive women who underwent anthracycline-taxane-based NAC for primary breast cancer between 2005 and 2015 at the Department of Obstetrics and Gynecology, Medical University of Vienna. Following the WHO criteria, women with a body mass index (BMI) ≥30 kg/m 2 at baseline were considered obese, whereas those with a BMI <30 kg/m 2 were considered non-obese. Those with dose reductions or dose capping were not eligible for study inclusion. Cox regression and logistic regression were performed. The Kaplan-Meier method was used to analyze disease-free, progression-free, and overall survival. The pCR served as the main outcome measure. Among 120 women who received neoadjuvant epirubicin plus cyclophosphamide and docetaxel, 28 (23.3%) were obese and 92 (76.7%) were non-obese. In the multivariate logistic regression model that adjusted for potentially confounding variables, obesity had an independent positive predictive effect on pCR (OR 4.29, 95% CI, 1.42-13.91; p = 0.011), which was significant in the postmenopausal subgroup (OR 4.72, 95% CI, 1.47-15.84; p = 0.01). When comparing non-obese with obese women, we found that obese women experienced longer progression-free survival (HR 0.10, 95% CI, 8.448 × 10 -4 -0.81; p = 0.025). Obese women receiving full uncapped doses of anthracycline-taxane-based NAC have increased pCR and favorable progression-free survival. This could result from increased dose intensity with increased efficacy and toxicity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Predictive capacity of sperm quality parameters and sperm subpopulations on field fertility after artificial insemination in sheep.

PubMed

Santolaria, P; Vicente-Fiel, S; Palacín, I; Fantova, E; Blasco, M E; Silvestre, M A; Yániz, J L

2015-12-01

This study was designed to evaluate the relevance of several sperm quality parameters and sperm population structure on the reproductive performance after cervical artificial insemination (AI) in sheep. One hundred and thirty-nine ejaculates from 56 adult rams were collected using an artificial vagina, processed for sperm quality assessment and used to perform 1319 AI. Analyses of sperm motility by computer-assisted sperm analysis (CASA), sperm nuclear morphometry by computer-assisted sperm morphometry analysis (CASMA), membrane integrity by acridine orange-propidium iodide combination and sperm DNA fragmentation using the sperm chromatin dispersion test (SCD) were performed. Clustering procedures using the sperm kinematic and morphometric data resulted in the classification of spermatozoa into three kinematic and three morphometric sperm subpopulations. Logistic regression procedures were used, including fertility at AI as the dependent variable (measured by lambing, 0 or 1) and farm, year, month of AI, female parity, female lambing-treatment interval, ram, AI technician and sperm quality parameters (including sperm subpopulations) as independent factors. Sperm quality variables remaining in the logistic regression model were viability and VCL. Fertility increased for each one-unit increase in viability (by a factor of 1.01) and in VCL (by a factor of 1.02). Multiple linear regression analyses were also performed to analyze the factors possibly influencing ejaculate fertility (N=139). The analysis yielded a significant (P<0.05) relationship between sperm viability and ejaculate fertility. The discriminant ability of the different semen variables to predict field fertility was analyzed using receiver operating characteristic (ROC) curve analysis. Sperm viability and VCL showed significant, albeit limited, predictive capacity on field fertility (0.57 and 0.54 Area Under Curve, respectively). The distribution of spermatozoa in the different subpopulations was not related to fertility. Copyright © 2015 Elsevier B.V. All rights reserved.

A Competing Risk Model of First Failure Site after Definitive Chemoradiation Therapy for Locally Advanced Non-Small Cell Lung Cancer.

PubMed

Nygård, Lotte; Vogelius, Ivan R; Fischer, Barbara M; Kjær, Andreas; Langer, Seppo W; Aznar, Marianne C; Persson, Gitte F; Bentzen, Søren M

2018-04-01

The aim of the study was to build a model of first failure site- and lesion-specific failure probability after definitive chemoradiotherapy for inoperable NSCLC. We retrospectively analyzed 251 patients receiving definitive chemoradiotherapy for NSCLC at a single institution between 2009 and 2015. All patients were scanned by fludeoxyglucose positron emission tomography/computed tomography for radiotherapy planning. Clinical patient data and fludeoxyglucose positron emission tomography standardized uptake values from primary tumor and nodal lesions were analyzed by using multivariate cause-specific Cox regression. In patients experiencing locoregional failure, multivariable logistic regression was applied to assess risk of each lesion being the first site of failure. The two models were used in combination to predict probability of lesion failure accounting for competing events. Adenocarcinoma had a lower hazard ratio (HR) of locoregional failure than squamous cell carcinoma (HR = 0.45, 95% confidence interval [CI]: 0.26-0.76, p = 0.003). Distant failures were more common in the adenocarcinoma group (HR = 2.21, 95% CI: 1.41-3.48, p < 0.001). Multivariable logistic regression of individual lesions at the time of first failure showed that primary tumors were more likely to fail than lymph nodes (OR = 12.8, 95% CI: 5.10-32.17, p < 0.001). Increasing peak standardized uptake value was significantly associated with lesion failure (OR = 1.26 per unit increase, 95% CI: 1.12-1.40, p < 0.001). The electronic model is available at http://bit.ly/LungModelFDG. We developed a failure site-specific competing risk model based on patient- and lesion-level characteristics. Failure patterns differed between adenocarcinoma and squamous cell carcinoma, illustrating the limitation of aggregating them into NSCLC. Failure site-specific models add complementary information to conventional prognostic models. Copyright © 2018 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

New robust statistical procedures for the polytomous logistic regression models.

PubMed

Castilla, Elena; Ghosh, Abhik; Martin, Nirian; Pardo, Leandro

2018-05-17

This article derives a new family of estimators, namely the minimum density power divergence estimators, as a robust generalization of the maximum likelihood estimator for the polytomous logistic regression model. Based on these estimators, a family of Wald-type test statistics for linear hypotheses is introduced. Robustness properties of both the proposed estimators and the test statistics are theoretically studied through the classical influence function analysis. Appropriate real life examples are presented to justify the requirement of suitable robust statistical procedures in place of the likelihood based inference for the polytomous logistic regression model. The validity of the theoretical results established in the article are further confirmed empirically through suitable simulation studies. Finally, an approach for the data-driven selection of the robustness tuning parameter is proposed with empirical justifications. © 2018, The International Biometric Society.

Updated logistic regression equations for the calculation of post-fire debris-flow likelihood in the western United States

USGS Publications Warehouse

Staley, Dennis M.; Negri, Jacquelyn A.; Kean, Jason W.; Laber, Jayme L.; Tillery, Anne C.; Youberg, Ann M.

2016-06-30

Wildfire can significantly alter the hydrologic response of a watershed to the extent that even modest rainstorms can generate dangerous flash floods and debris flows. To reduce public exposure to hazard, the U.S. Geological Survey produces post-fire debris-flow hazard assessments for select fires in the western United States. We use publicly available geospatial data describing basin morphology, burn severity, soil properties, and rainfall characteristics to estimate the statistical likelihood that debris flows will occur in response to a storm of a given rainfall intensity. Using an empirical database and refined geospatial analysis methods, we defined new equations for the prediction of debris-flow likelihood using logistic regression methods. We showed that the new logistic regression model outperformed previous models used to predict debris-flow likelihood.

Nowcasting of Low-Visibility Procedure States with Ordered Logistic Regression at Vienna International Airport

NASA Astrophysics Data System (ADS)

Kneringer, Philipp; Dietz, Sebastian; Mayr, Georg J.; Zeileis, Achim

2017-04-01

Low-visibility conditions have a large impact on aviation safety and economic efficiency of airports and airlines. To support decision makers, we develop a statistical probabilistic nowcasting tool for the occurrence of capacity-reducing operations related to low visibility. The probabilities of four different low visibility classes are predicted with an ordered logistic regression model based on time series of meteorological point measurements. Potential predictor variables for the statistical models are visibility, humidity, temperature and wind measurements at several measurement sites. A stepwise variable selection method indicates that visibility and humidity measurements are the most important model inputs. The forecasts are tested with a 30 minute forecast interval up to two hours, which is a sufficient time span for tactical planning at Vienna Airport. The ordered logistic regression models outperform persistence and are competitive with human forecasters.

EXpectation Propagation LOgistic REgRession (EXPLORER): distributed privacy-preserving online model learning.

PubMed

Wang, Shuang; Jiang, Xiaoqian; Wu, Yuan; Cui, Lijuan; Cheng, Samuel; Ohno-Machado, Lucila

2013-06-01

We developed an EXpectation Propagation LOgistic REgRession (EXPLORER) model for distributed privacy-preserving online learning. The proposed framework provides a high level guarantee for protecting sensitive information, since the information exchanged between the server and the client is the encrypted posterior distribution of coefficients. Through experimental results, EXPLORER shows the same performance (e.g., discrimination, calibration, feature selection, etc.) as the traditional frequentist logistic regression model, but provides more flexibility in model updating. That is, EXPLORER can be updated one point at a time rather than having to retrain the entire data set when new observations are recorded. The proposed EXPLORER supports asynchronized communication, which relieves the participants from coordinating with one another, and prevents service breakdown from the absence of participants or interrupted communications. Copyright © 2013 Elsevier Inc. All rights reserved.

A computational approach to compare regression modelling strategies in prediction research.

PubMed

Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H

2016-08-25

It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.

Reconstruction of the Foot and Ankle Using Pedicled or Free Flaps: Perioperative Flap Survival Analysis

PubMed Central

Li, Xiucun; Cui, Jianli; Maharjan, Suraj; Lu, Laijin; Gong, Xu

2016-01-01

Objective The purpose of this study is to determine the correlation between non-technical risk factors and the perioperative flap survival rate and to evaluate the choice of skin flap for the reconstruction of foot and ankle. Methods This was a clinical retrospective study. Nine variables were identified. The Kaplan-Meier method coupled with a log-rank test and a Cox regression model was used to predict the risk factors that influence the perioperative flap survival rate. The relationship between postoperative wound infection and risk factors was also analyzed using a logistic regression model. Results The overall flap survival rate was 85.42%. The necrosis rates of free flaps and pedicled flaps were 5.26% and 20.69%, respectively. According to the Cox regression model, flap type (hazard ratio [HR] = 2.592; 95% confidence interval [CI] (1.606, 4.184); P < 0.001) and postoperative wound infection (HR = 0.266; 95% CI (0.134, 0.529); P < 0.001) were found to be statistically significant risk factors associated with flap necrosis. Based on the logistic regression model, preoperative wound bed inflammation (odds ratio [OR] = 11.371,95% CI (3.117, 41.478), P < 0.001) was a statistically significant risk factor for postoperative wound infection. Conclusion Flap type and postoperative wound infection were both independent risk factors influencing the flap survival rate in the foot and ankle. However, postoperative wound infection was a risk factor for the pedicled flap but not for the free flap. Microvascular anastomosis is a major cause of free flap necrosis. To reconstruct complex or wide soft tissue defects of the foot or ankle, free flaps are safer and more reliable than pedicled flaps and should thus be the primary choice. PMID:27930679

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

PubMed

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

2017-04-25

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

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

PubMed Central

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

2017-01-01

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

Speech prosody impairment predicts cognitive decline in Parkinson's disease.

PubMed

Rektorova, Irena; Mekyska, Jiri; Janousova, Eva; Kostalova, Milena; Eliasova, Ilona; Mrackova, Martina; Berankova, Dagmar; Necasova, Tereza; Smekal, Zdenek; Marecek, Radek

2016-08-01

Impairment of speech prosody is characteristic for Parkinson's disease (PD) and does not respond well to dopaminergic treatment. We assessed whether baseline acoustic parameters, alone or in combination with other predominantly non-dopaminergic symptoms may predict global cognitive decline as measured by the Addenbrooke's cognitive examination (ACE-R) and/or worsening of cognitive status as assessed by a detailed neuropsychological examination. Forty-four consecutive non-depressed PD patients underwent clinical and cognitive testing, and acoustic voice analysis at baseline and at the two-year follow-up. Influence of speech and other clinical parameters on worsening of the ACE-R and of the cognitive status was analyzed using linear and logistic regression. The cognitive status (classified as normal cognition, mild cognitive impairment and dementia) deteriorated in 25% of patients during the follow-up. The multivariate linear regression model consisted of the variation in range of the fundamental voice frequency (F0VR) and the REM Sleep Behavioral Disorder Screening Questionnaire (RBDSQ). These parameters explained 37.2% of the variability of the change in ACE-R. The most significant predictors in the univariate logistic regression were the speech index of rhythmicity (SPIR; p = 0.012), disease duration (p = 0.019), and the RBDSQ (p = 0.032). The multivariate regression analysis revealed that SPIR alone led to 73.2% accuracy in predicting a change in cognitive status. Combining SPIR with RBDSQ improved the prediction accuracy of SPIR alone by 7.3%. Impairment of speech prosody together with symptoms of RBD predicted rapid cognitive decline and worsening of PD cognitive status during a two-year period. Copyright © 2016 Elsevier Ltd. All rights reserved.

Self-reported work ability and work performance in workers with chronic nonspecific musculoskeletal pain.

PubMed

de Vries, Haitze J; Reneman, Michiel F; Groothoff, Johan W; Geertzen, Jan H B; Brouwer, Sandra

2013-03-01

To assess self-reported work ability and work performance of workers who stay at work despite chronic nonspecific musculoskeletal pain (CMP), and to explore which variables were associated with these outcomes. In a cross-sectional study we assessed work ability (Work Ability Index, single item scale 0-10) and work performance (Health and Work Performance Questionnaire, scale 0-10) among 119 workers who continued work while having CMP. Scores of work ability and work performance were categorized into excellent (10), good (9), moderate (8) and poor (0-7). Hierarchical multiple regression and logistic regression analysis was used to analyze the relation of socio-demographic, pain-related, personal- and work-related variables with work ability and work performance. Mean work ability and work performance were 7.1 and 7.7 (poor to moderate). Hierarchical multiple regression analysis revealed that higher work ability scores were associated with lower age, better general health perception, and higher pain self-efficacy beliefs (R(2) = 42 %). Higher work performance was associated with lower age, higher pain self-efficacy beliefs, lower physical work demand category and part-time work (R(2) = 37 %). Logistic regression analysis revealed that work ability ≥8 was significantly explained by age (OR = 0.90), general health perception (OR = 1.04) and pain self-efficacy (OR = 1.15). Work performance ≥8 was explained by pain self-efficacy (OR = 1.11). Many workers with CMP who stay at work report poor to moderate work ability and work performance. Our findings suggest that a subgroup of workers with CMP can stay at work with high work ability and performance, especially when they have high beliefs of pain self-efficacy. Our results further show that not the pain itself, but personal and work-related factors relate to work ability and work performance.

Cytopathologic differential diagnosis of low-grade urothelial carcinoma and reactive urothelial proliferation in bladder washings: a logistic regression analysis.

PubMed

Cakir, Ebru; Kucuk, Ulku; Pala, Emel Ebru; Sezer, Ozlem; Ekin, Rahmi Gokhan; Cakmak, Ozgur

2017-05-01

Conventional cytomorphologic assessment is the first step to establish an accurate diagnosis in urinary cytology. In cytologic preparations, the separation of low-grade urothelial carcinoma (LGUC) from reactive urothelial proliferation (RUP) can be exceedingly difficult. The bladder washing cytologies of 32 LGUC and 29 RUP were reviewed. The cytologic slides were examined for the presence or absence of the 28 cytologic features. The cytologic criteria showing statistical significance in LGUC were increased numbers of monotonous single (non-umbrella) cells, three-dimensional cellular papillary clusters without fibrovascular cores, irregular bordered clusters, atypical single cells, irregular nuclear overlap, cytoplasmic homogeneity, increased N/C ratio, pleomorphism, nuclear border irregularity, nuclear eccentricity, elongated nuclei, and hyperchromasia (p ˂ 0.05), and the cytologic criteria showing statistical significance in RUP were inflammatory background, mixture of small and large urothelial cells, loose monolayer aggregates, and vacuolated cytoplasm (p ˂ 0.05). When these variables were subjected to a stepwise logistic regression analysis, four features were selected to distinguish LGUC from RUP: increased numbers of monotonous single (non-umbrella) cells, increased nuclear cytoplasmic ratio, hyperchromasia, and presence of small and large urothelial cells (p = 0.0001). By this logistic model of the 32 cases with proven LGUC, the stepwise logistic regression analysis correctly predicted 31 (96.9%) patients with this diagnosis, and of the 29 patients with RUP, the logistic model correctly predicted 26 (89.7%) patients as having this disease. There are several cytologic features to separate LGUC from RUP. Stepwise logistic regression analysis is a valuable tool for determining the most useful cytologic criteria to distinguish these entities. © 2017 APMIS. Published by John Wiley & Sons Ltd.

Science of Test Research Consortium: Year Two Final Report

DTIC Science & Technology

2012-10-02

July 2012. Analysis of an Intervention for Small Unmanned Aerial System ( SUAS ) Accidents, submitted to Quality Engineering, LQEN-2012-0056. Stone... Systems Engineering. Wolf, S. E., R. R. Hill, and J. J. Pignatiello. June 2012. Using Neural Networks and Logistic Regression to Model Small Unmanned ...Human Retina. 6. Wolf, S. E. March 2012. Modeling Small Unmanned Aerial System Mishaps using Logistic Regression and Artificial Neural Networks. 7

Logistic quantile regression provides improved estimates for bounded avian counts: a case study of California Spotted Owl fledgling production

Treesearch

Brian S. Cade; Barry R. Noon; Rick D. Scherer; John J. Keane

2017-01-01

Counts of avian fledglings, nestlings, or clutch size that are bounded below by zero and above by some small integer form a discrete random variable distribution that is not approximated well by conventional parametric count distributions such as the Poisson or negative binomial. We developed a logistic quantile regression model to provide estimates of the empirical...

Comparison of four methods for deriving hospital standardised mortality ratios from a single hierarchical logistic regression model.

PubMed

Mohammed, Mohammed A; Manktelow, Bradley N; Hofer, Timothy P

2016-04-01

There is interest in deriving case-mix adjusted standardised mortality ratios so that comparisons between healthcare providers, such as hospitals, can be undertaken in the controversial belief that variability in standardised mortality ratios reflects quality of care. Typically standardised mortality ratios are derived using a fixed effects logistic regression model, without a hospital term in the model. This fails to account for the hierarchical structure of the data - patients nested within hospitals - and so a hierarchical logistic regression model is more appropriate. However, four methods have been advocated for deriving standardised mortality ratios from a hierarchical logistic regression model, but their agreement is not known and neither do we know which is to be preferred. We found significant differences between the four types of standardised mortality ratios because they reflect a range of underlying conceptual issues. The most subtle issue is the distinction between asking how an average patient fares in different hospitals versus how patients at a given hospital fare at an average hospital. Since the answers to these questions are not the same and since the choice between these two approaches is not obvious, the extent to which profiling hospitals on mortality can be undertaken safely and reliably, without resolving these methodological issues, remains questionable. © The Author(s) 2012.

Three methods to construct predictive models using logistic regression and likelihood ratios to facilitate adjustment for pretest probability give similar results.

PubMed

Chan, Siew Foong; Deeks, Jonathan J; Macaskill, Petra; Irwig, Les

2008-01-01

To compare three predictive models based on logistic regression to estimate adjusted likelihood ratios allowing for interdependency between diagnostic variables (tests). This study was a review of the theoretical basis, assumptions, and limitations of published models; and a statistical extension of methods and application to a case study of the diagnosis of obstructive airways disease based on history and clinical examination. Albert's method includes an offset term to estimate an adjusted likelihood ratio for combinations of tests. Spiegelhalter and Knill-Jones method uses the unadjusted likelihood ratio for each test as a predictor and computes shrinkage factors to allow for interdependence. Knottnerus' method differs from the other methods because it requires sequencing of tests, which limits its application to situations where there are few tests and substantial data. Although parameter estimates differed between the models, predicted "posttest" probabilities were generally similar. Construction of predictive models using logistic regression is preferred to the independence Bayes' approach when it is important to adjust for dependency of tests errors. Methods to estimate adjusted likelihood ratios from predictive models should be considered in preference to a standard logistic regression model to facilitate ease of interpretation and application. Albert's method provides the most straightforward approach.

A comparison of three methods of assessing differential item functioning (DIF) in the Hospital Anxiety Depression Scale: ordinal logistic regression, Rasch analysis and the Mantel chi-square procedure.

PubMed

Cameron, Isobel M; Scott, Neil W; Adler, Mats; Reid, Ian C

2014-12-01

It is important for clinical practice and research that measurement scales of well-being and quality of life exhibit only minimal differential item functioning (DIF). DIF occurs where different groups of people endorse items in a scale to different extents after being matched by the intended scale attribute. We investigate the equivalence or otherwise of common methods of assessing DIF. Three methods of measuring age- and sex-related DIF (ordinal logistic regression, Rasch analysis and Mantel χ(2) procedure) were applied to Hospital Anxiety Depression Scale (HADS) data pertaining to a sample of 1,068 patients consulting primary care practitioners. Three items were flagged by all three approaches as having either age- or sex-related DIF with a consistent direction of effect; a further three items identified did not meet stricter criteria for important DIF using at least one method. When applying strict criteria for significant DIF, ordinal logistic regression was slightly less sensitive. Ordinal logistic regression, Rasch analysis and contingency table methods yielded consistent results when identifying DIF in the HADS depression and HADS anxiety scales. Regardless of methods applied, investigators should use a combination of statistical significance, magnitude of the DIF effect and investigator judgement when interpreting the results.

Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Ling, Wing-Kuen; Zhao, Huimin; Marshall, Stephen

2017-12-01

Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.

Latin hypercube approach to estimate uncertainty in ground water vulnerability

USGS Publications Warehouse

Gurdak, J.J.; McCray, J.E.; Thyne, G.; Qi, S.L.

2007-01-01

A methodology is proposed to quantify prediction uncertainty associated with ground water vulnerability models that were developed through an approach that coupled multivariate logistic regression with a geographic information system (GIS). This method uses Latin hypercube sampling (LHS) to illustrate the propagation of input error and estimate uncertainty associated with the logistic regression predictions of ground water vulnerability. Central to the proposed method is the assumption that prediction uncertainty in ground water vulnerability models is a function of input error propagation from uncertainty in the estimated logistic regression model coefficients (model error) and the values of explanatory variables represented in the GIS (data error). Input probability distributions that represent both model and data error sources of uncertainty were simultaneously sampled using a Latin hypercube approach with logistic regression calculations of probability of elevated nonpoint source contaminants in ground water. The resulting probability distribution represents the prediction intervals and associated uncertainty of the ground water vulnerability predictions. The method is illustrated through a ground water vulnerability assessment of the High Plains regional aquifer. Results of the LHS simulations reveal significant prediction uncertainties that vary spatially across the regional aquifer. Additionally, the proposed method enables a spatial deconstruction of the prediction uncertainty that can lead to improved prediction of ground water vulnerability. ?? 2007 National Ground Water Association.

Pregnancy outcomes among female hairdressers who participated in the Danish National Birth Cohort.

PubMed

Zhu, Jin Liang; Vestergaard, Mogens; Hjollund, Niels Henrik; Olsen, Jørn

2006-02-01

The Danish National Birth Cohort (DNBC) was used to examine pregnancy outcomes among female hairdressers and neurodevelopment in their offspring. A population-based cohort study was conducted of 550 hairdressers and 3216 shop assistants (reference group) by using data from the Danish National Birth Cohort between 1997 and 2003. Information on job characteristics was reported by the women in the first interview (around 17 weeks of gestation). Pregnancy outcomes were obtained by linkage to the national registers. Developmental milestones were reported by the mother at the fourth interview, when the child was approximately 19 months old. Cox regression was applied to analyze fetal loss and congenital malformation. Logistic regression was used to analyze other pregnancy outcomes and developmental milestones. We found no significant differences in fetal loss, multiple births, gender ratio, preterm birth, small-for-gestational age, congenital malformations, or achievement of developmental milestones among the children of hairdressers and shop assistants. The results do not indicate that children of hairdressers in Denmark currently have a high risk of fetal impairment or delayed psychomotor development.

Examining the long-term racial disparities in health and economic conditions among Hurricane Katrina survivors: policy implications for Gulf Coast recovery.

PubMed

Toldson, Ivory A; Ray, Kilynda; Hatcher, Schnavia Smith; Louis, Laura Straughn

2011-01-01

This study examines disparities in the long-term health, emotional well-being, and economic consequences of the 2005 Gulf Coast hurricanes. Researchers analyzed the responses of 216 Black and 508 White Hurricane Katrina survivors who participated in the ABC News Hurricane Katrina Anniversary Poll in 2006. Self-reported data of the long-term negative impact of the hurricane on personal health, emotional well-being, and finances were regressed on race, income, and measures of loss, injury, family mortality, anxiety, and confidence in the government. Descriptive analyses, stepwise logistic regression, and analyses of variance revealed that Black hurricane survivors more frequently reported hurricane-related problems with personal health, emotional well-being, and finances. In addition, Blacks were more likely than Whites to report the loss of friends, relatives, and personal property.

Association of comorbid mental health symptoms and physical health conditions with employee productivity.

PubMed

Parker, Kristin M; Wilson, Mark G; Vandenberg, Robert J; DeJoy, David M; Orpinas, Pamela

2009-10-01

This study tests the hypothesis that employees with comorbid physical health conditions and mental health symptoms are less productive than other employees. Self-reported health status and productivity measures were collected from 1723 employees of a national retail organization. chi2, analysis of variance, and linear contrast analyses were conducted to evaluate whether health status groups differed on productivity measures. Multivariate linear regression and multinomial logistic regression analyses were conducted to analyze how predictive health status was of productivity. Those with comorbidities were significantly less productive on all productivity measures compared with all other health status groups and those with only physical health conditions or mental health symptoms. Health status also significantly predicted levels of employee productivity. These findings provide evidence for the relationship between health statuses and productivity, which has potential programmatic implications.

Multiple network-constrained regressions expand insights into influenza vaccination responses.

PubMed

Avey, Stefan; Mohanty, Subhasis; Wilson, Jean; Zapata, Heidi; Joshi, Samit R; Siconolfi, Barbara; Tsang, Sui; Shaw, Albert C; Kleinstein, Steven H

2017-07-15

Systems immunology leverages recent technological advancements that enable broad profiling of the immune system to better understand the response to infection and vaccination, as well as the dysregulation that occurs in disease. An increasingly common approach to gain insights from these large-scale profiling experiments involves the application of statistical learning methods to predict disease states or the immune response to perturbations. However, the goal of many systems studies is not to maximize accuracy, but rather to gain biological insights. The predictors identified using current approaches can be biologically uninterpretable or present only one of many equally predictive models, leading to a narrow understanding of the underlying biology. Here we show that incorporating prior biological knowledge within a logistic modeling framework by using network-level constraints on transcriptional profiling data significantly improves interpretability. Moreover, incorporating different types of biological knowledge produces models that highlight distinct aspects of the underlying biology, while maintaining predictive accuracy. We propose a new framework, Logistic Multiple Network-constrained Regression (LogMiNeR), and apply it to understand the mechanisms underlying differential responses to influenza vaccination. Although standard logistic regression approaches were predictive, they were minimally interpretable. Incorporating prior knowledge using LogMiNeR led to models that were equally predictive yet highly interpretable. In this context, B cell-specific genes and mTOR signaling were associated with an effective vaccination response in young adults. Overall, our results demonstrate a new paradigm for analyzing high-dimensional immune profiling data in which multiple networks encoding prior knowledge are incorporated to improve model interpretability. The R source code described in this article is publicly available at https://bitbucket.org/kleinstein/logminer . steven.kleinstein@yale.edu or stefan.avey@yale.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Determinants of postnatal care utilization in urban community among women in Debre Birhan Town, Northern Shewa, Ethiopia.

PubMed

Angore, Banchalem Nega; Tufa, Efrata Girma; Bisetegen, Fithamlak Solomon

2018-04-19

Reducing maternal mortality and improving maternal health care through increased utilization of postnatal care utilization is a global and local priority. However studies that have been carried out in Ethiopia regarding determinants are limited. So This study aims to assess the magnitude of postnatal care utilization and its determinants in Debre Birhan Town, North Ethiopia. A community-based cross-sectional study was conducted from March 1 to April 25, 2015, in Debre Birhan Town. Data were collected through face-to-face interviews using structured pre-tested questionnaires. The data were entered and cleaned in Epi Info version 3.5 and analyzed using SPSS version 20. Bivariate and multiple logistic regression analyses were used. Variable with p value less than or equal to 0.2 at bivariate analysis were entered into multiple logistic regression. Significance was declared at 0.05 in multiple logistic regressions and considered to be an independent factor. From the total respondents, we found that 327 (83.3%) mothers utilized the postnatal care services. Single mothers were less likely to utilize postnatal care services than those mothers who are married and live together [adjusted odds ratio (AOR) = 0.06, 95% CI (0.01, 0.45)]. This study revealed that respondent's knowledge about postnatal care services is an important predictor of postnatal care utilization [AOR = 0.03, 95% CI (0.00, 0.44)] and mothers who delivered in a health care facility were more likely to receive PNC than mothers who did not deliver in a health care facility [AOR = 0.65, 95% CI (0.58, 0.94)]. The postnatal care utilization rate in Debre Birhan town was 83.3%. Marital status, maternal knowledge, and place of delivery were predictors of postnatal care service utilization. So specific attention should be directed towards the improvement of women's education since the perception of the need for PNC services were positively correlated with the mother's education.

Genetic and clinical risk factors of new-onset diabetes after transplantation in Hispanic kidney transplant recipients.

PubMed

Yang, Jaewook; Hutchinson, Ian I; Shah, Tariq; Min, David I

2011-05-27

New-onset diabetes after transplantation (NODAT) is one of the major complications after transplantation and is associated with reduced overall patient and graft survival. The objective of this study was to determine the genetic and clinical risk factors for NODAT in Hispanic kidney transplant recipients. Hispanic kidney allograft recipients without evidence of preexisting diabetes who developed NODAT (n=133) were studied using Hispanic kidney transplant recipients with no evidence of diabetes as a control group (n=170). NODAT was defined as fasting glucose levels ≥126 mg/dL on two or more occasions or patients taking any insulin or oral hypoglycemic agents 1 month or later after kidney transplantation. Fourteen alleles in nine genes were genotyped and other patients' clinical data with genotype data were analyzed by logistic regression. Among 14 alleles, hepatocyte nuclear factor 4 alpha (HNF4A) AA (rs2144908, odds ratio [OR]=1.96, confidence interval [CI]=1.08-3.50, P=0.010), HNF4A TT (rs1884614, OR=2.44, CI=1.42-4.48, P=0.002), and insulin receptor substrate 1 AA+AG (rs1801278, OR=2.71, CI=1.16-6.89, P=0.021) remained significant after logistic regression. Among the clinical factors, average age (OR=1.01, CI=1.00-1.08, P=0.048), sirolimus (OR=5.36, CI=3.02-10.4, P=0.001), deceased donor (OR=1.96, CI=1.16-2.94, P=0.015), and acute rejection (OR=2.92, CI=1.31-5.77, P=0.009) remained significant after logistic regression. This study indicates that polymorphism of two alleles of HNF-4A gene (rs2144908 and rs1884614) and insulin receptor substrate 1 (rs1801278) are significantly associated with NODAT in kidney transplant patients with Hispanic ethnicity. In the case of clinical factors, older age (>50 year), deceased donor type, acute rejection, and sirolimus use are associated with NODAT in Hispanic kidney transplant recipients.

Risk Factors for Venous Thromboembolism After Spine Surgery

PubMed Central

Tominaga, Hiroyuki; Setoguchi, Takao; Tanabe, Fumito; Kawamura, Ichiro; Tsuneyoshi, Yasuhiro; Kawabata, Naoya; Nagano, Satoshi; Abematsu, Masahiko; Yamamoto, Takuya; Yone, Kazunori; Komiya, Setsuro

2015-01-01

Abstract The efficacy and safety of chemical prophylaxis to prevent the development of deep venous thrombosis (DVT) or pulmonary embolism (PE) following spine surgery are controversial because of the possibility of epidural hematoma formation. Postoperative venous thromboembolism (VTE) after spine surgery occurs at a frequency similar to that seen after joint operations, so it is important to identify the risk factors for VTE formation following spine surgery. We therefore retrospectively studied data from patients who had undergone spinal surgery and developed postoperative VTE to identify those risk factors. We conducted a retrospective clinical study with logistic regression analysis of a group of 80 patients who had undergone spine surgery at our institution from June 2012 to August 2013. All patients had been screened by ultrasonography for DVT in the lower extremities. Parameters of the patients with VTE were compared with those without VTE using the Mann–Whitney U-test and Fisher exact probability test. Logistic regression analysis was used to analyze the risk factors associated with VTE. A value of P < 0.05 was used to denote statistical significance. The prevalence of VTE was 25.0% (20/80 patients). One patient had sensed some incongruity in the chest area, but the vital signs of all patients were stable. VTEs had developed in the pulmonary artery in one patient, in the superficial femoral vein in one patient, in the popliteal vein in two patients, and in the soleal vein in 18 patients. The Mann–Whitney U-test and Fisher exact probability test showed that, except for preoperative walking disability, none of the parameters showed a significant difference between patients with and without VTE. Risk factors identified in the multivariate logistic regression analysis were preoperative walking disability and age. The prevalence of VTE after spine surgery was relatively high. The most important risk factor for developing postoperative VTE was preoperative walking disability. Gait training during the early postoperative period is required to prevent VTE. PMID:25654385

Psychosocial predictors of breast self-examination behavior among female students: an application of the health belief model using logistic regression.

PubMed

Didarloo, Alireza; Nabilou, Bahram; Khalkhali, Hamid Reza

2017-11-03

Breast cancer is a life-threatening condition affecting women around the world. The early detection of breast lumps using a breast self-examination (BSE) is important for the prevention and control of this disease. The aim of this study was to examine BSE behavior and its predictive factors among female university students using the Health Belief Model (HBM). This investigation was a cross-sectional survey carried out with 334 female students at Urmia University of Medical Sciences in the northwest of Iran. To collect the necessary data, researchers applied a valid and reliable three-part questionnaire. The data were analyzed using descriptive statistics and a chi-square test, in addition to multivariate logistic regression statistics in SPSS software version 16.0 (SPSS Inc., Chicago, IL, USA). The results indicated that 82 of the 334 participants (24.6%) reported practicing BSEs. Multivariate logistic regression analyses showed that high perceived severity [OR = 2.38, 95% CI = (1.02-5.54)], high perceived benefits [OR = 1.94, 95% CI = (1.09-3.46)], and high perceived self-efficacy [OR = 13.15, 95% CI = (3.64-47.51)] were better predictors of BSE behavior (P < 0.05) than low perceived severity, benefits, and self-efficacy. The findings also showed that a high level of knowledge compared to a low level of knowledge [OR = 5.51, 95% CI = (1.79-16.86)] and academic undergraduate and graduate degrees compared to doctoral degrees [OR = 2.90, 95% CI = (1.42-5.92)] of the participants were predictors of BSE performance (P < 0.05). The study revealed that the HBM constructs are able to predict BSE behavior. Among these constructs, self-efficacy was the most important predictor of the behavior. Interventions based on the constructs of perceived self-efficacy, benefits, and severity are recommended for increasing women's regular screening for breast cancer.

A case-control study of determinants for high and low dental caries prevalence in Nevada youth

PubMed Central

2010-01-01

Background The main purpose of this study was to compare the 30% of Nevada Youth who presented with the highest Decayed Missing and Filled Teeth (DMFT) index to a cohort who were caries free and to national NHANES data. Secondly, to explore the factors associated with higher caries prevalence in those with the highest DMFT scores compared to the caries-free group. Methods Over 4000 adolescents between ages 12 and 19 (Case Group: N = 2124; Control Group: N = 2045) received oral health screenings conducted in public/private middle and high schools in Nevada in 2008/2009 academic year. Caries prevalence was computed (Untreated decay scores [D-Score] and DMFT scores) for the 30% of Nevada Youth who presented with the highest DMFT score (case group) and compared to the control group (caries-free) and to national averages. Bivariate and multivariate logistic regression was used to analyze the relationship between selected variables and caries prevalence. Results A majority of the sample was non-Hispanic (62%), non-smokers (80%), and had dental insurance (70%). With the exception of gender, significant differences in mean D-scores were found in seven of the eight variables. All variables produced significant differences between the case and control groups in mean DMFT Scores. With the exception of smoking status, there were significant differences in seven of the eight variables in the bivariate logistic regression. All of the independent variables remained in the multivariate logistic regression model contributing significantly to over 40% of the variation in the increased DMFT status. The strongest predictors for the high DMFT status were racial background, age, fluoridated community, and applied sealants respectively. Gender, second hand smoke, insurance status, and tobacco use were significant, but to a lesser extent. Conclusions Findings from this study will aid in creating educational programs and other primary and secondary interventions to help promote oral health for Nevada youth, especially focusing on the subgroup that presents with the highest mean DMFT scores. PMID:21067620

Beyond logistic regression: structural equations modelling for binary variables and its application to investigating unobserved confounders.

PubMed

Kupek, Emil

2006-03-15

Structural equation modelling (SEM) has been increasingly used in medical statistics for solving a system of related regression equations. However, a great obstacle for its wider use has been its difficulty in handling categorical variables within the framework of generalised linear models. A large data set with a known structure among two related outcomes and three independent variables was generated to investigate the use of Yule's transformation of odds ratio (OR) into Q-metric by (OR-1)/(OR+1) to approximate Pearson's correlation coefficients between binary variables whose covariance structure can be further analysed by SEM. Percent of correctly classified events and non-events was compared with the classification obtained by logistic regression. The performance of SEM based on Q-metric was also checked on a small (N = 100) random sample of the data generated and on a real data set. SEM successfully recovered the generated model structure. SEM of real data suggested a significant influence of a latent confounding variable which would have not been detectable by standard logistic regression. SEM classification performance was broadly similar to that of the logistic regression. The analysis of binary data can be greatly enhanced by Yule's transformation of odds ratios into estimated correlation matrix that can be further analysed by SEM. The interpretation of results is aided by expressing them as odds ratios which are the most frequently used measure of effect in medical statistics.

Predictors of postoperative outcomes of cubital tunnel syndrome treatments using multiple logistic regression analysis.

PubMed

Suzuki, Taku; Iwamoto, Takuji; Shizu, Kanae; Suzuki, Katsuji; Yamada, Harumoto; Sato, Kazuki

2017-05-01

This retrospective study was designed to investigate prognostic factors for postoperative outcomes for cubital tunnel syndrome (CubTS) using multiple logistic regression analysis with a large number of patients. Eighty-three patients with CubTS who underwent surgeries were enrolled. The following potential prognostic factors for disease severity were selected according to previous reports: sex, age, type of surgery, disease duration, body mass index, cervical lesion, presence of diabetes mellitus, Workers' Compensation status, preoperative severity, and preoperative electrodiagnostic testing. Postoperative severity of disease was assessed 2 years after surgery by Messina's criteria which is an outcome measure specifically for CubTS. Bivariate analysis was performed to select candidate prognostic factors for multiple linear regression analyses. Multiple logistic regression analysis was conducted to identify the association between postoperative severity and selected prognostic factors. Both bivariate and multiple linear regression analysis revealed only preoperative severity as an independent risk factor for poor prognosis, while other factors did not show any significant association. Although conflicting results exist regarding prognosis of CubTS, this study supports evidence from previous studies and concludes early surgical intervention portends the most favorable prognosis. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

Recurrence of superficial vein thrombosis in patients with varicose veins.

PubMed

Karathanos, Christos; Spanos, Konstantinos; Saleptsis, Vassileios; Tsezou, Aspasia; Kyriakou, Despina; Giannoukas, Athanasios D

2016-08-01

To investigate which factors other than history of superficial vein thrombosis (SVT) are associated with recurrent spontaneous SVT episodes in patients with varicose veins (VVs). Patients with a history of spontaneous SVT and VVs were followed up for a mean period of 55 months. Demographics, comorbidities, and thrombophilia screening test were analyzed. Patients were grouped according to the clinical-etiology-anatomy-pathophysiology classification. A multiple logistic regression analysis with the forward likelihood ratio method was undertaken. Thirteen patients out of 97 had a recurrence SVT episode during the follow-up period. All those patients were identified to have a thrombophilia defect. Protein C and S, antithrombin, and plasminogen deficiencies were more frequently present in patients without recurrence. Gene mutations were present in 38% in the nonrecurrence group and 77% in the recurrence group. After logistic regression analysis, patients with dislipidemia and mutation in prothrombin G20210A (FII) had an increased risk for recurrence by 5.4-fold and 4.6-fold, respectively. No deep vein thrombosis or pulmonary embolism occurred. Dislipidemia and gene mutations of F II are associated with SVT recurrence in patients with VVs. A selection of patients may benefit from anticoagulation in the short term and from VVs intervention in the long term. © The Author(s) 2015.

Lack of motivation for treatment associated with greater care needs and psychosocial problems.

PubMed

Stobbe, Jolanda; Wierdsma, Andre I; Kok, Rob M; Kroon, Hans; Depla, Marja; Roosenschoon, Bert-Jan; Mulder, Cornelis L

2013-01-01

To compare the care needs and severity of psychosocial problems in older patients with severe mental illness (SMI) between those who were and were not motivated for treatment. Cross-sectional study in which we enrolled 141 outpatients with SMI aged 55 and older. Needs were measured using the Camberwell Assessment of Needs for the Elderly, and psychosocial problems with the Health of the Nation Outcome Scale 65+. Motivation for treatment was assessed using a motivation-for-change scale. Parametric and non-parametric tests were used to analyze differences between motivated and non-motivated patients. Explorative logistic regression analyses were used to establish, which unmet needs were associated with motivation. Less-motivated patients had greater unmet care needs and more psychosocial problems than those who were motivated. Logistic regression analyses showed that lack of motivation was associated with greater unmet needs regarding daytime activities, psychotic symptoms, behavioral problems, and addiction problems. Lack of treatment motivation was associated with more unmet needs and more severe psychosocial problems. Further research will be needed to identify other factors associated with motivation in older people with SMI and to investigate whether this group of patient benefits from interventions such as assertive outreach, integrated care or treatment-adherence therapy.

The correlations of psychological status, quality of life, self-esteem, social support and body image disturbance in Chinese patients with Systemic Lupus Erythematosus.

PubMed

Zhao, Qian; Chen, Haoyang; Yan, Hongyan; He, Yan; Zhu, Li; Fu, WenTing; Shen, Biyu

2018-01-31

This study aimed (i) to complement existing research by focusing on body image disturbance issues in Chinese Systemic Lupus Erythematosus (SLE) patients; (ii) to investigate how Chinese patients make sense of disease diagnosis and perceived cultural influences within the context of their SLE. A total of 118 SLE patients underwent standardized laboratory examinations and completed several questionnaires. Independent sample t-test, Mann-Whitney U-test, Chi-square test, and multivariate analysis using backward stepwise logistic regression model were used to analyze these data. We found 18.3% SLE patients had BID, which were significantly higher than the control group (.8%). SLE patients are more concerned about their physical changes caused by disease. There were significant correlations among personal health insurance, complication of diabetes, appearance of new rash, depression, anxiety, self-esteem and BID in patients with SLE. Meanwhile, logistic regression analysis revealed that appearance of new rash and high anxiety were significantly associated with BID in SLE patients. In conclusion, it is beneficial to pay attention to the physical and mental health of patients with rheumatic disease from the perspective of body image, to understand their needs and to provide effective and effective service for them.

Next day discharge rate has little use as a quality measure for individual physician performance.

PubMed

Inabnit, Christopher; Markwell, Stephen; Gruwell, Jack; Jaeger, Cassie; Millburg, Lance; Griffen, David

2018-06-18

Emergency Department (ED) physicians' next day discharge rate (NDDR), the percentage of patients who were admitted from the ED and subsequently discharged within the next calendar day was hypothesized as a potential measure for unnecessary admissions. The objective was to determine if NDDR has validity as a measure for quality of individual ED physician performance. Hospital admission data was obtained for thirty-six ED physicians for calendar year 2015. Funnel plots were used to identify NDDR outliers beyond 95% control limits. A mixed model logistic regression was built to investigate factors contributing to NDDR. To determine yearly variation, data from calendar years 2014 and 2016 were analyzed, again by funnel plots and logistic regression. Intraclass correlation coefficient was used to estimate the percent of total variation in NDDR attributable to individual ED physicians. NDDR varied significantly among ED physicians. Individual ED physician outliers in NDDR varied year to year. Individual ED physician contribution to NDDR variation was minimal, accounting for 1%. Years of experience in Emergency Medicine practice was not correlated with NDDR. NDDR does not appear to be a reliable independent quality measure for individual ED physician performance. The percent of variance attributable to the ED physician was 1%. Copyright © 2018. Published by Elsevier Inc.

Impact of preoperative antithrombotic therapy on blood management after implantation of primary total knee arthroplasty

PubMed Central

Leitner, Lukas; Musser, Ewald; Kastner, Norbert; Friesenbichler, Jörg; Hirzberger, Daniela; Radl, Roman; Leithner, Andreas; Sadoghi, Patrick

2016-01-01

Red blood cell concentrates (RCC) substitution after total knee arthroplasty (TKA) is correlated with multifold of complications and an independent predictor for higher postoperative mortality. TKA is mainly performed in elderly patients with pre-existing polymorbidity, often requiring permanent preoperative antithrombotic therapy (PAT). The aim of this retrospective analysis was to investigate the impact of demand for PAT on inpatient blood management in patients undergoing TKA. In this study 200 patients were retrospectively evaluated after TKA for differences between PAT and non-PAT regarding demographic parameters, preoperative ASA score > 2, duration of operation, pre-, and intraoperative hemoglobin level, and postoperative parameters including amount of wound drainage, RCC requirement, and inpatient time. In a multivariate logistic regression analysis the independent influences of PAT, demographic parameters, ASA score > 2, and duration of the operation on RCC demand following TKA were analyzed. Patients with PAT were significantly older, more often had an ASA > 2 at surgery, needed a higher number of RCCs units and more frequently and had lower perioperative hemoglobin levels. Multivariate logistic regression revealed PAT was an independent predictor for RCC requirement. PAT patients are more likely to require RCC following TKA and should be accurately monitored with respect to postoperative blood loss. PMID:27488941

Impact of preoperative antithrombotic therapy on blood management after implantation of primary total knee arthroplasty.

PubMed

Leitner, Lukas; Musser, Ewald; Kastner, Norbert; Friesenbichler, Jörg; Hirzberger, Daniela; Radl, Roman; Leithner, Andreas; Sadoghi, Patrick

2016-08-04

Red blood cell concentrates (RCC) substitution after total knee arthroplasty (TKA) is correlated with multifold of complications and an independent predictor for higher postoperative mortality. TKA is mainly performed in elderly patients with pre-existing polymorbidity, often requiring permanent preoperative antithrombotic therapy (PAT). The aim of this retrospective analysis was to investigate the impact of demand for PAT on inpatient blood management in patients undergoing TKA. In this study 200 patients were retrospectively evaluated after TKA for differences between PAT and non-PAT regarding demographic parameters, preoperative ASA score > 2, duration of operation, pre-, and intraoperative hemoglobin level, and postoperative parameters including amount of wound drainage, RCC requirement, and inpatient time. In a multivariate logistic regression analysis the independent influences of PAT, demographic parameters, ASA score > 2, and duration of the operation on RCC demand following TKA were analyzed. Patients with PAT were significantly older, more often had an ASA > 2 at surgery, needed a higher number of RCCs units and more frequently and had lower perioperative hemoglobin levels. Multivariate logistic regression revealed PAT was an independent predictor for RCC requirement. PAT patients are more likely to require RCC following TKA and should be accurately monitored with respect to postoperative blood loss.

Trail impacts in Sagarmatha (Mt. Everest) National Park, Nepal: a logistic regression analysis.

PubMed

Nepal, S K

2003-09-01

A trail study was conducted in the Sagarmatha (Mt. Everest) National Park, Nepal, during 1997-1998. Based on that study, this paper examines the spatial variability of trail conditions and analyzes factors that influence trail conditions. Logistic regression (multinomial logit model) is applied to examine the influence of use and environmental factors on trail conditions. The assessment of trail conditions is based on a four-class rating system: (class I, very little damaged; class II, moderately damaged, class III, heavily damaged; and class IV, severely damaged). Wald statistics and a model classification table have been used for data interpretation. Results indicate that altitude, trail gradient, hazard potential, and vegetation type are positively associated with trail condition. Trails are more degraded at higher altitude, on steep gradients, in areas with natural hazard potential, and within shrub/grassland zones. Strong correlations between high levels of trail degradation and higher frequencies of visitors and lodges were found. A detailed analysis of environmental and use factors could provide valuable information to park managers in their decisions about trail design, layout and maintenance, and efficient and effective visitor management strategies. Comparable studies on high alpine environments are needed to predict precisely the effects of topographic and climatic extremes. More refined approaches and experimental methods are necessary to control the effects of environmental factors.

Cognitive Factors Related to Drug Abuse Among a Sample of Iranian Male Medical College Students

PubMed Central

Jalilian, Farzad; Ataee, Mari; Matin, Behzad Karami; Ahmadpanah, Mohammad; Jouybari, Touraj Ahmadi; Eslami, Ahmad Ali; Mahboubi, Mohammad; Alavijeh, Mehdi Mirzaei

2015-01-01

Backgrounds: Drug abuse is one of the most serious social problems in many countries. College students, particularly at their first year of education, are considered as one of the at risk groups for drug abuse. The present study aimed to determine cognitive factors related to drug abuse among a sample of Iranian male medical college students based on the social cognitive theory (SCT). Method: This cross-sectional study was carried out on 425 Iranian male medical college students who were randomly selected to participate voluntarily in the study. The participants filled out a self-administered questionnaire. Data were analyzed by the SPSS software (ver. 21.0) using bivariate correlations, logistic and linear regression at 95% significant level. Results: Attitude, outcome expectation, outcome expectancies, subjective norms, and self-control were cognitive factors that accounted for 49% of the variation in the outcome measure of the intention to abuse drugs. Logistic regression showed that attitude (OR=1.062), outcome expectancies (OR=1.115), and subjective norms (OR=1.269) were the most influential predictors for drug abuse. Conclusions: The findings suggest that designing and implementation of educational programs may be useful to increase negative attitude, outcome expectancies, and subjective norms towards drug abuse for college students in order to prevent drug abuse. PMID:26156919

Serum antioxidant vitamins and the risk of oral cancer in patients seen at a tertiary institution in Nigeria.

PubMed

Lawal, A O; Kolude, B; Adeyemi, B F; Lawoyin, J O; Akang, E E

2012-01-01

Tobacco and alcohol are major risk factors of oral cancer, but nutritional deficiency may also contribute to development of oral cancer. This study compared serum antioxidant vitamin levels in oral cancer patients and controls in order to validate the role of vitamin deficiencies in the etiology of oral cancer. Serum vitamin A, C, and E levels of 33 oral cancer patients and 30 controls at University College Hospital, Ibadan, Nigeria, were determined using standard methods. The data obtained were analyzed using the Student t-test, odds ratio, and logistic regression. Mean vitamin A, C, and E levels were significantly lower in oral cancer patients (P=0.022, P=0.000, and P=0.013 respectively). Risk of oral cancer was 10.89, 11.35, and 5.6 times more in patients with low serum vitamins A, C, and E, respectively. However, on logistic regression analysis, only low serum vitamin E independently predicted occurrence of oral cancer. The lower serum vitamin A, C, and E levels in oral cancer patients could be either a cause or an effect of the oral cancer. Further studies using a larger sample size and cohort studies with long-term follow-up of subjects are desirable.

Landslide-susceptibility analysis using light detection and ranging-derived digital elevation models and logistic regression models: a case study in Mizunami City, Japan

NASA Astrophysics Data System (ADS)

Wang, Liang-Jie; Sawada, Kazuhide; Moriguchi, Shuji

2013-01-01

To mitigate the damage caused by landslide disasters, different mathematical models have been applied to predict landslide spatial distribution characteristics. Although some researchers have achieved excellent results around the world, few studies take the spatial resolution of the database into account. Four types of digital elevation model (DEM) ranging from 2 to 20 m derived from light detection and ranging technology to analyze landslide susceptibility in Mizunami City, Gifu Prefecture, Japan, are presented. Fifteen landslide-causative factors are considered using a logistic-regression approach to create models for landslide potential analysis. Pre-existing landslide bodies are used to evaluate the performance of the four models. The results revealed that the 20-m model had the highest classification accuracy (71.9%), whereas the 2-m model had the lowest value (68.7%). In the 2-m model, 89.4% of the landslide bodies fit in the medium to very high categories. For the 20-m model, only 83.3% of the landslide bodies were concentrated in the medium to very high classes. When the cell size decreases from 20 to 2 m, the area under the relative operative characteristic increases from 0.68 to 0.77. Therefore, higher-resolution DEMs would provide better results for landslide-susceptibility mapping.

High-Density Lipoprotein Cholesterol, Blood Urea Nitrogen, and Serum Creatinine Can Predict Severe Acute Pancreatitis.

PubMed

Hong, Wandong; Lin, Suhan; Zippi, Maddalena; Geng, Wujun; Stock, Simon; Zimmer, Vincent; Xu, Chunfang; Zhou, Mengtao

2017-01-01

Early prediction of disease severity of acute pancreatitis (AP) would be helpful for triaging patients to the appropriate level of care and intervention. The aim of the study was to develop a model able to predict Severe Acute Pancreatitis (SAP). A total of 647 patients with AP were enrolled. The demographic data, hematocrit, High-Density Lipoprotein Cholesterol (HDL-C) determinant at time of admission, Blood Urea Nitrogen (BUN), and serum creatinine (Scr) determinant at time of admission and 24 hrs after hospitalization were collected and analyzed statistically. Multivariate logistic regression indicated that HDL-C at admission and BUN and Scr at 24 hours (hrs) were independently associated with SAP. A logistic regression function (LR model) was developed to predict SAP as follows: -2.25-0.06 HDL-C (mg/dl) at admission + 0.06 BUN (mg/dl) at 24 hours + 0.66 Scr (mg/dl) at 24 hours. The optimism-corrected c-index for LR model was 0.832 after bootstrap validation. The area under the receiver operating characteristic curve for LR model for the prediction of SAP was 0.84. The LR model consists of HDL-C at admission and BUN and Scr at 24 hours, representing an additional tool to stratify patients at risk of SAP.

Neck and upper extremity symptoms among male dentists and pharmacists.

PubMed

Aminian, Omid; Alemohammad, Zahra Banafsheh; Hosseini, Mohammad Hashem

2015-01-01

There are many studies discussed about musculoskeletal disorders in dentists, but most of them do not have a control group. The aim of this study was to assess neck and upper limb symptoms in male dentists in comparison with pharmacists. In this cross-sectional study, 252 male general dentists compared with 188 male general pharmacists with Standardized Nordic Questionnaire. Subjects were at least one year in clinical practice after becoming qualified. The data were analyzed using a series of univariate and multivariate analysis. Having at least one neck or upper extremity symptom in the past 12 months (OR = 3.2, P< 0.001) was reported by 76.2% of the male dentists and 50.0% of the male pharmacists In logistic regression analyses, with adjustments for occupation, age, body mass index, smoking, working years and weekly work hours, there was a significant association between dentistry and 12-month period prevalence symptoms of neck (OR = 2.136), shoulder (OR = 2.059) and elbow (OR = 4.167). Second logistic regression model in male dentists indicated that working years was negatively related to self-reported symptoms of neck, shoulder and hand. Male dentists are at risk of developing musculoskeletal disorders in the neck and upper extremities more than male pharmacists.

Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

PubMed Central

Zeng, Fangfang; Li, Zhongtao; Yu, Xiaoling; Zhou, Linuo

2013-01-01

Background This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. Methods and Materials We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses. Conclusion The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset. PMID:23940593

Profiling healthy eaters. Determining factors that predict healthy eating practices among Dutch adults.

PubMed

Swan, Emily; Bouwman, Laura; Hiddink, Gerrit Jan; Aarts, Noelle; Koelen, Maria

2015-06-01

Research has identified multiple factors that predict unhealthy eating practices. However what remains poorly understood are factors that promote healthy eating practices. This study aimed to determine a set of factors that represent a profile of healthy eaters. This research applied Antonovsky's salutogenic framework for health development to examine a set of factors that predict healthy eating in a cross-sectional study of Dutch adults. Data were analyzed from participants (n = 703) who completed the study's survey in January 2013. Logistic regression analysis was performed to test the association of survey factors on the outcome variable high dietary score. In the multivariate logistic regression model, five factors contributed significantly (p < .05) to the predictive ability of the overall model: being female; living with a partner; a strong sense of coherence (construct from the salutogenic framework), flexible restraint of eating, and self-efficacy for healthy eating. Findings complement what is already known of the factors that relate to poor eating practices. This can provide nutrition promotion with a more comprehensive picture of the factors that both support and hinder healthy eating practices. Future research should explore these factors to better understand their origins and mechanisms in relation to healthy eating practices. Copyright © 2015 Elsevier Ltd. All rights reserved.

Knowledge and perception on tuberculosis transmission in Tanzania: Multinomial logistic regression analysis of secondary data.

PubMed

Ismail, Abbas; Josephat, Peter

2014-01-01

Tuberculosis (TB) is one of the most important public health problems in Tanzania and was declared as a national public health emergency in 2006. Community and individual knowledge and perceptions are critical factors in the control of the disease. The objective of this study was to analyze the knowledge and perception on the transmission of TB in Tanzania. Multinomial Logistic Regression analysis was considered in order to quantify the impact of knowledge and perception on TB. The data used was adopted as secondary data from larger national survey 2007-08 Tanzania HIV/AIDS and Malaria Indicator Survey. The findings across groups revealed that knowledge on TB transmission increased with an increase in age and level of education. People in rural areas had less knowledge regarding tuberculosis transmission compared to urban areas [OR = 0.7]. People with the access to radio [OR = 1.7] were more knowledgeable on tuberculosis transmission compared to those who did not have access to radio. People who did not have telephone [OR = 0.6] were less knowledgeable on tuberculosis route of transmission compared to those who had telephone. The findings showed that socio-demographic factors such as age, education, place of residence and owning telephone or radio varied systematically with knowledge on tuberculosis transmission.

Computed tomography pulmonary embolism index for the assessment of survival in patients with pulmonary embolism.

PubMed

Pech, Maciej; Wieners, Gero; Dul, Przemyslaw; Fischbach, Frank; Dudeck, Oliver; Lopez Hänninen, Enrique; Ricke, Jens

2007-08-01

This study was an analysis of the correlation between pulmonary embolism (PE) and patient survival. Among 694 consecutive patients referred to our institution with clinical suspicion of acute PE who underwent CT pulmonary angiography, 188 patients comprised the study group: 87 women (46.3%, median age: 60.7; age range: 19-88 years) and 101 men (53.7%, median age: 66.9; age range: 21-97 years). PE was assessed by two radiologist who were blinded to the results from the follow-up. A PE index was derived for each set of images on the basis of the embolus size and location. Results were analyzed using logistic regression, and correlation with risk factors and patient outcome (survival or death) was calculated. We observed no significant correlation between the CTPE index and patient outcome (p = 0.703). The test of logistic regression with the sum of heart and liver disease or presence of cancer was significantly (p< 0.05) correlated with PE and overall patient outcome. Interobserver agreement showed a significant correlation rate for the assessment of the PE index (0.993; p< 0.001). In our study the CT PE index did not translate into patient outcome. Prospective larger scale studies are needed to confirm the predictive value of the index and refine the index criteria.

Parental Body Mass Index Is Associated With Adolescent Obesity in Taiwan.

PubMed

Chen, Chen-Mei; Lou, Meei-Fang; Gau, Bih-Shya

2016-12-01

Adolescent obesity is a crucial public health concern, and understanding its risk factors can facilitate the establishment of prevention policies. In this study we investigated the prevalence of adolescent obesity in Taiwan, determined the influential factors, and compared the prevalence of obesity in our study population with international indices. The cross-sectional study was an analysis of data from the 2010-2011 Nutrition and Health Survey in Taiwan, an anthropometric measurement and questionnaire survey of adolescents aged 11-18 years. Our sample was 1,826 adolescents (910 males and 916 females). Data were analyzed using logistic regression modeling. Based on body mass index standards specific to Taiwan norms, the prevalence of overweight and obesity in Taiwan adolescents was 12.4% and 16.8%, respectively. The prevalence was lower when international indices of overweight and obesity were applied. In logistic regression, obesity was linked to male gender, an obese father, overweight or obese mother, poor dietary attitudes, and perceived low dietary benefits. Monitoring and preventing adolescent obesity should focus on both adolescents and their parents. When planning behavioral change and education for adolescent obesity, health professionals and policy-makers should view the family as a unit. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Pregnancy and Race/Ethnicity as Predictors of Motivation for Drug Treatment

PubMed Central

Mitchell, Mary M.; Severtson, S. Geoff; Latimer, William W.

2009-01-01

While drug use during pregnancy represents substantial obstetrical risks to mother and baby, little research has examined motivation for drug treatment among pregnant women. We analyzed data collected between 2000 and 2007 from 149 drug-using women located in Baltimore, Maryland. We hypothesized that pregnant drug-using women would be more likely than their non-pregnant counterparts to express greater motivation for treatment. Also, we explored race/ethnicity differences in motivation for treatment. Propensity scores were used to match a sample of 49 pregnant drug-using women with 100 non-pregnant drug-using women. A factor analysis using 11 items from a readiness for treatment scale was used to create a dichotomous outcome variable representing higher and lower levels of motivation for treatment. The first logistic regression model indicated that pregnant women were more than four times as likely as non-pregnant women to express greater motivation for treatment. The second logistic regression analysis indicated a significant interaction between pregnancy status and race/ethnicity, such that white pregnant women were nearly eight times as likely as African-American pregnant women to score higher on the motivation for treatment measure. These results suggest that African-American pregnant drug-using women should be targeted for interventions that increase their motivation for treatment. PMID:18584569

Factors Associated with Suicidal Ideation and Suicide Attempt among School-Going Urban Adolescents in Peru

PubMed Central

Sharma, Bimala; Nam, Eun Woo; Kim, Ha Yun; Kim, Jong Koo

2015-01-01

The study examines the prevalence of suicidal ideation and suicide attempt, and associated factors among school-going urban adolescents in Peru. A cross-sectional survey was conducted in a sample of 916 secondary school adolescents in 2014. A structured questionnaire adapted from Global School-based Student Health Survey was used to obtain information. Data were analyzed using logistic regression models at 5% level of significance. Overall, 26.3% reported having suicidal ideation, and 17.5% reported having attempted suicide during the past 12 months. Multivariate logistic regression analysis showed that female sex, being in a fight, being insulted, being attacked, perceived unhappiness, smoking and sexual intercourse initiation were significantly associated with increased risk of suicidal ideation, while female sex, being in a fight, being insulted, being attacked, perceived unhappiness, alcohol and illicit drug use were related to suicide attempt. The prevalence of suicidal ideation and suicide attempts observed in the survey area is relatively high. Female adolescents are particularly vulnerable to report suicidal ideation and suicide attempt. Interventions that address the issue of violence against adolescents, fighting with peers, health risk behaviors particularly initiation of smoking, alcohol and illicit drug use and encourage supportive role of parents may reduce the risk of suicidal behaviors. PMID:26610536

Subjective vs objective evaluations of smile esthetics.

PubMed

Schabel, Brian J; Franchi, Lorenzo; Baccetti, Tiziano; McNamara, James A

2009-04-01

The aim of this study was to analyze the relationships between subjective evaluations of posttreatment smiles captured with clinical photography and rated by a panel of orthodontists and parents of orthodontic patients, and objective evaluations of the same smiles from the Smile Mesh program (TDG Computing, Philadelphia, Pa). The clinical photographs of 48 orthodontically treated patients were rated by a panel of 25 experienced orthodontists and 20 parents of patients. Independent samples t tests were used to test whether objective measurements were significantly different between subjects with "attractive" and "unattractive" smiles, and those with the "most attractive" and "least attractive" smiles. Additionally, logistic regression was performed to evaluate whether the measurements could predict whether a smile captured with clinical photography would be attractive or unattractive. The comparison between groups showed no significant differences for any measurement. Subjects with the "most unattractive" smiles had a significantly greater distance between the incisal edge of the maxillary central incisors and the lower lip during smiling, and a significantly smaller smile index than did those with the "most attractive" smiles. As shown by the coefficients of logistic regression, smile attractiveness could not be predicted by any objectively gathered measurement. No objective measure of the smile could predict attractive or unattractive smiles as judged subjectively.

[Diabetic Foot Neuropathy and Related Factors in Patients With Type 2 Diabetes Mellitus].

PubMed

Chen, Tzu-Yu; Lin, Chia-Huei; Chang, Yue-Cune; Wang, Chih-Hsin; Hung, Yi-Jen; Tzeng, Wen-Chii

2018-06-01

Patients with type 2 diabetes mellitus (T2DM) face a higher risk of diabetic foot neuropathy, which increases the risk of death. The early detection of factors that influence diabetic neuropathy reduces the risk of foot lesions, including foot ulcerations, lower extremity amputation, and mortality. To explore the demographic, disease-characteristic, health-literacy, and foot-self-care-behavior factors that affect diabetic foot neuropathy in patients with T2DM. A case-control study design was employed in which cases (Michigan Neuropathy Screening Instrument, MNSI) ≥ 2 were matched to controls based on age and gender in a medical center. A total of 114 patients diagnosed with T2DM in a medical center were recruited as participants. Data were collected using a structured questionnaire. The collected data were analyzed using Fisher's exact test, Mann-Whitney U test, and logistic regression. The results of multiple logistic regression showed that glycated hemoglobin (B = 1.696, p = .041) and communication and critical health literacy (B = -0.082, p = .034) were significant factors of diabetic foot neuropathy. The findings of this study suggest that nurses should assess the health literacy of patients with T2DM before providing health education and should develop a specific foot-care intervention for individuals with poor glycemic control.

Trace element analysis of rough diamond by LA-ICP-MS: a case of source discrimination?

PubMed

Dalpé, Claude; Hudon, Pierre; Ballantyne, David J; Williams, Darrell; Marcotte, Denis

2010-11-01

Current profiling of rough diamond source is performed using different physical and/or morphological techniques that require strong knowledge and experience in the field. More recently, chemical impurities have been used to discriminate diamond source and with the advance of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) empirical profiling of rough diamonds is possible to some extent. In this study, we present a LA-ICP-MS methodology that we developed for analyzing ultra-trace element impurities in rough diamond for origin determination ("profiling"). Diamonds from two sources were analyzed by LA-ICP-MS and were statistically classified by accepted methods. For the two diamond populations analyzed in this study, binomial logistic regression produced a better overall correct classification than linear discriminant analysis. The results suggest that an anticipated matrix match reference material would improve the robustness of our methodology for forensic applications. © 2010 American Academy of Forensic Sciences.

Evaluating the perennial stream using logistic regression in central Taiwan

NASA Astrophysics Data System (ADS)

Ruljigaljig, T.; Cheng, Y. S.; Lin, H. I.; Lee, C. H.; Yu, T. T.

2014-12-01

This study produces a perennial stream head potential map, based on a logistic regression method with a Geographic Information System (GIS). Perennial stream initiation locations, indicates the location of the groundwater and surface contact, were identified in the study area from field survey. The perennial stream potential map in central Taiwan was constructed using the relationship between perennial stream and their causative factors, such as Catchment area, slope gradient, aspect, elevation, groundwater recharge and precipitation. Here, the field surveys of 272 streams were determined in the study area. The areas under the curve for logistic regression methods were calculated as 0.87. The results illustrate the importance of catchment area and groundwater recharge as key factors within the model. The results obtained from the model within the GIS were then used to produce a map of perennial stream and estimate the location of perennial stream head.

The use of logistic regression to enhance risk assessment and decision making by mental health administrators.

PubMed

Menditto, Anthony A; Linhorst, Donald M; Coleman, James C; Beck, Niels C

2006-04-01

Development of policies and procedures to contend with the risks presented by elopement, aggression, and suicidal behaviors are long-standing challenges for mental health administrators. Guidance in making such judgments can be obtained through the use of a multivariate statistical technique known as logistic regression. This procedure can be used to develop a predictive equation that is mathematically formulated to use the best combination of predictors, rather than considering just one factor at a time. This paper presents an overview of logistic regression and its utility in mental health administrative decision making. A case example of its application is presented using data on elopements from Missouri's long-term state psychiatric hospitals. Ultimately, the use of statistical prediction analyses tempered with differential qualitative weighting of classification errors can augment decision-making processes in a manner that provides guidance and flexibility while wrestling with the complex problem of risk assessment and decision making.

Victimization and health risk factors among weapon-carrying youth.

PubMed

Stayton, Catherine; McVeigh, Katharine H; Olson, E Carolyn; Perkins, Krystal; Kerker, Bonnie D

2011-11-01

To compare health risks of 2 subgroups of weapon carriers: victimized and nonvictimized youth. 2003-2007 NYC Youth Risk Behavior Surveys were analyzed using bivariate analyses and multinomial logistic regression. Among NYC teens, 7.5% reported weapon carrying without victimization; 6.9% reported it with victimization. Both subgroups were more likely than non-weapon carriers to binge drink, use marijuana, smoke, fight, and have multiple sex partners; weapon carriers with victimization also experienced persistent sadness and attempted suicide. Subgroups of weapon carriers have distinct profiles. Optimal response should pair disciplinary action with screening for behavioral and mental health concerns and victimization.

When the Cop Is the Victim: A Test of Target Congruence Theory on Intimate Partner Violence Victimization Experienced by Police Officers.

PubMed

Zavala, Egbert

2017-05-01

This study analyzed data from the Police Stress and Domestic Violence in Police Families in Baltimore, Maryland, 1997-1999 ( N = 753) to examine propositions derived from target congruence theory in the context of intimate partner violence (IPV) victimization experienced by police officers. Specifically, this study tested the influence of target vulnerability, target gratifiability, and target antagonism on IPV victimization. Results from logistic regression models showed that all three theoretical constructs positively and significantly predicted IPV victimization. Results, as well as the study's limitations and directions for future research, are discussed.

An application in identifying high-risk populations in alternative tobacco product use utilizing logistic regression and CART: a heuristic comparison.

PubMed

Lei, Yang; Nollen, Nikki; Ahluwahlia, Jasjit S; Yu, Qing; Mayo, Matthew S

2015-04-09

Other forms of tobacco use are increasing in prevalence, yet most tobacco control efforts are aimed at cigarettes. In light of this, it is important to identify individuals who are using both cigarettes and alternative tobacco products (ATPs). Most previous studies have used regression models. We conducted a traditional logistic regression model and a classification and regression tree (CART) model to illustrate and discuss the added advantages of using CART in the setting of identifying high-risk subgroups of ATP users among cigarettes smokers. The data were collected from an online cross-sectional survey administered by Survey Sampling International between July 5, 2012 and August 15, 2012. Eligible participants self-identified as current smokers, African American, White, or Latino (of any race), were English-speaking, and were at least 25 years old. The study sample included 2,376 participants and was divided into independent training and validation samples for a hold out validation. Logistic regression and CART models were used to examine the important predictors of cigarettes + ATP users. The logistic regression model identified nine important factors: gender, age, race, nicotine dependence, buying cigarettes or borrowing, whether the price of cigarettes influences the brand purchased, whether the participants set limits on cigarettes per day, alcohol use scores, and discrimination frequencies. The C-index of the logistic regression model was 0.74, indicating good discriminatory capability. The model performed well in the validation cohort also with good discrimination (c-index = 0.73) and excellent calibration (R-square = 0.96 in the calibration regression). The parsimonious CART model identified gender, age, alcohol use score, race, and discrimination frequencies to be the most important factors. It also revealed interesting partial interactions. The c-index is 0.70 for the training sample and 0.69 for the validation sample. The misclassification rate was 0.342 for the training sample and 0.346 for the validation sample. The CART model was easier to interpret and discovered target populations that possess clinical significance. This study suggests that the non-parametric CART model is parsimonious, potentially easier to interpret, and provides additional information in identifying the subgroups at high risk of ATP use among cigarette smokers.

Should the poor have no medicines to cure? A study on the association between social class and social security among the rural migrant workers in urban China.

PubMed

Guan, Ming

2017-11-07

The rampant urbanization and medical marketization in China have resulted in increased vulnerabilities to health and socioeconomic disparities among the rural migrant workers in urban China. In the Chinese context, the socioeconomic characteristics of rural migrant workers have attracted considerable research attention in the recent past years. However, to date, no previous studies have explored the association between the socioeconomic factors and social security among the rural migrant workers in urban China. This study aims to explore the association between socioeconomic inequity and social security inequity and the subsequent associations with medical inequity and reimbursement rejection. Data from a regionally representative sample of 2009 Survey of Migrant Workers in Pearl River Delta in China were used for analyses. Multiple logistic regressions were used to analyze the impacts of socioeconomic factors on the eight dimensions of social security (sick pay, paid leave, maternity pay, medical insurance, pension insurance, occupational injury insurance, unemployment insurance, and maternity insurance) and the impacts of social security on medical reimbursement rejection. The zero-inflated negative binomial regression model (ZINB regression) was adopted to explore the relationship between socioeconomic factors and hospital visits among the rural migrant workers with social security. The study population consisted of 848 rural migrant workers with high income who were young and middle-aged, low-educated, and covered by social security. Reimbursement rejection and abusive supervision for the rural migrant workers were observed. Logistic regression analysis showed that there were significant associations between socioeconomic factors and social security. ZINB regression showed that there were significant associations between socioeconomic factors and hospital visits among the rural migrant workers. Also, several dimensions of social security had significant associations with reimbursement rejections. This study showed that social security inequity, medical inequity, and reimbursement inequity happened to the rural migrant workers simultaneously. Future policy should strengthen health justice and enterprises' medical responsibilities to the employed rural migrant workers.