Inference Methods for the Conditional Logistic Regression Model with Longitudinal Data

2008 ◽  
Vol 50 (1) ◽  
pp. 97-109 ◽  
Author(s):  
Radu V. Craiu ◽  
Thierry Duchesne ◽  
Daniel Fortin
Keyword(s):  
Logistic Regression ◽  
Longitudinal Data ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Conditional Logistic Regression ◽  
Conditional Logistic Regression Model ◽  
Inference Methods

scholarly journals Sparse estimation for case–control studies with multiple disease subtypes

Biostatistics ◽  
2020 ◽  
Author(s):  
Nadim Ballout ◽  
Cedric Garcia ◽  
Vivian Viallon
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multinomial Logistic Regression ◽  
Conditional Logistic Regression ◽  
Case Control ◽  
Case Control Studies ◽  
Conditional Logistic Regression Model ◽  
Symmetric Formulation ◽  
Disease Subtypes

Summary The analysis of case–control studies with several disease subtypes is increasingly common, e.g. in cancer epidemiology. For matched designs, a natural strategy is based on a stratified conditional logistic regression model. Then, to account for the potential homogeneity among disease subtypes, we adapt the ideas of data shared lasso, which has been recently proposed for the estimation of stratified regression models. For unmatched designs, we compare two standard methods based on $L_1$-norm penalized multinomial logistic regression. We describe formal connections between these two approaches, from which practical guidance can be derived. We show that one of these approaches, which is based on a symmetric formulation of the multinomial logistic regression model, actually reduces to a data shared lasso version of the other. Consequently, the relative performance of the two approaches critically depends on the level of homogeneity that exists among disease subtypes: more precisely, when homogeneity is moderate to high, the non-symmetric formulation with controls as the reference is not recommended. Empirical results obtained from synthetic data are presented, which confirm the benefit of properly accounting for potential homogeneity under both matched and unmatched designs, in terms of estimation and prediction accuracy, variable selection and identification of heterogeneities. We also present preliminary results from the analysis of a case–control study nested within the EPIC (European Prospective Investigation into Cancer and nutrition) cohort, where the objective is to identify metabolites associated with the occurrence of subtypes of breast cancer.

scholarly journals Analysis of comparable academic performance: panel logistic regression model

2010 ◽  
Vol 51 ◽  
Author(s):  
Marijus Radavičius ◽  
Pavel Samusenko
Keyword(s):  
Logistic Regression ◽  
Academic Performance ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Conditional Logistic Regression ◽  
Related Factors ◽  
Conditional Logistic Regression Model ◽  
Student Academic Performance

Conditional logistic regression model is fitted to data of student academic performance. This enables one to compare difficulties of different examinations and to identify related factors.

scholarly journals A Conditional Logistic Regression Model for Analyzing Unemployment Rates in West Java

2021 ◽  
Vol 5 (1) ◽  
pp. 195-204
Author(s):  
Dwi Jayanti ◽  
Septian P Palupi ◽  
Khairil Anwar Notodiputro
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Conditional Logistic Regression ◽  
Complex Problem ◽  
Matched Pair ◽  
West Java ◽  
Conditional Logistic Regression Model ◽  
Unemployment Rates ◽  
Determinant Factors

Unemployment is a critical problem faced by developing countries.  It is a complex problem which creates other social and economic problems such as poverty, economic gaps, and crimes. This paper discusses the determinant factors of unemployment rates based on empirical data using the conditional logistic regression model.  The model was used to analyze matched pair data using gender, age and residence as matching factors.  The result showed that household status, marriage status, as well as levels of education were the determinant factors of a person being unemployed in West Java.  It is also shown that the conditional logistic regression outperformed the standard logistic regression for analyzing the cause of unemployment.

scholarly journals The risk of flax versus straw bedding on ileal impaction in colic horses: Retrospective analysis of 2336 cases (2008-2017)

2018 ◽  
Vol 87 (5) ◽  
pp. 255-262 ◽  
Author(s):  
A. Dufourni ◽  
A. Decloedt ◽  
L. Lefère ◽  
D. De Clercq ◽  
P. Deprez ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Body Weight ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Cox Regression ◽  
Conditional Logistic Regression ◽  
Large Animal ◽  
Multivariable Logistic Regression Model ◽  
Multivariable Logistic Regression ◽  
Flax Shives

While mature coastal bermudagrass hay is strongly associated with ileal impaction in the Southeastern United States, stabling on flax bedding has anecdotally been associated with this condition in Europe. The aim of this retrospective study was to investigate the association between ileal impaction and the use of flax shives compared to straw as bedding in horses with colic. Medical records of 2336 referral cases evaluated for abdominal pain between January 2008 and May 2017 at the Department of Large Animal Internal Medicine, Ghent University were reviewed. Diagnosis, date of admission, age, breed, gender, body weight and stable bedding were recorded. Conditional logistic regression analysis was used to assess the association between ileal impaction and each individual variable. Odds ratios (OR) and 95% confidence intervals (CI) were determined. Predictors with a value of P < 0.2 were included in a multivariable Cox regression model and Wald’s test was used to assess parameter estimate significance. Further, the association between survival to discharge and type of bedding or type of treatment (medical versus surgical) was analyzed for horses with ileal impactions. The proportion of colic cases stabled on flax bedding at home was 11.3%. The overall prevalence of ileal impaction was 4.2%. In the flax group, the prevalence of ileal impaction was 9.4% as opposed to 3.6% within the straw group. The OR of 2.8 (95% CI 1.7-4.7; P < 0.001) in the multivariable logistic regression model indicated that horses stabled on flax shives were approximately three times more likely to have ileal impactions than horses stabled on straw. There was no significant association found between ileal impaction and the period of admission, age, gender or body weight in a multivariable logistic regression model. The odds for having ileal impaction is approximately six times (OR 6.3; 95% CI 2.4-16.4; P < 0.001) higher in draft horses than in warmbloods in the multivariable logistic regression model. No significant association was found between survival to discharge and type of bedding or treatment. These results suggest that horses with colic that were housed on flax bedding are more likely to present ileal impactions than horses housed on straw.

scholarly journals Analysis of student WEB survey

2011 ◽  
Vol 52 ◽  
Author(s):  
Gediminas Murauskas ◽  
Marijus Radavičius
Keyword(s):  
Logistic Regression ◽  
Logistic Regression Model ◽  
Conditional Logistic Regression ◽  
University Student ◽  
Web Survey ◽  
Student Survey ◽  
Conditional Logistic Regression Model ◽  
Information Technology Services ◽  
Technology Services ◽  
Vilnius University

In the paper the problem of adjustment for nonresponse in a WEB survey is addressed. The study is based on Vilnius University student survey on quality assessment of information technology services. A conditional logistic regression model is applied along with traditional nonresponse adjustment methods.  

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