Model selection in the weighted generalized estimating equations for longitudinal data with dropout-Reference-Cited by-同舟云学术

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Model selection in the weighted generalized estimating equations for longitudinal data with dropout

Published:2015-10-28 Issue:3 Volume:58 Page:570-587
ISSN:0323-3847
Container-title:Biometrical Journal
language:en
Short-container-title:Biom. J.

Author:

Gosho Masahiko¹²

Affiliation:

1. Advanced Medical Research Center; Aichi Medical University; 1-1, Yazakokarimata Nagakute Aichi 480-1195 Japan

2. Department of Clinical Trial and Clinical Epidemiology; Faculty of Medicine; University of Tsukuba; 1-1-1, Tennodai Tsukuba Ibaraki 305-8575 Japan

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

Reference35 articles.

1. Longitudinal variable selection by cross-validation in the case of many covariates;Cantoni;Statistics in Medicine,2007

2. Variable selection for marginal longitudinal generalized linear models;Cantoni;Biometrics,2005

3. Working covariance model selection for generalized estimating equations;Carey;Statistics in Medicine,2011

4. Selection of working correlation structure and best model in GEE analyses of longitudinal data;J;Communications in Statistics -Simulation and Computation,2007

5. A caveat concerning independence estimating equations with multivariate binary data;Fitzmaurice;Biometrics,1995

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2. A multiple robust propensity score method for longitudinal analysis with intermittent missing data;Biometrics;2020-07-25

3. APOE ε4 and the Influence of Sex, Age, Vascular Risk Factors, and Ethnicity on Cognitive Decline;The Journals of Gerontology: Series A;2020-05-12

4. Empirical‐likelihood‐based criteria for model selection on marginal analysis of longitudinal data with dropout missingness;Biometrics;2019-04-25

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