title: Correlation structure and variable selection in generalized estimating equations via composite likelihood information criteria.
abstract: The method of generalized estimating equations (GEE) is popular in the biostatistics literature for analyzing longitudinal binary and count data. It assumes a generalized linear model for the outcome variable, and a working correlation among repeated measurements. In this paper, we introduce a viable competitor: the weighted scores method for generalized linear model margins. We weight the univariate score equations using a working discretized multivariate normal model that is a proper multivariate model. Because the weighted scores method is a parametric method based on likelihood, we propose composite likelihood information criteria as an intermediate step for model selection. The same criteria can be used for both correlation structure and variable selection. Simulations studies and the application example show that our method outperforms other existing model selection methods in GEE. From the example, it can be seen that our methods not only improve on GEE in terms of interpretability and efficiency but also can change the inferential conclusions with respect to GEE. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, U.K.
authors.forename: Aristidis K
authors.identifier: 0000-0003-0853-0084
authors.initials: AK
authors.lastname: Nikoloulopoulos
mesh_terms: D056808:Biostatistics; D000068456:Clinical Studies as Topic / Q000706:statistics & numerical data; D003198:Computer Simulation; D003865:Major Depressive Disorder / Q000188:drug therapy; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D015337:Multicenter Studies as Topic / Q000706:statistics & numerical data; D015999:Multivariate Analysis
keywords: AIC; BIC; binary/poisson regression; composite likelihood; generalized linear models; weighted scores