Selection of working correlation structure and best model in GEE analyses of longitudinal data

Cui, James and Qian, Guoqi 2007, Selection of working correlation structure and best model in GEE analyses of longitudinal data, Communications in statistics : simulation and computation, vol. 36, no. 5, pp. 987-996.


Title Selection of working correlation structure and best model in GEE analyses of longitudinal data
Author(s) Cui, James
Qian, Guoqi
Journal name Communications in statistics : simulation and computation
Volume number 36
Issue number 5
Start page 987
End page 996
Total pages 10
Publisher Taylor & Francis
Place of publication Philadelphia, Pa.
Publication date 2007-09
ISSN 0361-0918
1532-4141
Summary The Generalized Estimating Equations (GEE) method is one of the most commonly used statistical methods for the analysis of longitudinal data in epidemiological studies. A working correlation structure for the repeated measures of the outcome variable of a subject needs to be specified by this method. However, statistical criteria for selecting the best correlation structure and the best subset of explanatory variables in GEE are only available recently because the GEE method is developed on the basis of quasi-likelihood theory. Maximum likelihood based model selection methods, such as the widely used Akaike Information Criterion (AIC), are not applicable to GEE directly. Pan (2001) proposed a selection method called QIC which can be used to select the best correlation structure and the best subset of explanatory variables. Based on the QIC method, we developed a computing program to calculate the QIC value for a range of different distributions, link functions and correlation structures. This program was written in Stata software. In this article, we introduce this program and demonstrate how to use it to select the most parsimonious model in GEE analyses of longitudinal data through several representative examples.
Language eng
Field of Research 111706 Epidemiology
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ┬ęTaylor & Francis Group
Persistent URL http://hdl.handle.net/10536/DRO/DU:30025308

Document type: Journal Article
Collection: Public Health Research, Evaluation, and Policy Cluster
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