QIC program and model selection in GEE analyses
James Cui
Department of Epidemiology and Preventive Medicine
Monash University
Melbourne, Australia
[email protected]
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Abstract. The generalized estimating equation (GEE) approach is a widely used
statistical method in the analysis of longitudinal data in clinical and
epidemiological studies. It is an extension of the generalized linear model
(GLM) method to correlated data such that valid standard errors of the
parameter estimates can be drawn. Unlike the GLM method, which is based on
the maximum likelihood theory for independent observations, the GEE method
is based on the quasilikelihood theory and no assumption is made about the
distribution of response observations. Therefore, Akaike’s information
criterion, a widely used method for model selection in GLM, is not
applicable to GEE directly. However, Pan (Biometrics 2001; 57: 120–125)
proposed a model-selection method for GEE and termed it quasilikelihood
under the independence model criterion. This criterion can also be used to
select the best-working correlation structure. From Pan’s methods, I
developed a general Stata program, qic, that accommodates all the
distribution and link functions and correlation structures available in
Stata version 9. In this paper, I introduce this program and demonstrate how
to use it to select the best working correlation structure and the best
subset of covariates through two examples in longitudinal studies.
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James Cui
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qic, Akaike's information criterion, GEE, likelihood, model, quasilikelihood under the independence model criterion
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