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Biometrics. 2001 Mar;57(1):120-5.

Akaike's information criterion in generalized estimating equations.

Author information

  • Division of Biostatistics, University of Minnesota, Minneapolis 55455, USA. weip@biostat.umn.edu

Abstract

Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model-selection criteria available in GEE. The well-known Akaike Information Criterion (AIC) cannot be directly applied since AIC is based on maximum likelihood estimation while GEE is nonlikelihood based. We propose a modification to AIC, where the likelihood is replaced by the quasi-likelihood and a proper adjustment is made for the penalty term. Its performance is investigated through simulation studies. For illustration, the method is applied to a real data set.

PMID:
11252586
[PubMed - indexed for MEDLINE]
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