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      Akaike's information criterion in generalized estimating equations.

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      Biometrics
      Wiley

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          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.

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          Author and article information

          Journal
          Biometrics
          Biometrics
          Wiley
          0006-341X
          0006-341X
          Mar 2001
          : 57
          : 1
          Affiliations
          [1 ] Division of Biostatistics, University of Minnesota, Minneapolis 55455, USA. weip@biostat.umn.edu
          Article
          10.1111/j.0006-341x.2001.00120.x
          11252586
          41509540-095c-41c9-abaf-c0e913f0432e
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