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Working correlation structure selection in GEE analysis

Author

Listed:
  • María Carmen Pardo

    (Complutense University of Madrid)

  • Rosa Alonso

    (Complutense University of Madrid)

Abstract

The method of generalized estimating equations models the association between the repeated observations on a subject. It is important to select an appropriate working correlation structure for the repeated measures per subject in order to enhance efficiency of estimation of the regression parameter. Some existing selection criteria choose the structure for which the covariance matrix estimator and the specified working covariance matrix are closest. So, we define a new criterion based on this idea for selecting a working correlation structure. Also, we compare our criterion with some existing criteria to identify the true correlation structure via simulations for Poisson, binomial and normal responses, and exchangeable or AR(1) intracluster correlation structure. We assume that for each subject, the number of observations remains the same. Furthermore, we also illustrate the performance of our criterion using two data sets. Finally, we conclude that our approach is a good selection criterion in most of the different considered settings.

Suggested Citation

  • María Carmen Pardo & Rosa Alonso, 2019. "Working correlation structure selection in GEE analysis," Statistical Papers, Springer, vol. 60(5), pages 1447-1467, October.
    • Handle: RePEc:spr:stpapr:v:60:y:2019:i:5:d:10.1007_s00362-017-0881-0
    DOI: 10.1007/s00362-017-0881-0
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    References listed on IDEAS

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