Improving power in small-sample longitudinal studies when using generalized estimating equations

Philip M. Westgate, Woodrow W. Burchett

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Generalized estimating equations (GEE) are often used for the marginal analysis of longitudinal data. Although much work has been performed to improve the validity of GEE for the analysis of data arising from small-sample studies, little attention has been given to power in such settings. Therefore, we propose a valid GEE approach to improve power in small-sample longitudinal study settings in which the temporal spacing of outcomes is the same for each subject. Specifically, we use a modified empirical sandwich covariance matrix estimator within correlation structure selection criteria and test statistics. Use of this estimator can improve the accuracy of selection criteria and increase the degrees of freedom to be used for inference. The resulting impacts on power are demonstrated via a simulation study and application example.

Original languageEnglish
Pages (from-to)3733-3744
Number of pages12
JournalStatistics in Medicine
Issue number21
StatePublished - Sep 20 2016

Bibliographical note

Funding Information:
We thank the anonymous associate editor and two reviewers for their constructive comments that helped improve this paper. We also thank Dr Richard J. Kryscio, Dr Frederick A. Schmitt, and Dr Erin Abner for providing us the application example data from the PREADViSE trial, which was supported by a grant from the National Institute on Aging (R01 AG019241). This publication was supported by the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant UL1TR000117. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

Publisher Copyright:
Copyright © 2016 John Wiley & Sons, Ltd.


  • correlation selection
  • degrees of freedom
  • efficiency
  • empirical covariance matrix

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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