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

Philip M. Westgate, Woodrow W. Burchett

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

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
Volume35
Issue number21
DOIs
StatePublished - Sep 20 2016

Bibliographical note

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

Keywords

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

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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