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Improving power in small‐sample longitudinal studies when using generalized estimating equations
Author(s) -
Westgate Philip M.,
Burchett Woodrow W.
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6967
Subject(s) - generalized estimating equation , gee , estimator , statistics , inference , estimating equations , sample size determination , covariance matrix , mathematics , econometrics , sample (material) , computer science , degrees of freedom (physics and chemistry) , model selection , statistical inference , covariance , artificial intelligence , physics , quantum mechanics , chemistry , chromatography
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. Copyright © 2016 John Wiley & Sons, Ltd.