title: Improving power in small-sample longitudinal studies when using generalized estimating equations.
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. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, 40536, KY, U.S.A.
authors.forename: Philip M
authors.identifier: 0000-0003-2991-268X
authors.initials: PM
authors.lastname: Westgate
authors.affiliation: Department of Statistics, College of Arts and Sciences, University of Kentucky, Lexington, 40536, KY, U.S.A.
authors.forename: Woodrow W
authors.initials: WW
authors.lastname: Burchett
mesh_terms: D001699:Biometry; D003198:Computer Simulation; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical
keywords: correlation selection; degrees of freedom; efficiency; empirical covariance matrix