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

• Published in: Statistics in medicine Vol. 35; no. 21; pp. 3733 - 3744
• Main Authors: Westgate, Philip M., Burchett, Woodrow W.
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 20.09.2016; Wiley Subscription Services, Inc
• Subjects: Biometry; Computer Simulation; correlation selection; Data analysis; degrees of freedom; efficiency; empirical covariance matrix; Estimating techniques; Humans; Longitudinal Studies; Models, Statistical; Sample size; Simulation; Statistical inference; empirical covariance matrix; efficiency; degrees of freedom; correlation selection
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.6967

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.