Power and Sample Size Calculations for Generalized Estimating Equations via Local Asymptotics.
We consider the problem of calculating power and sample size for tests based on generalized estimating equations (GEE), that arise in studies involving clustered or correlated data (e.g., longitudinal studies and sibling studies). Previous approaches approximate the power of such tests using the asymptotic behavior of the test statistics underfixedalternatives. We develop a more accurate approach in which the asymptotic behavior is studied under a sequence oflocalalternatives that converge to the null hypothesis at root-mrate, wheremis the number of clusters. Based on this approach, explicit sample size formulae are derived for Wald and quasi-score test statistics in a variety of GEE settings. Simulation results show that in the important special case of logistic regression with exchangeable correlation structure, previous approaches can inflate the projected sample size (to obtain nominal 90% power using the Wald statistic) by over 10%, whereas the proposed approach provides an accuracy of around 2%.
Dartmouth College.
Zhigang
Z
Li
Columbia University.
Ian W
IW
McKeague
Clustered and correlated data; GEE; Local alternatives; Longitudinal data analysis; Marginal models