title: Power analyses for longitudinal trials and other clustered designs.
abstract: Existing methods for power and sample size estimation for longitudinal and other clustered study designs have limited applications. In this paper, we review and extend existing approaches to improve these limitations. In particular, we focus on power analysis for the two most popular approaches for clustered data analysis, the generalized estimating equations and the linear mixed-effects models. By basing the derivation of the power function on the asymptotic distribution of the model estimates, the proposed approach provides estimates of power that are consistent with the methods of inference for data analysis. The proposed methodology is illustrated with numerous examples that are motivated by real study designs.
authors.affiliation: Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, NY 14642, USA. xin_tu@urmc.rochester.edu
authors.forename: X M
authors.initials: XM
authors.lastname: Tu
authors.forename: J
authors.initials: J
authors.lastname: Kowalski
authors.forename: J
authors.initials: J
authors.lastname: Zhang
authors.forename: K G
authors.initials: KG
authors.lastname: Lynch
authors.forename: P
authors.initials: P
authors.lastname: Crits-Christoph
mesh_terms: D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D008137:Longitudinal Studies; D014481:United States