Affiliation:
1. Department of Biostatistics and Bioinformatics Duke University School of Medicine Durham North Carolina USA
2. Department of Biostatistics Yale School of Public Health New Haven Connecticut USA
3. Duke Global Health Institute Duke University Durham North Carolina USA
4. Center for Methods in Implementation and Prevention Science Yale University New Haven Connecticut USA
Abstract
Individually randomized group treatment (IRGT) trials, in which the clustering of outcome is induced by group‐based treatment delivery, are increasingly popular in public health research. IRGT trials frequently incorporate longitudinal measurements, of which the proper sample size calculations should account for correlation structures reflecting both the treatment‐induced clustering and repeated outcome measurements. Given the relatively sparse literature on designing longitudinal IRGT trials, we propose sample size procedures for continuous and binary outcomes based on the generalized estimating equations approach, employing the block exchangeable correlation structures with different correlation parameters for the treatment arm and for the control arm, and surveying five marginal mean models with different assumptions of time effect: no‐time constant treatment effect, linear‐time constant treatment effect, categorical‐time constant treatment effect, linear time by treatment interaction, and categorical time by treatment interaction. Closed‐form sample size formulas are derived for continuous outcomes, which depends on the eigenvalues of the correlation matrices; detailed numerical sample size procedures are proposed for binary outcomes. Through simulations, we demonstrate that the empirical power agrees well with the predicted power, for as few as eight groups formed in the treatment arm, when data are analyzed using the matrix‐adjusted estimating equations for the correlation parameters with a bias‐corrected sandwich variance estimator.
Funder
National Institutes of Health
Subject
Statistics and Probability,Epidemiology