Affiliation:
1. Department of Biostatistics Harvard T. H. Chan School of Public Health Boston Massachusetts USA
2. Department of Population Medicine Harvard Pilgrim Health Care Institute and Harvard Medical School Boston Massachusetts USA
3. Department of Biostatistics Yale School of Public Health New Haven Connecticut USA
4. Center for Methods in Implementation and Prevention Science Yale School of Public Health New Haven Connecticut USA
Abstract
AbstractCluster randomized trials (CRTs) refer to a popular class of experiments in which randomization is carried out at the group level. While methods have been developed for planning CRTs to study the average treatment effect, and more recently, to study the heterogeneous treatment effect, the development for the latter objective has currently been limited to a continuous outcome. Despite the prevalence of binary outcomes in CRTs, determining the necessary sample size and statistical power for detecting differential treatment effects in CRTs with a binary outcome remain unclear. To address this methodological gap, we develop sample size procedures for testing treatment effect heterogeneity in two‐level CRTs under a generalized linear mixed model. Closed‐form sample size expressions are derived for a binary effect modifier, and in addition, a computationally efficient Monte Carlo approach is developed for a continuous effect modifier. Extensions to multiple effect modifiers are also discussed. We conduct simulations to examine the accuracy of the proposed sample size methods. We present several numerical illustrations to elucidate features of the proposed formulas and to compare our method to the approximate sample size calculation under a linear mixed model. Finally, we use data from the Strategies and Opportunities to Stop Colon Cancer in Priority Populations (STOP CRC) CRT to illustrate the proposed sample size procedure for testing treatment effect heterogeneity.
Funder
National Institute of Allergy and Infectious Diseases
Patient-Centered Outcomes Research Institute
National Institutes of Health
Subject
Statistics and Probability,Epidemiology