HIV Working Group

Speaker: Lara Maleyeff, Doctoral Student, Department of Biostatistics, Harvard University

Title: Sample size requirements for testing treatment effect heterogeneity in cluster randomized trials with binary outcomes

Abstract: Cluster randomized trials (CRTs) refer to a popular set of experiments in which randomization is carried out at the group level. While methods have been developed for planning CRTs to study the overall 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. Because the sample size and power requirements for detecting differential treatment effect in CRTs with a binary outcome remain unclear but are highly relevant given the prevalence of binary outcomes, we develop formal 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 with a binary effect modifier, whereas a Monte Carlo approach is developed with a continuous effect modifier. We present several numerical studies to elucidate features of the proposed formulas and to compare our method to the approximation calculation under a linear mixed model. Extensions to multiple effect modifiers are also discussed. We conduct simulations to examine the accuracy of the proposed sample size methods and use data from the STOP CRC cluster randomized trial to illustrate the proposed sample size procedure for testing treatment effect heterogeneity.