Method for Sample Size Determination for Cluster-Randomized Trials Using the Bayes Factor

Abstract

Determining sample size is crucial in research study design. The hierarchical structure of the data in cluster-randomized trials (CRTs) complicates this process, thereby necessitating the determination of the sample size at each level. Most methods for these trials are based on null hypothesis significance testing, which has numerous pitfalls. Using the Bayes factor may avoid these drawbacks, but existing methods are limited to trials without a multilevel structure. This study presents a method to determine the sample size for a one-period two-treatment parallel CRT using the Bayes factor. We introduce the implementation of this method in an R package. Simulation results show that the required sample size increases with decreasing effect sizes and with increasing intraclass correlation and Bayes factors.