Bayesian group sequential designs for cluster-randomized trials

Flexible approaches have been proposed for individually randomized trials to save time or reduce the sample size. However, flexible designs for cluster-randomized trials in which groups of participants rather than individuals are randomized to treatment arms are less common. Motivated by a cluster-randomized trial designed to assess the effectiveness of a machine-learning based clinical decision support system for physicians treating patients with depression, two Bayesian group sequential designs for cluster-randomized trials are proposed to allow for early stopping for efficacy at pre-planned interim analyses. The difference between the two designs lies in the way that participants are sequentially recruited. Given a maximum number of clusters as well as the maximum cluster size allowed in the trial, one design sequentially recruits clusters with the given maximum cluster size, while the other recruits all clusters at the beginning of the trial but sequentially enrolls individual participants until the trial is stopped early for efficacy or the final analysis has been reached. The design operating characteristics are explored via simulations for a variety of scenarios and two outcome types for the two designs. We make recommendations for Bayesian group sequential designs of cluster-randomized trials based on the simulation results.

Automated summary

This study discusses the flexible designs for cluster-randomized trials and proposes two Bayesian group sequential designs to assess the effectiveness of a machine-learning based clinical decision support system for physicians treating patients with depression. The operating characteristics of these designs are explored via simulations, and recommendations are made based on the results.