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.

中文翻译：

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用于整群随机试验的贝叶斯组序贯设计

已经为单独的随机试验提出了灵活的方法，以节省时间或减少样本量。然而，将参与者组而不是个人随机分配到治疗组的整群随机试验的灵活设计并不常见。受一项旨在评估基于机器学习的临床决策支持系统对医生治疗抑郁症患者的有效性的整群随机试验的启发，提出了两种用于整群随机试验的贝叶斯组序贯设计，以允许提前停止治疗前的疗效-计划的中期分析。两种设计的不同之处在于参与者按顺序招募的方式。给定最大集群数以及试验中允许的最大集群大小，一种设计按顺序招募具有给定最大集群大小的集群，而另一种设计在试验开始时招募所有集群，但按顺序招募个体参与者，直到试验因疗效而提前停止或达到最终分析。通过模拟各种场景和两种设计的两种结果类型来探索设计操作特征。我们根据模拟结果对整群随机试验的贝叶斯组序贯设计提出建议。通过模拟各种场景和两种设计的两种结果类型来探索设计操作特征。我们根据模拟结果对整群随机试验的贝叶斯组序贯设计提出建议。通过模拟各种场景和两种设计的两种结果类型来探索设计操作特征。我们根据模拟结果对整群随机试验的贝叶斯组序贯设计提出建议。