In cluster randomized trials (CRTs) groups rather than individuals are randomized to different interventions. Individuals’ responses within clusters are commonly more similar than those across clusters. This dependency introduces complexity when calculating the number of clusters required to reach a specified statistical power for nominal significance levels and effect sizes. Current CRTs’ sample size estimation approaches rely on asymptotic-based formulae or Monte Carlo methods. We propose a new Monte Carlo procedure which is based on the potential outcomes framework. By explicitly defining the causal estimand, the data generating, the sampling, and the treatment assignment mechanisms, this procedure allows for sample size calculations in a broad range of study designs including sample size calculations in finite and infinite populations. It can also address financial and administrative considerations by allowing for unequal allocation of clusters. The R package CRTsampleSearch implements the method and we provide examples for using this package.

在集群随机试验 (CRT) 中，组而不是个人被随机分配到不同的干预措施中。集群内个人的反应通常比集群间的反应更相似。在计算达到名义显着性水平和效应大小的指定统计功效所需的聚类数时，这种依赖性会引入复杂性。当前 CRT 的样本大小估计方法依赖于基于渐近的公式或蒙特卡罗方法。我们提出了一种基于潜在结果框架的新蒙特卡罗程序。通过明确定义因果估计、数据生成、抽样和治疗分配机制，该程序允许在广泛的研究设计中进行样本量计算，包括有限和无限总体中的样本量计算。它还可以通过允许集群的不平等分配来解决财务和行政方面的考虑。R 包 CRTsampleSearch 实现了该方法，我们提供了使用该包的示例。