When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with certain characteristics in order to improve trial economy or support inferences about subgroups of clusters, may preclude simple random sampling from the cohort into the trial, and thus interfere with the goal of producing generalizable inferences about the target population. We describe a nested trial design where the randomized clusters are embedded within a cohort of trial-eligible clusters from the target population and where clusters are selected for inclusion in the trial with known sampling probabilities that may depend on cluster characteristics (e.g., allowing clusters to be chosen to facilitate trial conduct or to examine hypotheses related to their characteristics). We develop and evaluate methods for analyzing data from this design to generalize causal inferences to the target population underlying the cohort. We present identification and estimation results for the expectation of the average potential outcome and for the average treatment effect, in the entire target population of clusters and in its non-randomized subset. In simulation studies, we show that all the estimators have low bias but markedly different precision. Cluster randomized trials where clusters are selected for inclusion with known sampling probabilities that depend on cluster characteristics, combined with efficient estimation methods, can precisely quantify treatment effects in the target population, while addressing objectives of trial conduct that require oversampling clusters on the basis of their characteristics.

中文翻译：

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旨在支持可推广推论的集群随机试验

在计划整群随机试验时，评估人员通常可以访问代表整群目标人群的枚举队列。进行试验的实用性，例如需要对具有某些特征的集群进行过采样，以改善试验经济性或支持有关集群子组的推论，可能会妨碍从队列中进行简单随机抽样进入试验，从而干扰生产的目标对目标人群的普遍推论。我们描述了一种嵌套试验设计，其中随机聚类嵌入来自目标人群的一组符合试验资格的聚类中，并且选择聚类以包含在具有已知抽样概率的试验中，该抽样概率可能取决于聚类特征（例如，允许聚类选择以促进试验的进行或检查与其特征相关的假设）。我们开发和评估用于分析该设计的数据的方法，以将因果推论推广到该队列的目标人群。我们提供了整个集群目标人群及其非随机子集中的平均潜在结果预期和平均治疗效果的识别和估计结果。在模拟研究中，我们表明所有估计量都具有较低的偏差，但精度明显不同。聚类随机试验中，根据聚类特征选择包含已知抽样概率的聚类，结合有效的估计方法，可以精确量化目标人群的治疗效果，同时解决需要根据聚类特征进行过采样聚类的试验实施目标。特征。