Estimands can help clarify the interpretation of treatment effects and ensure that estimators are aligned to the study's objectives. Cluster randomised trials require additional attributes to be defined within the estimand compared to individually randomised trials, including whether treatment effects are marginal or cluster specific, and whether they are participant or cluster average. In this paper, we provide formal definitions of estimands encompassing both these attributes using potential outcomes notation and describe differences between them. We then provide an overview of estimators for each estimand that are asymptotically unbiased under minimal assumptions. Then, through a reanalysis of a published cluster randomised trial, we demonstrate that estimates corresponding to the different estimands can vary considerably. Estimated odds ratios corresponding to different estimands varied by more than 30 percent, from 3.69 to 4.85. We conclude that careful specification of the estimand, along with appropriate choice of estimator, are essential to ensuring that cluster randomised trials are addressing the right question.

翻译：估计目标有助于明确处理效应的解释，并确保估计量与研究目标保持一致。与个体随机试验相比，群组随机试验需要在估计目标中定义额外属性，包括处理效应是边际性的还是群组特异性的，以及是参与者平均还是群组平均。本文使用潜在结局符号给出了涵盖这些属性的估计目标的正式定义，并描述了它们之间的差异。随后，我们概述了每种估计目标在最小假设下具有渐近无偏性的估计量。通过重新分析一项已发表的群组随机试验，我们证明不同估计目标对应的估计值可能存在显著差异。不同估计目标对应的比值比估计值变化超过30%，从3.69到4.85。我们得出结论：仔细指定估计目标并选择合适的估计量，对于确保群组随机试验解决正确的问题至关重要。