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, describe their assumptions, and show consistency (i.e. asymptotically unbiased estimation) for a series of analyses based on cluster level summaries. Then, through a reanalysis of a published cluster randomised trial, we demonstrate that the choice of both estimand and estimator can affect interpretation. For instance, the estimated odds ratio ranged from 1.38 (p=0.17) to 1.83 (p=0.03) depending on the target estimand, and for some estimands, the choice of estimator affected the conclusions by leading to smaller treatment effect estimates. We conclude that careful specification of the estimand, along with an appropriate choice of estimator, are essential to ensuring that cluster randomised trials address the right question.

翻译：估计目标有助于明确治疗效应的解释，并确保估计量与研究目标保持一致。与个体随机试验相比，集群随机试验需要在估计目标中定义额外的属性，包括治疗效应是边际的还是集群特异性的，以及它们是参与者平均还是集群平均。本文利用潜在结果符号为涵盖这些属性的估计目标提供了正式定义，并描述了它们之间的差异。随后，我们概述了每种估计目标对应的估计量，描述了它们的假设，并展示了基于集群层面汇总的一系列分析的一致性（即渐近无偏估计）。通过重新分析一项已发表的集群随机试验，我们证明了估计目标和估计量的选择都会影响解释。例如，根据目标估计目标的不同，估计的比值比范围从1.38（p=0.17）到1.83（p=0.03），而对于某些估计目标，估计量的选择通过导致更小的治疗效应估计值影响了结论。我们得出结论：仔细指定估计目标，并选择合适的估计量，对于确保集群随机试验回答正确的问题至关重要。