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 and describe their assumptions. 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 conclusions by leading to smaller treatment effect estimates. 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：估计量有助于澄清治疗效应的解释，并确保估计量与研究目标一致。与个体随机试验相比，群体随机试验需要在估计量中定义额外属性，包括治疗效应是边际性的还是群体特异性的，以及是个体平均还是群体平均。本文通过潜在结果符号为涵盖这些属性的估计量提供了正式定义，并描述了它们之间的差异。随后，我们概述了每种估计量的对应估计方法及其假设条件。通过对一项已发表的群体随机试验进行重新分析，我们证明了估计量和估计方法的选择会影响解释。例如，根据目标估计量的不同，估计的比值比范围从1.38（p=0.17）到1.83（p=0.03），而对于某些估计量，估计方法的选择会导致治疗效应估计值减小，从而影响结论。我们得出结论：仔细指定估计量并选择适当的估计方法，对于确保群体随机试验解决正确的问题至关重要。