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:估计量可以帮助澄清治疗效果的解释,并确保估计器与研究目标一致。与个体随机试验相比,集群随机试验需要定义额外的属性,包括治疗效果是边际的还是集群特定的,以及它们是参与者的平均值还是集群平均值。在本文中,我们使用潜在结果符号提供包含这些属性的估计量的正式定义,并描述它们之间的差异。然后,我们提供每个估计量的估计器的概述,这些估计器在最小假设下是渐近无偏的。然后,通过对一项已发布的集群随机试验的重新分析,我们演示了与不同估计量对应的估计量可以有相当大的差异。与不同估计量对应的估计的比值超过30%,从3.69到4.85。我们得出结论:精心说明估计量以及适当的估计器选择对确保集群随机试验回答正确问题至关重要。