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Estimands in cluster-randomized trials: choosing analyses that answer the right question
International Journal of Epidemiology ( IF 7.7 ) Pub Date : 2022-07-14 , DOI: 10.1093/ije/dyac131 Brennan C Kahan 1 , Fan Li 2, 3 , Andrew J Copas 1 , Michael O Harhay 4, 5
International Journal of Epidemiology ( IF 7.7 ) Pub Date : 2022-07-14 , DOI: 10.1093/ije/dyac131 Brennan C Kahan 1 , Fan Li 2, 3 , Andrew J Copas 1 , Michael O Harhay 4, 5
Affiliation
Background Cluster-randomized trials (CRTs) involve randomizing groups of individuals (e.g. hospitals, schools or villages) to different interventions. Various approaches exist for analysing CRTs but there has been little discussion around the treatment effects (estimands) targeted by each. Methods We describe the different estimands that can be addressed through CRTs and demonstrate how choices between different analytic approaches can impact the interpretation of results by fundamentally changing the question being asked, or, equivalently, the target estimand. Results CRTs can address either the participant-average treatment effect (the average treatment effect across participants) or the cluster-average treatment effect (the average treatment effect across clusters). These two estimands can differ when participant outcomes or the treatment effect depends on the cluster size (referred to as ‘informative cluster size’), which can occur for reasons such as differences in staffing levels or types of participants between small and large clusters. Furthermore, common estimators, such as mixed-effects models or generalized estimating equations with an exchangeable working correlation structure, can produce biased estimates for both the participant-average and cluster-average treatment effects when cluster size is informative. We describe alternative estimators (independence estimating equations and cluster-level analyses) that are unbiased for CRTs even when informative cluster size is present. Conclusion We conclude that careful specification of the estimand at the outset can ensure that the study question being addressed is clear and relevant, and, in turn, that the selected estimator provides an unbiased estimate of the desired quantity.
中文翻译:
整群随机试验中的估计值:选择能够回答正确问题的分析
背景 整群随机试验 (CRT) 涉及将个体组(例如医院、学校或村庄)随机分配至不同的干预措施。存在多种分析 CRT 的方法,但几乎没有讨论每种方法针对的治疗效果(估计值)。方法 我们描述了可以通过 CRT 解决的不同估计量,并展示了不同分析方法之间的选择如何通过从根本上改变所提出的问题或等效地改变目标估计量来影响结果的解释。结果 CRT 可以解决参与者平均治疗效果(参与者之间的平均治疗效果)或集群平均治疗效果(集群之间的平均治疗效果)。当参与者结果或治疗效果取决于集群规模(称为“信息集群规模”)时,这两个估计值可能会有所不同,这可能是由于小型集群和大型集群之间的人员配备水平或参与者类型不同等原因造成的。此外,常见的估计量,例如混合效应模型或具有可交换工作相关结构的广义估计方程,可以在集群大小提供信息时对参与者平均和集群平均治疗效果产生有偏差的估计。我们描述了替代估计量(独立估计方程和集群级分析),即使存在提供信息的集群大小,它们对 CRT 也是无偏的。
更新日期:2022-07-14
中文翻译:
整群随机试验中的估计值:选择能够回答正确问题的分析
背景 整群随机试验 (CRT) 涉及将个体组(例如医院、学校或村庄)随机分配至不同的干预措施。存在多种分析 CRT 的方法,但几乎没有讨论每种方法针对的治疗效果(估计值)。方法 我们描述了可以通过 CRT 解决的不同估计量,并展示了不同分析方法之间的选择如何通过从根本上改变所提出的问题或等效地改变目标估计量来影响结果的解释。结果 CRT 可以解决参与者平均治疗效果(参与者之间的平均治疗效果)或集群平均治疗效果(集群之间的平均治疗效果)。当参与者结果或治疗效果取决于集群规模(称为“信息集群规模”)时,这两个估计值可能会有所不同,这可能是由于小型集群和大型集群之间的人员配备水平或参与者类型不同等原因造成的。此外,常见的估计量,例如混合效应模型或具有可交换工作相关结构的广义估计方程,可以在集群大小提供信息时对参与者平均和集群平均治疗效果产生有偏差的估计。我们描述了替代估计量(独立估计方程和集群级分析),即使存在提供信息的集群大小,它们对 CRT 也是无偏的。