Li Fan, Tian Zizhong, Bobb Jennifer, Papadogeorgou Georgia, Li Fan
Department of Statistical Science, Duke University, Durham, NC, USA.
Department of Public Health Sciences, Pennsylvania State University, Hershey, PA, USA.
Clin Trials. 2022 Feb;19(1):33-41. doi: 10.1177/17407745211056875. Epub 2021 Dec 11.
In cluster randomized trials, patients are typically recruited after clusters are randomized, and the recruiters and patients may not be blinded to the assignment. This often leads to differential recruitment and consequently systematic differences in baseline characteristics of the recruited patients between intervention and control arms, inducing post-randomization selection bias. We aim to rigorously define causal estimands in the presence of selection bias. We elucidate the conditions under which standard covariate adjustment methods can validly estimate these estimands. We further discuss the additional data and assumptions necessary for estimating causal effects when such conditions are not met.
Adopting the principal stratification framework in causal inference, we clarify there are two average treatment effect (ATE) estimands in cluster randomized trials: one for the overall population and one for the recruited population. We derive analytical formula of the two estimands in terms of principal-stratum-specific causal effects. Furthermore, using simulation studies, we assess the empirical performance of the multivariable regression adjustment method under different data generating processes leading to selection bias.
When treatment effects are heterogeneous across principal strata, the average treatment effect on the overall population generally differs from the average treatment effect on the recruited population. A naïve intention-to-treat analysis of the recruited sample leads to biased estimates of both average treatment effects. In the presence of post-randomization selection and without additional data on the non-recruited subjects, the average treatment effect on the recruited population is estimable only when the treatment effects are homogeneous between principal strata, and the average treatment effect on the overall population is generally not estimable. The extent to which covariate adjustment can remove selection bias depends on the degree of effect heterogeneity across principal strata.
There is a need and opportunity to improve the analysis of cluster randomized trials that are subject to post-randomization selection bias. For studies prone to selection bias, it is important to explicitly specify the target population that the causal estimands are defined on and adopt design and estimation strategies accordingly. To draw valid inferences about treatment effects, investigators should (1) assess the possibility of heterogeneous treatment effects, and (2) consider collecting data on covariates that are predictive of the recruitment process, and on the non-recruited population from external sources such as electronic health records.
在整群随机试验中，患者通常在整群被随机分组后招募，招募者和患者可能无法对分组情况设盲。这通常会导致不同的招募情况，进而使干预组和对照组中招募患者的基线特征出现系统性差异，引发随机化后选择偏倚。我们旨在严格定义存在选择偏倚时的因果估计量。我们阐明标准协变量调整方法能够有效估计这些估计量的条件。我们还进一步讨论了在不满足这些条件时估计因果效应所需的额外数据和假设。
采用因果推断中的主分层框架，我们阐明在整群随机试验中有两种平均治疗效应（ATE）估计量：一种针对总体人群，另一种针对招募人群。我们根据主层特定因果效应推导出这两种估计量的解析公式。此外，通过模拟研究，我们评估了在导致选择偏倚的不同数据生成过程下多变量回归调整方法的实证性能。
当治疗效应在主层间存在异质性时，总体人群的平均治疗效应通常与招募人群的平均治疗效应不同。对招募样本进行简单的意向性分析会导致对两种平均治疗效应的估计产生偏差。在存在随机化后选择且没有未招募受试者的额外数据时，只有当主层间治疗效应相同时，招募人群的平均治疗效应才可估计，而总体人群的平均治疗效应通常不可估计。协变量调整能够消除选择偏倚的程度取决于主层间效应异质性的程度。
有必要且有机会改进受随机化后选择偏倚影响的整群随机试验的分析。对于容易出现选择偏倚的研究，明确指定因果估计量所定义的目标人群并相应地采用设计和估计策略非常重要。为了对治疗效应得出有效的推断，研究者应（1）评估治疗效应异质性的可能性，（2）考虑收集预测招募过程的协变量数据以及来自电子健康记录等外部来源的未招募人群的数据。
