Methods for generalizing inferences from cluster randomized controlled trials to target populations

项目摘要/摘要 当干预措施在群体水平得到最佳应用时，分组试验是研究设计的最佳选择 一个人的暴露可能会影响同一群组中其他人的结果。群组试验是 越来越多地嵌入大型医疗保健系统，允许使用常规收集的数据来 提高研究效率。然而，令人担忧的是--这项提案提供了佐证-- 这种随机分组并不能代表常规护理中的目标人群。在治疗的时候 效果因影响试验参与的因素而异，试验的治疗效果不能直接 适用于现实世界中有实质性兴趣的目标人群。因此，即使在精心设计的集群试验中， 选择性参与可能会导致对目标人群做出因果推断时的偏见。给定 越来越多的集群试验正在进行，调查人员需要严格的方法来推广 针对目标人群的群组试验的结果解决了选择性参与偏见，并可以解释 多重数据科学挑战，包括同一群集中观测数据之间的随机相关性； 仅从少数几个组或从样本量相对较小的组获得随机试验数据； 缺乏对试验参与和结果的预测因素的知识，当候选协变量经常超过 可用集群的数量，需要使用灵活的机器学习方法；以及缺失 结果数据。为响应特别关注通知NOT-LM-19-003，我们提出新的、领域- 独立的、可重复使用的因果和统计方法，以应对这些数据科学挑战并 通过消除因选择性试验而产生的偏差，提高集群试验为临床和政策决策提供信息的能力 在估计平均治疗效果和估计最优协变量依赖时的参与度 治疗策略。我们将在现实的模拟研究和实证分析中评估这些方法 来自美国疗养院流感疫苗接种策略的3个大规模集群试验的数据。

PROJECT SUMMARY/ABSTRACT Cluster trials are the study design of choice when interventions are best applied at the group level and when exposure of one individual may affect the outcomes of other individuals in the same cluster. Cluster trials are increasingly embedded within large health care systems, allowing the use of routinely collected data to increase research efficiency. There is concern, however – and this proposal provides supportive evidence – that randomized clusters are not representative the target populations seen in routine care. When treatment effects vary over factors that influence trial participation, treatment effects from the trial cannot be directly applied to real-world target populations of substantive interest. Thus, even in well-designed cluster trials, selective participation can lead to bias in drawing causal inferences about the target population. Given the increasing number of cluster trials being conducted, investigators need rigorous methods for generalizing findings from cluster trials to target populations that address selective participation bias and can account for multiple data science challenges, including stochastic dependence among observations in the same cluster; availability of randomized trial data from only a few clusters or from clusters with relatively small sample sizes; lack of knowledge of predictors of trial participation and the outcome, when candidate covariates often exceed the number of available clusters and necessitate the use of flexible machine learning approaches; and missing outcome data. In response to Notice of Special Interest NOT-LM-19-003, we propose novel, domain- independent, reusable causal and statistical methods to address these data-science challenges and to increase the ability of cluster trials to inform clinical and policy decisions by eliminating bias due to selective participation when estimating average treatment effects and when estimating the optimal covariate-dependent treatment strategy. We will evaluate the methods in realistic simulation studies and in empirical analyses using data from 3 large-scale cluster trials of influenza vaccination strategies in U.S. nursing homes.