论文标题
概括和可运输性分析目标人群中的治疗选择
Selection on treatment in the target population of generalizabillity and transportability analyses
论文作者
论文摘要
研究人员越来越多地使用新的方法将因果关系从试验扩展到目标人群。在许多可推广性和可运输性分析中，在非巢试验设计之后，分别采样了来自目标人群的试验和观察数据。在这种设计的实际实施中，通常通过使用特定治疗的条件来确定来自目标人群的非随机人，而将其他候选治疗方法用于相同指示或不使用任何治疗的个体的个人被排除在外。在本文中，我们认为，在目标人群中对治疗的条件改变了对可推广性和可运输性分析的估计，并可能在目标人群中的因果估计值估计或使用特定治疗方面引起严重的偏见。此外，我们认为，基于边缘化或基于权重的标准化方法的幼稚应用不会产生任何合理的因果估计和估计。我们使用因果图和反事实论证来表征目标人群中的治疗条件引起的识别问题，并使用模拟数据说明问题。我们通过考虑我们发现对应用工作的含义的结论。
Investigators are increasingly using novel methods for extending (generalizing or transporting) causal inferences from a trial to a target population. In many generalizability and transportability analyses, the trial and the observational data from the target population are separately sampled, following a non-nested trial design. In practical implementations of this design, non-randomized individuals from the target population are often identified by conditioning on the use of a particular treatment, while individuals who used other candidate treatments for the same indication or individuals who did not use any treatment are excluded. In this paper, we argue that conditioning on treatment in the target population changes the estimand of generalizability and transportability analyses and potentially introduces serious bias in the estimation of causal estimands in the target population or the subset of the target population using a specific treatment. Furthermore, we argue that the naive application of marginalization-based or weighting-based standardization methods does not produce estimates of any reasonable causal estimand. We use causal graphs and counterfactual arguments to characterize the identification problems induced by conditioning on treatment in the target population and illustrate the problems using simulated data. We conclude by considering the implications of our findings for applied work.
