Department of Psychiatry and Human Behavior, The Warren Alpert Medical School of Brown University, Providence, RI, USA.
Bradley Hasbro Children's Research Center, Providence, RI, USA.
Prev Sci. 2023 Nov;24(8):1648-1658. doi: 10.1007/s11121-023-01586-2. Epub 2023 Sep 20.
Evidence synthesis involves drawing conclusions from trial samples that may differ from the target population of interest, and there is often heterogeneity among trials in sample characteristics, treatment implementation, study design, and assessment of covariates. Stitching together this patchwork of evidence requires subject-matter knowledge, a clearly defined target population, and guidance on how to weigh evidence from different trials. Transportability analysis has provided formal identifiability conditions required to make unbiased causal inference in the target population. In this manuscript, we review these conditions along with an additional assumption required to address systematic missing data. The identifiability conditions highlight the importance of accounting for differences in treatment effect modifiers between the populations underlying the trials and the target population. We perform simulations to evaluate the bias of conventional random effect models and multiply imputed estimates using the pooled trials sample and describe causal estimators that explicitly address trial-to-target differences in key covariates in the context of systematic missing data. Results indicate that the causal transportability estimators are unbiased when treatment effect modifiers are accounted for in the analyses. Results also highlight the importance of carefully evaluating identifiability conditions for each trial to reduce bias due to differences in participant characteristics between trials and the target population. Bias can be limited by adjusting for covariates that are strongly correlated with missing treatment effect modifiers, including data from trials that do not differ from the target on treatment modifiers, and removing trials that do differ from the target and did not assess a modifier.
证据综合涉及从可能与目标人群不同的试验样本中得出结论，并且试验在样本特征、治疗实施、研究设计和协变量评估方面常常存在异质性。将这些证据拼凑在一起需要主题知识、明确的目标人群以及如何权衡来自不同试验的证据的指导。可传输性分析提供了在目标人群中进行无偏因果推断所需的正式可识别性条件。在本文中，我们回顾了这些条件以及解决系统缺失数据所需的附加假设。可识别性条件强调了在试验和目标人群所依据的人群中考虑治疗效果修饰符差异的重要性。我们进行模拟评估了使用汇总试验样本的常规随机效应模型和多重插补估计的偏差，并描述了在系统缺失数据背景下明确解决关键协变量在试验与目标人群之间差异的因果估计量。结果表明，当分析中考虑到治疗效果修饰符时，因果可传输性估计量是无偏的。结果还强调了在每个试验中仔细评估可识别性条件的重要性，以减少由于试验和目标人群之间参与者特征的差异而导致的偏差。通过调整与缺失治疗效果修饰符强相关的协变量（包括来自与目标人群在治疗修饰剂方面无差异的试验的数据），并删除与目标人群有差异且未评估修饰剂的试验，可以限制偏差。
