Extending inferences from a randomized trial to a new target population
Extending inferences from a randomized trial to a new target population
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DOI：
10.1002/sim.8426
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发表时间：
2020-06-30
影响因子：
2
通讯作者：
Hernan, Miguel A.
中科院分区：
文献类型：
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作者：
Dahabreh, Issa J.;Robertson, Sarah E.;Hernan, Miguel A.
当治疗效果修正因素影响参与随机试验的决定时，随机个体所代表的人群中的平均治疗效果将不同于其他人群中的效果。在本教程中，我们考虑使用已完成的随机试验的数据和来自目标人群样本的基线协变量数据，将有关固定时间治疗的因果推论从试验扩展到新的非参与者目标人群的方法。我们检查基于对结果预期、参与概率或两者（双重稳健）进行建模的方法。我们比较了模拟研究中的方法，并展示了如何在软件中实施它们。我们将这些方法应用于符合试验资格的患者队列中的一项随机试验，以比较慢性冠状动脉疾病患者的冠状动脉手术加药物治疗与单独药物治疗。最后，我们讨论在应用分析中使用这些方法时出现的问题。
When treatment effect modifiers influence the decision to participate in a randomized trial, the average treatment effect in the population represented by the randomized individuals will differ from the effect in other populations. In this tutorial, we consider methods for extending causal inferences about time-fixed treatments from a trial to a new target population of nonparticipants, using data from a completed randomized trial and baseline covariate data from a sample from the target population. We examine methods based on modeling the expectation of the outcome, the probability of participation, or both (doubly robust). We compare the methods in a simulation study and show how they can be implemented in software. We apply the methods to a randomized trial nested within a cohort of trial-eligible patients to compare coronary artery surgery plus medical therapy versus medical therapy alone for patients with chronic coronary artery disease. We conclude by discussing issues that arise when using the methods in applied analyses.