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.

中文翻译：

---

将随机试验的推论扩展到新的目标人群。

当治疗效果修正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，我们考虑使用来自已完成随机试验的数据和来自目标人群样本的基线协变量数据，将有关时间固定治疗的因果推断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的期望、参与概率或两者（双重稳健）建模的方法。我们在模拟研究中比较了这些方法，并展示了它们如何在软件中实现。我们将这些方法应用于嵌套在一组符合试验条件的患者中的随机试验，以比较冠状动脉手术加药物治疗与单独药物治疗对慢性冠状动脉疾病患者的疗效。最后，我们讨论了在应用分析中使用这些方法时出现的问题。