标题： 当试验参与影响依从性时，将试验结果转化为外部目标人群时 显示英文标题

标题： Transporting results from a trial to an external target population when trial participation impacts adherence

摘要： 随机对照试验被认为是制定治疗指南的黄金标准，但其结果可能无法推广到现实世界的人群中。基线协变量和治疗-结局中介变量的分布差异阻碍了推广性。虽然处理变量差异的处理方法已经比较成熟，但对于中介变量差异的处理方法则更为有限。在此，我们考虑一种情况，即试验活动与常规护理环境不同（例如，经济补偿、随访频率）会影响治疗依从性。当目标人群中的治疗和依从性数据不可用时，我们无法识别特定治疗分配下目标人群的平均结局（即平均潜在结局）。因此，我们提出了一种敏感性分析方法，在该方法中需要指定一个参数，用于描述试验与目标人群之间对某一特定治疗依从性的相对差异，可能还需要根据协变量进行条件设置。我们讨论了基于外部知识确定敏感性分析参数的方法，包括设定范围以估计边界或将概率分布指定为反复抽取参数值（即使用蒙特卡洛抽样）。我们介绍了两种目标人群中平均反事实结局的估计器，这些估计器结合了这一敏感性参数：一种是嵌入式估计器，另一种是一步估计器，它具有双重稳健性并支持使用机器学习来估计辅助模型。最后，我们将所提出的方法应用于实际应用中，即将一项关于两种不同药物治疗阿片类使用障碍的试验结果推广到现实世界的人群中，并评估复发风险。 显示英文摘要

摘要： Randomized clinical trials are considered the gold standard for informing treatment guidelines, but results may not generalize to real-world populations. Generalizability is hindered by distributional differences in baseline covariates and treatment-outcome mediators. Approaches to address differences in covariates are well established, but approaches to address differences in mediators are more limited. Here we consider the setting where trial activities that differ from usual care settings (e.g., monetary compensation, follow-up visits frequency) affect treatment adherence. When treatment and adherence data are unavailable for the real-world target population, we cannot identify the mean outcome under a specific treatment assignment (i.e., mean potential outcome) in the target. Therefore, we propose a sensitivity analysis in which a parameter for the relative difference in adherence to a specific treatment between the trial and the target, possibly conditional on covariates, must be specified. We discuss options for specification of the sensitivity analysis parameter based on external knowledge including setting a range to estimate bounds or specifying a probability distribution from which to repeatedly draw parameter values (i.e., use Monte Carlo sampling). We introduce two estimators for the mean counterfactual outcome in the target that incorporates this sensitivity parameter, a plug-in estimator and a one-step estimator that is double robust and supports the use of machine learning for estimating nuisance models. Finally, we apply the proposed approach to the motivating application where we transport the risk of relapse under two different medications for the treatment of opioid use disorder from a trial to a real-world population.