Investigators often believe that relative effect measures conditional on covariates, such as risk ratios and mean ratios, are “transportable” across populations. Here, we examine the identification of causal effects in a target population using an assumption that conditional relative effect measures are transportable from a trial to the target population. We show that transportability for relative effect measures is largely incompatible with transportability for difference effect measures, unless the treatment has no effect on average or one is willing to make even stronger transportability assumptions that imply the transportability of both relative and difference effect measures. We then describe how marginal (population-averaged) causal estimands in a target population can be identified under the assumption of transportability of relative effect measures, when we are interested in the effectiveness of a new experimental treatment in a target population where the only treatment in use is the control treatment evaluated in the trial. We extend these results to consider cases where the control treatment evaluated in the trial is only one of the treatments in use in the target population, under an additional partial exchangeability assumption in the target population (i.e., an assumption of no unmeasured confounding in the target population with respect to potential outcomes under the control treatment in the trial). We also develop identification results that allow for the covariates needed for transportability of relative effect measures to be only a small subset of the covariates needed to control confounding in the target population. Last, we propose estimators that can be easily implemented in standard statistical software and illustrate their use using data from a comprehensive cohort study of stable ischemic heart disease.

研究人员通常认为，以协变量为条件的相对效应测量（例如风险比和均值比）在人群之间是“可转移的”。在这里，我们使用条件相对效应测量可以从试验转移到目标人群的假设来检查目标人群中因果效应的识别。我们表明，相对效应措施的可运输性在很大程度上与差异效应措施的可运输性不相容，除非处理对平均影响没有影响，或者人们愿意做出更强的可运输性假设，这意味着相对和差异效应测量的可运输性。然后，当我们对目标人群中新的实验性治疗的有效性感兴趣时，我们描述了如何在相对效应测量的可运输性假设下识别目标人群中的边际（群体平均）因果估计量，其中唯一使用的治疗方法是在试验中评估的对照治疗。我们将这些结果扩展到考虑试验中评估的对照治疗只是目标人群中使用的治疗之一的情况，在目标人群中的额外部分可交换性假设（即，假设目标人群中没有未测量的混杂关于试验中对照治疗下的潜在结果）。我们还开发了识别结果，允许相对效应测量的可传递性所需的协变量只是控制目标人群混杂所需的协变量的一小部分。 最后，我们提出了可以在标准统计软件中轻松实现的估计器，并使用来自稳定型缺血性心脏病综合队列研究的数据来说明它们的使用。