When extending inferences from a randomized trial to a new target population, an assumption of transportability of difference effect measures (e.g., conditional average treatment effects) -- or even stronger assumptions of transportability in expectation or distribution of potential outcomes -- is invoked to identify the marginal causal mean difference in the target population. However, many clinical investigators believe that relative effect measures conditional on covariates, such as conditional risk ratios and mean ratios, are more likely to be ``transportable'' across populations compared with difference effect measures. Here, we examine the identification and estimation of the marginal counterfactual mean difference and ratio under a transportability assumption for conditional relative effect measures. We obtain identification results for two scenarios that often arise in practice when individuals in the target population (1) only have access to the control treatment, or (2) have access to the control and other treatments but not necessarily the experimental treatment evaluated in the trial. We then propose multiply robust and nonparametric efficient estimators that allow for the use of data-adaptive methods (e.g., machine learning techniques) to model the nuisance parameters. We examine the performance of the methods in simulation studies and illustrate their use with data from two trials of paliperidone for patients with schizophrenia. We conclude that the proposed methods are attractive when background knowledge suggests that the transportability assumption for conditional relative effect measures is more plausible than alternative assumptions.

翻译：当将随机试验的推论扩展到新的目标人群时，通常需要利用差分效应测量（如条件平均处理效应）的可传递性假设——甚至更严格的期望或潜在结果分布可传递性假设——来识别目标人群中的边际因果均值差。然而，许多临床研究者认为，与差分效应测量相比，条件风险比和均值比等条件相对效应测量更可能在不同人群间具有"可传递性"。本文研究了在条件相对效应测量可传递性假设下，边际反事实均值差和比值的识别与估计问题。我们针对实践中经常出现的两种场景获得了识别结果：（1）目标人群个体仅能获得对照治疗，或（2）可获得对照及其他治疗，但未必能获得试验中评估的实验性治疗。随后，我们提出了多重稳健且非参数有效的估计量，允许使用数据自适应方法（如机器学习技术）建立干扰参数模型。我们通过模拟研究检验了这些方法的性能，并利用两项棕榈酸帕利哌酮治疗精神分裂症患者的试验数据展示了其实用性。结论表明，当背景知识提示条件相对效应测量可传递性假设比其他替代假设更合理时，所提出的方法具有显著优势。