Department of Epidemiology, Mailman School of Public Health, Columbia University; and Division of Biostatistics, Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
Biostatistics. 2022 Jul 18;23(3):789-806. doi: 10.1093/biostatistics/kxaa057.
The same intervention can produce different effects in different sites. Existing transport mediation estimators can estimate the extent to which such differences can be explained by differences in compositional factors and the mechanisms by which mediating or intermediate variables are produced; however, they are limited to consider a single, binary mediator. We propose novel nonparametric estimators of transported interventional (in)direct effects that consider multiple, high-dimensional mediators and a single, binary intermediate variable. They are multiply robust, efficient, asymptotically normal, and can incorporate data-adaptive estimation of nuisance parameters. They can be applied to understand differences in treatment effects across sites and/or to predict treatment effects in a target site based on outcome data in source sites.
同一干预措施在不同部位可能产生不同的效果。现有的传输中介估计量可以估计这种差异在多大程度上可以用组成因素的差异以及中介或中间变量产生的机制来解释；然而，它们仅限于考虑单个二元中介变量。我们提出了新的非参数传输干预（间接）效应估计量，这些估计量考虑了多个高维中介变量和单个二元中间变量。它们是多重稳健的、有效的、渐近正态的，并且可以结合对讨厌参数的自适应数据估计。它们可以用于了解不同部位的治疗效果差异，或者基于源部位的结果数据预测目标部位的治疗效果。
