Miles Caleb, Kanki Phyllis, Meloni Seema, Tchetgen Eric Tchetgen
Department of Biostatistics, University of California, Berkeley 94720-7358.
Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA 02115.
J Causal Inference. 2017 Sep;5(2). doi: 10.1515/jci-2016-0004. Epub 2017 Feb 28.
In causal mediation analysis, nonparametric identification of the natural indirect effect typically relies on, in addition to no unobserved pre-exposure confounding, fundamental assumptions of (i) so-called "cross-world-countterfactuals" independence and (ii) no exposure-induced confounding. When the mediator is binary, bounds for partial identification have been given when neither assumption is made, or alternatively when assuming only (ii). We extend existing bounds to the case of a polytomous mediator, and provide bounds for the case assuming only (i). We apply these bounds to data from the Harvard PEPFAR program in Nigeria, where we evaluate the extent to which the effects of antiretroviral therapy on virological failure are mediated by a patient's adherence, and show that inference on this effect is somewhat sensitive to model assumptions.
在因果中介分析中,除了不存在未观察到的暴露前混杂因素外,自然间接效应的非参数识别通常还依赖于以下基本假设:(i)所谓的“跨世界反事实”独立性,以及(ii)不存在暴露引起的混杂因素。当中介变量为二元变量时,在不做任何假设的情况下,或者仅假设(ii)的情况下,已经给出了部分识别的界限。我们将现有界限扩展到多分类中介变量的情况,并给出仅假设(i)时的界限。我们将这些界限应用于尼日利亚哈佛总统紧急艾滋病救援计划(PEPFAR)的数据,在该数据中我们评估抗逆转录病毒疗法对病毒学失败的影响在多大程度上是由患者的依从性介导的,并表明关于这种影响的推断对模型假设有些敏感。