VanderWeele Tyler J, Chiba Yasutaka
Departments of Epidemiology and biostatistics, Harvard School of Public Health, Boston, MA, USA.
Division of Biostatistics, Clinical Research Center, Kinki University School of Medicine, Osaka, Japan.
Epidemiol Biostat Public Health. 2014;11(2). doi: 10.2427/9027.
Questions of mediation are often of interest in reasoning about mechanisms, and methods have been developed to address these questions. However, these methods make strong assumptions about the absence of confounding. Even if exposure is randomized, there may be mediator-outcome confounding variables. Inference about direct and indirect effects is particularly challenging if these mediator-outcome confounders are affected by the exposure because in this case these effects are not identified irrespective of whether data is available on these exposure-induced mediator-outcome confounders. In this paper, we provide a sensitivity analysis technique for natural direct and indirect effects that is applicable even if there are mediator-outcome confounders affected by the exposure. We give techniques for both the difference and risk ratio scales and compare the technique to other possible approaches.
中介问题在机制推理中常常备受关注,并且已经开发出一些方法来解决这些问题。然而,这些方法对不存在混杂因素做出了强有力的假设。即使暴露是随机分配的,也可能存在中介-结局混杂变量。如果这些中介-结局混杂因素受到暴露的影响,那么对直接效应和间接效应的推断就会特别具有挑战性,因为在这种情况下,无论是否有关于这些暴露诱导的中介-结局混杂因素的数据,这些效应都无法识别。在本文中,我们提供了一种针对自然直接效应和间接效应的敏感性分析技术,即使存在受暴露影响的中介-结局混杂因素,该技术也适用。我们给出了差值尺度和风险比尺度的技术,并将该技术与其他可能的方法进行比较。