Gao Xin, Li Li, Luo Li
Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM, 87131, USA; Comprehensive Cancer Center, University of New Mexico, Albuquerque, NM, 87131, USA.
Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM, 87131, USA.
J Causal Inference. 2022 Jan;10(1):18-44. doi: 10.1515/jci-2020-0017. Epub 2022 Mar 19.
Mediation analysis has been used in many disciplines to explain the mechanism or process that underlies an observed relationship between an exposure variable and an outcome variable via the inclusion of mediators. Decompositions of the total effect (TE) of an exposure variable into effects characterizing mediation pathways and interactions have gained an increasing amount of interest in the last decade. In this work, we develop decompositions for scenarios where two mediators are causally sequential or non-sequential. Current developments in this area have primarily focused on either decompositions without interaction components or with interactions but assuming no causally sequential order between the mediators. We propose a new concept called natural mediated interaction (MI) effect that captures the two-way and three-way interactions for both scenarios and extends the two-way MIs in the literature. We develop a unified approach for decomposing the TE into the effects that are due to mediation only, interaction only, both mediation and interaction, neither mediation nor interaction within the counterfactual framework. Finally, we compare our proposed decomposition to an existing method in a non-sequential two-mediator scenario using simulated data, and illustrate the proposed decomposition for a sequential two-mediator scenario using a real data analysis.
中介分析已在许多学科中得到应用，通过纳入中介变量来解释暴露变量与结果变量之间观察到的关系背后的机制或过程。在过去十年中，将暴露变量的总效应（TE）分解为表征中介路径和相互作用的效应越来越受到关注。在这项工作中，我们针对两个中介变量因果顺序或非顺序的情况开发了分解方法。该领域目前的进展主要集中在没有相互作用成分的分解方法，或者有相互作用但假设中介变量之间没有因果顺序的分解方法。我们提出了一个名为自然中介相互作用（MI）效应的新概念，它捕捉了两种情况下的双向和三向相互作用，并扩展了文献中的双向MI。我们开发了一种统一的方法，将TE分解为仅由中介、仅由相互作用、中介和相互作用两者、在反事实框架内既非中介也非相互作用所导致的效应。最后，我们使用模拟数据在非顺序双中介变量的情况下将我们提出的分解方法与现有方法进行比较，并通过实际数据分析说明了顺序双中介变量情况下的分解方法。
