Decomposition of the total effect for two mediators: A natural mediated interaction effect framework.

IF 1.7 4区 医学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Journal of Causal Inference Pub Date : 2022-01-01 DOI:10.1515/jci-2020-0017
Xin Gao, Li Li, Li Luo
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引用次数: 2

Abstract

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.

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两个中介的总效应分解:一个自然中介的相互作用效应框架。
中介分析已在许多学科中使用,通过包含中介来解释暴露变量和结果变量之间观察到的关系的机制或过程。在过去十年中,暴露变量的总效应(TE)分解为表征中介途径和相互作用的效应已获得越来越多的兴趣。在这项工作中,我们为两个中介是因果顺序或非顺序的场景开发了分解。目前这一领域的发展主要集中在没有相互作用成分的分解或有相互作用但假设介质之间没有因果顺序的分解。我们提出了一个新的概念,称为自然介导的相互作用(MI)效应,它捕获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分解为仅由于中介,仅由于互动,中介和互动,在反事实框架内既不中介也不互动的影响。最后,我们使用模拟数据将我们提出的分解方法与非顺序双介质场景中的现有方法进行了比较,并使用真实数据分析说明了顺序双介质场景的拟议分解方法。
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来源期刊
Journal of Causal Inference
Journal of Causal Inference Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
1.90
自引率
14.30%
发文量
15
审稿时长
86 weeks
期刊介绍: Journal of Causal Inference (JCI) publishes papers on theoretical and applied causal research across the range of academic disciplines that use quantitative tools to study causality.
期刊最新文献
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