Mediation Analysis with Multiple Mediators

T J VanderWeele, S Vansteelandt, T J VanderWeele, S Vansteelandt

Abstract

Recent advances in the causal inference literature on mediation have extended traditional approaches to direct and indirect effects to settings that allow for interactions and non-linearities. In this paper, these approaches from causal inference are further extended to settings in which multiple mediators may be of interest. Two analytic approaches, one based on regression and one based on weighting are proposed to estimate the effect mediated through multiple mediators and the effects through other pathways. The approaches proposed here accommodate exposure-mediator interactions and, to a certain extent, mediator-mediator interactions as well. The methods handle binary or continuous mediators and binary, continuous or count outcomes. When the mediators affect one another, the strategy of trying to assess direct and indirect effects one mediator at a time will in general fail; the approach given in this paper can still be used. A characterization is moreover given as to when the sum of the mediated effects for multiple mediators considered separately will be equal to the mediated effect of all of the mediators considered jointly. The approach proposed in this paper is robust to unmeasured common causes of two or more mediators.

Keywords: Direct and indirect effects; joint effects mediation; regression; weighting.

Figures

Figure 1
Figure 1
Mediation with a single mediator M, exposure A, outcome Y, and confounders C.
Figure 2
Figure 2
Mediation with two mediators of interest.
Figure 3
Figure 3
Two mediators with an unmeasured common cause.
Figure 4
Figure 4
Two mediators in which one affects the other and they share an unmeasured common cause U.

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Source: PubMed

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