Decomposition of the Total Effect in the Presence of Multiple Mediators and Interactions. Issue 6 (13th November 2017)

• Record Type: Journal Article
• Authors: Bellavia, Andrea; Valeri, Linda
• Is Part Of: American journal of epidemiology. Volume 187:Issue 6(2018)
• Page Start: 1311
• Page End: 1318
• Publication Date: 2017-11-13
• Subjects: causal inference -- effect decomposition -- interaction -- mediation; Epidemiology -- Periodicals; Public health -- Periodicals; 614.4
• DOI: 10.1093/aje/kwx355
• Languages: English
• ISSNs: 0002-9262

Abstract

Abstract: Mediation analysis allows decomposing a total effect into a direct effect of the exposure on the outcome and an indirect effect operating through a number of possible hypothesized pathways. Recent studies have provided formal definitions of direct and indirect effects when multiple mediators are of interest and have described parametric and semiparametric methods for their estimation. Investigating direct and indirect effects with multiple mediators, however, can be challenging in the presence of multiple exposure-mediator and mediator-mediator interactions. In this paper we derive a decomposition of the total effect that unifies mediation and interaction when multiple mediators are present. We illustrate the properties of the proposed framework in a secondary analysis of a pragmatic trial for the treatment of schizophrenia. The decomposition is employed to investigate the interplay of side effects and psychiatric symptoms in explaining the effect of antipsychotic medication on quality of life in schizophrenia patients. Our result offers a valuable tool to identify the proportions of total effect due to mediation and interaction when more than one mediator is present, providing the finest decomposition of the total effect that unifies multiple mediators and interactions.