Causal mediation analysis with multiple mediators.


Daniel, RM; De Stavola, BL; Cousens, SN; Vansteelandt, S; (2014) Causal mediation analysis with multiple mediators. Biometrics, 71 (1). pp. 1-14. ISSN 0006-341X DOI: https://doi.org/10.1111/biom.12248

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Abstract

: In diverse fields of empirical research-including many in the biological sciences-attempts are made to decompose the effect of an exposure on an outcome into its effects via a number of different pathways. For example, we may wish to separate the effect of heavy alcohol consumption on systolic blood pressure (SBP) into effects via body mass index (BMI), via gamma-glutamyl transpeptidase (GGT), and via other pathways. Much progress has been made, mainly due to contributions from the field of causal inference, in understanding the precise nature of statistical estimands that capture such intuitive effects, the assumptions under which they can be identified, and statistical methods for doing so. These contributions have focused almost entirely on settings with a single mediator, or a set of mediators considered en bloc; in many applications, however, researchers attempt a much more ambitious decomposition into numerous path-specific effects through many mediators. In this article, we give counterfactual definitions of such path-specific estimands in settings with multiple mediators, when earlier mediators may affect later ones, showing that there are many ways in which decomposition can be done. We discuss the strong assumptions under which the effects are identified, suggesting a sensitivity analysis approach when a particular subset of the assumptions cannot be justified. These ideas are illustrated using data on alcohol consumption, SBP, BMI, and GGT from the Izhevsk Family Study. We aim to bridge the gap from "single mediator theory" to "multiple mediator practice," highlighting the ambitious nature of this endeavor and giving practical suggestions on how to proceed.<br/>

Item Type: Article
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology
Faculty of Epidemiology and Population Health > Dept of Medical Statistics
Research Centre: Centre for Statistical Methodology
Tropical Epidemiology Group
PubMed ID: 25351114
Web of Science ID: 352585700002
URI: http://researchonline.lshtm.ac.uk/id/eprint/2019621

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