Mediation analysis in epidemiology: methods, interpretation and bias
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The impact of the three main sources of potential bias in the traditional approach to mediation analyses are reviewed and discussed: (i) mediator-outcome confounding; (ii) exposure-mediator interaction and (iii) mediATOR- outcome confounding affected by the exposure.Abstract:
In epidemiological studies it is often necessary to disentangle the pathways that link an exposure to an outcome. Typically the aim is to identify the total effect of the exposure on the outcome, the effect of the exposure that acts through a given set of mediators of interest (indirect effect) and the effect of the exposure unexplained by those same mediators (direct effect). The traditional approach to mediation analysis is based on adjusting for the mediator in standard regression models to estimate the direct effect. However, several methodological papers have shown that under a number of circumstances this traditional approach may produce flawed conclusions. Through a better understanding of the causal structure of the variables involved in the analysis, with a formal definition of direct and indirect effects in a counterfactual framework, alternative analytical methods have been introduced to improve the validity and interpretation of mediation analysis. In this paper, we review and discuss the impact of the three main sources of potential bias in the traditional approach to mediation analyses: (i) mediator-outcome confounding;(ii) exposure-mediator interaction and (iii) mediator-outcome confounding affected by the exposure. We provide examples and discuss the impact these sources have in terms of bias.read more
Citations
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Advances in Mediation Analysis: A Survey and Synthesis of New Developments
TL;DR: This review provides a survey and synthesis of four areas of active methodological research: (a) mediation analysis for longitudinal data, (b) causal inference for indirect effects, (c) mediationAnalysis for discrete and nonnormal variables, and (d) mediation assessment in multilevel designs.
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Applying the Bradford Hill criteria in the 21st century: how data integration has changed causal inference in molecular epidemiology.
TL;DR: Novel ways by which researchers can apply and interpret the Bradford Hill criteria when considering data gathered using modern molecular techniques, such as epigenetics, biomarkers, mechanistic toxicology, and genotoxicology are discussed.
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Mendelian randomization in cardiometabolic disease: challenges in evaluating causality
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Prenatal exposure to maternal smoking and offspring DNA methylation across the lifecourse: findings from the Avon Longitudinal Study of Parents and Children (ALSPAC)
Rebecca C Richmond,Andrew J Simpkin,G. Woodward,Tom R. Gaunt,Oliver Lyttleton,Wendy L. McArdle,Susan M. Ring,Andrew D A C Smith,Nicholas J. Timpson,Kate Tilling,George Davey Smith,Caroline L Relton +11 more
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Interpretation and Identification of Causal Mediation
TL;DR: It is shown that natural effects can be identified by methods that go beyond standard adjustment for confounders, applicable to observational studies in which treatment assignment remains confounded with the mediator or with the outcome.
References
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