Cited by 1,985Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyBayesian Modeling and Causal Inference

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Mediation Analysis: A Practitioner's Guide

VanderWeele, Tyler J.·2016·Annual Review of Public Health

DOI 10.1146/annurev-publhealth-032315-021402

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Abstract

This article provides an overview of recent developments in mediation analysis, that is, analyses used to assess the relative magnitude of different pathways and mechanisms by which an exposure may affect an outcome. Traditional approaches to mediation in the biomedical and social sciences are described. Attention is given to the confounding assumptions required for a causal interpretation of direct and indirect effect estimates. Methods from the causal inference literature to conduct mediation in the presence of exposure-mediator interactions, binary outcomes, binary mediators, and case-control study designs are presented. Sensitivity analysis techniques for unmeasured confounding and measurement error are introduced. Discussion is given to extensions to time-to-event outcomes and multiple mediators. Further flexible modeling strategies arising from the precise counterfactual definitions of direct and indirect effects are also described. The focus throughout is on methodology that is easily implementable in practice across a broad range of potential applications.

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Authors: VanderWeele, T. J.
Affiliations: Harvard University (US)
Corresponding author: VanderWeele, Tyler J.
ORCID: VanderWeele, Tyler J.
Venue: Annual Review of Public Health
Publisher: Annual Reviews
Published: 2016-03-18
Volume: 37
Issue: 1
Pages: 17-32
ISSN: 0163-7525 (ISSN-L)
1545-2093
DOI: 10.1146/annurev-publhealth-032315-021402
OpenAlex: W2193561325
Cited by: 1,985
References: 58
Citations by year: 2026: 298 · 2025: 340 · 2024: 338 · 2023: 260 · 2022: 226 · 2021: 195 · 2020: 136 · 2019: 103 · 2018: 54 · 2017: 21 · 2016: 11
FWCI: 4.78
Type: journal article
Language: en
Open access: closed
Locations: Annual Review of Public Health (publishedVersion)
PubMed (publishedVersion)
Funders: National Institutes of Health
National Institute of Environmental Health Sciences (ES017876)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Keywords: mediation analysis, direct and indirect effects, causal inference, binary outcomes, case-control study, sensitivity analysis, unmeasured confounding, measurement error, time-to-event outcomes, multiple mediators, flexible modeling
MeSH terms: Time Factors, Epidemiologic Research Design, Bias, Causality, Confounding Factors, Epidemiologic, Logistic Models

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