The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Mediation Analysis: A Practitioner's Guide
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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“Recent methodologic developments have addressed challenging issues in causality for single [77] and multiple randomized trials [25], evolving interventions [12], natural experiments [78, 79], and multilevel [78] as well as multidimensional situations [80].”
From “Scaling-out” evidence-based interventions to new populations or new health care delivery systems (2017) · cites this paper as [77]
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Cites
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Asymptotic Confidence Intervals for Indirect Effects in Structural Equation Models
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Introduction to Statistical Mediation Analysis
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A general approach to causal mediation analysis.
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Process Analysis: Estimating Mediation in Treatment Evaluations
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Valeri, L., & VanderWeele, T. J. (2013) · Psychological Methods · doi:10.1037/a0031034 · Cited by 2,132
Self-citationIdentifiability and exchangeability for direct and indirect effects
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Explanation in Causal Inference: Methods for Mediation and Interaction
VanderWeele, T. J. (2015) · Cited by 1,540
Self-citation
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Multivariable association discovery in population-scale meta-omics studies
Mallick, H., Rahnavard, A., McIver, L. J., Ma, S., Zhang, Y., Nguyen, L. H., Tickle, T. L., Weingart, G., Ren, B., Schwager, E. H., et al. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009442 · Cited by 2,596
Mendelian randomisation for mediation analysis: current methods and challenges for implementation
Carter, A. R., Sanderson, E., Hammerton, G., Richmond, R. C., Davey Smith, G., Heron, J., Taylor, A. E., Davies, N. M., & Howe, L. D. (2021) · European Journal of Epidemiology · doi:10.1007/s10654-021-00757-1 · Cited by 1,215
Hayes, A. F., & Rockwood, N. (2019) · American Behavioral Scientist · doi:10.1177/0002764219859633 · Cited by 1,127
Multivariable Mendelian Randomization and Mediation
Sanderson, E. (2021) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a038984 · Cited by 827
The contribution of health behaviors to socioeconomic inequalities in health: A systematic review
Petrović, D., de Mestral, C., Bochud, M., Bartley, M., Mika Kivimaki, B., Vineis, P., Mackenbach, J. P., & Stringhini, S. (2018) · Preventive Medicine · doi:10.1016/j.ypmed.2018.05.003 · Cited by 541
Metadata
- Authors
- VanderWeele, T. J.
- Affiliations
- Harvard University (US)
- Corresponding author
- 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
- 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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