Annual Review of Public Health· 2015Q1· Review
Mediation Analysis: A Practitioner's Guide
- 1,963citations
- Q1SCImago
- 2015year
Short summary
This review details recent mediation analysis methods for quantifying pathways and mechanisms by which exposures affect outcomes, including handling interactions, binary outcomes/mediators, and case-control designs.
AI-generated from the title and abstract; the full text is not read.
Key points
- Mediation analysis quantifies the magnitude of different pathways and mechanisms linking an exposure to an outcome.
- Methods are presented for handling exposure-mediator interactions, binary outcomes, binary mediators, and case-control designs.
- Techniques for sensitivity analysis regarding unmeasured confounding and measurement error are introduced.
- The review focuses on easily implementable methodologies for broad practical application.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Annual Review of Public Health, 2015 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics