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Mediation analysis methods used in observational research: a scoping review and recommendations
Judith J M Rijnhart1, Sophia J Lamp2, Matthew J Valente3
1Department of Epidemiology and Data Science, Amsterdam UMC, Location VU University Medical Center, Amsterdam Public Health Research Institute, PO Box 7057, 1007, MB, Amsterdam, The Netherlands. j.rijnhart@amsterdamumc.nl.
BMC Medical Research Methodology
|October 25, 2021
Summary
Causal mediation analysis is underutilized in observational studies. Recommendations include adopting causal methods and improving software for complex models to ensure reliable effect estimates.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Mediation analysis has advanced with causal mediation analysis (CMA) based on the counterfactual framework.
- Previous reviews indicated low CMA uptake in experimental studies.
- This review focuses on mediation analysis in observational epidemiologic studies (2015-2019).
Purpose of the Study:
- To review methodological characteristics of mediation analyses in observational studies.
- To provide recommendations for future mediation analysis applications.
Main Methods:
- Searched MEDLINE and EMBASE for observational studies (2015-2019) using mediation analysis.
- Extracted data on mediation model characteristics and analysis methods.
Main Results:
- 174 studies were included; 70.7% used traditional methods.
- Causal mediation analysis was infrequently used for complex models (e.g., multiple mediators).
- Most studies adjusted for measured confounders but lacked sensitivity analyses for unmeasured confounders and did not assess exposure-mediator interaction.
Conclusions:
- Researchers should use causal mediation analysis for causal interpretation and assess causal assumptions.
- Increased CMA uptake requires tutorials and software development for complex models.
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