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Assessing the Robustness of Mediation Analysis Results Using Multiverse Analysis
Judith J M Rijnhart1, Jos W R Twisk2, Dorly J H Deeg2
1Department of Epidemiology and Data Science, Amsterdam UMC, Location VU University Medical Center, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands. j.rijnhart@amsterdamumc.nl.
Summary
Multiverse analysis helps assess the robustness of research findings against analytical choices. This study provides guidance for applying multiverse analysis to mediation analysis, improving the reliability of effect estimates.
Area of Science:
- Psychology
- Statistics
- Research Methodology
Background:
- Replication is crucial in empirical studies, but results often fail to replicate.
- Multiverse analysis assesses result robustness against analytical decisions.
- Uptake of multiverse analysis is low due to limited guidance, especially for complex analyses like mediation.
Purpose of the Study:
- To provide an overview and worked example of multiverse analysis for mediation analysis.
- To demonstrate how to assess the robustness of effect estimates in mediation analysis.
- To address the challenges researchers face in applying multiverse analysis to mediation.
Main Methods:
- Applied multiverse analysis to a real-life dataset (Longitudinal Aging Study Amsterdam).
- Utilized specification curves to visualize the impact of analytical decisions.
- Compared the number of analytical decisions in mediation versus bivariate analyses.
Main Results:
- Mediation analysis involves more data analytical decisions than bivariate analysis.
- Multiverse analysis effectively demonstrates the impact of these decisions on effect estimates.
- Specification curves revealed the influence on direct, indirect, and total effects.
Conclusions:
- Multiverse analysis is a valuable tool for assessing the robustness of mediation analysis effect estimates.
- This methodology can inform and strengthen replication studies.
- Further research is needed to advance multiverse analysis techniques.
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