Hypothesis tests of indirect effects for multiple mediators

John Kidd (), Annie Green Howard, Heather M. Highland, Penny Gordon-Larsen, Michael Patrick Bancks, Mercedes Carnethon and Dan-Yu Lin
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John Kidd: University of North Carolina at Chapel Hill
Annie Green Howard: University of North Carolina at Chapel Hill
Heather M. Highland: University of North Carolina at Chapel Hill
Penny Gordon-Larsen: University of North Carolina at Chapel Hill
Michael Patrick Bancks: Wake Forest University School of Medicine
Mercedes Carnethon: Northwestern University
Dan-Yu Lin: University of North Carolina at Chapel Hill

Statistical Methods & Applications, 2025, vol. 34, issue 1, No 6, 113-127

Abstract: Abstract Mediation analysis seeks to determine whether an independent variable affects a response directly or whether it does so indirectly, by way of a mediator or mediators. Scenarios that assume a single mediation are often overly simplistic, and analyses that include multiple mediators are becoming more common, particularly with the incorporation of high-dimensional data. Surprisingly, however, little attention has been given to multiple mediator and interaction effects. In this article, we propose new methods for testing the null hypothesis of no indirect effect with multiple mediators and interaction effects. We allow the estimators of the path effects to be possibly correlated; we also consider the practice of using confidence intervals to determine whether a mediation effect is zero. We compare the performance of our proposed method with existing methods through extensive simulation studies. Finally, we provide an application to data from the Coronary Artery Risk Development in Young Adults (CARDIA) study.

Keywords: Mediation pathway; Confidence intervals; Joint significance test; Mediation analysis; Missing data (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10260-024-00777-7

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