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Hypothesis tests of indirect effects for multiple mediators

Author

Listed:
  • John Kidd

    (University of North Carolina at Chapel Hill)

  • Annie Green Howard

    (University of North Carolina at Chapel Hill
    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
    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)

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.

Suggested Citation

  • John Kidd & Annie Green Howard & Heather M. Highland & Penny Gordon-Larsen & Michael Patrick Bancks & Mercedes Carnethon & Dan-Yu Lin, 2025. "Hypothesis tests of indirect effects for multiple mediators," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 34(1), pages 113-127, March.
    • Handle: RePEc:spr:stmapp:v:34:y:2025:i:1:d:10.1007_s10260-024-00777-7
    DOI: 10.1007/s10260-024-00777-7
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    References listed on IDEAS

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    1. Kai Wang, 2018. "Understanding Power Anomalies in Mediation Analysis," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 387-406, June.
    2. Tyler J. Vanderweele, 2011. "Controlled Direct and Mediated Effects: Definition, Identification and Bounds," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 38(3), pages 551-563, September.
    3. Yen-Tsung Huang & Wen-Chi Pan, 2016. "Hypothesis test of mediation effect in causal mediation model with high-dimensional continuous mediators," Biometrics, The International Biometric Society, vol. 72(2), pages 402-413, June.
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