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A Multivariate Generalized Linear Model Approach to Mediation Analysis and Application of Confidence Ellipses

Author

Listed:
  • Brandie D. Wagner

    (University of Colorado Denver
    University of Colorado School of Medicine)

  • Miranda Kroehl

    (University of Colorado Denver)

  • Ryan Gan

    (University of Colorado)

  • Susan K. Mikulich-Gilbertson

    (University of Colorado Denver
    University of Colorado School of Medicine)

  • Scott D. Sagel

    (University of Colorado School of Medicine)

  • Paula D. Riggs

    (University of Colorado School of Medicine)

  • Talia Brown

    (University of Colorado)

  • Janet Snell-Bergeon

    (University of Colorado School of Medicine)

  • Gary O. Zerbe

    (University of Colorado Denver)

Abstract

Mediation analysis evaluates the significance of an intermediate variable on the causal pathway between an exposure and an outcome. One commonly utilized test for mediation involves evaluation of counterfactual effects, estimated from separate regression models, corresponding to a composite null hypothesis. However, the “compositeness” of this null hypothesis is not commonly acknowledged and accounted for in mediation analyses. We describe a generalized multivariate approach in which these separate regression models are fit simultaneously in a single parsimonious model. This multivariate modeling approach can reproduce standard mediation analysis and has notable advantages over separate regression models, including the ability to combine distributions in the exponential family with any link functions and perform likelihood-based tests of some relevant hypotheses using existing software. We propose the use of a novel visual representation of confidence intervals of the two estimates for the indirect path with the use of a confidence ellipse. The calculation of the confidence ellipse is facilitated by the multivariate approach, can test the components of the composite null hypothesis under a single experiment-wise type I error rate, and does not require estimation of the standard error of the product of coefficients from two separate regressions. This method is illustrated using three examples. The first compares results between the multivariate method and separate regression models. The second example illustrates the proposed methods in the presence of an exposure–mediator interaction, missing data and confounding, and the third example utilizes these proposed methods for an outcome and mediator with negative binomial distributions.

Suggested Citation

  • Brandie D. Wagner & Miranda Kroehl & Ryan Gan & Susan K. Mikulich-Gilbertson & Scott D. Sagel & Paula D. Riggs & Talia Brown & Janet Snell-Bergeon & Gary O. Zerbe, 2018. "A Multivariate Generalized Linear Model Approach to Mediation Analysis and Application of Confidence Ellipses," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 139-159, April.
  • Handle: RePEc:spr:stabio:v:10:y:2018:i:1:d:10.1007_s12561-017-9191-2
    DOI: 10.1007/s12561-017-9191-2
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    References listed on IDEAS

    as
    1. Jeffrey M. Albert & Suchitra Nelson, 2011. "Generalized Causal Mediation Analysis," Biometrics, The International Biometric Society, vol. 67(3), pages 1028-1038, September.
    2. Pearl Judea, 2010. "An Introduction to Causal Inference," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-62, February.
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    Cited by:

    1. Chiara Di Maria & Antonino Abbruzzo & Gianfranco Lovison, 2022. "Networks as mediating variables: a Bayesian latent space approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 1015-1035, October.
    2. Caubet, Miguel & Samoilenko, Mariia & Drouin, Simon & Sinnett, Daniel & Krajinovic, Maja & Laverdière, Caroline & Marcil, Valérie & Lefebvre, Geneviève, 2023. "Bayesian joint modeling for causal mediation analysis with a binary outcome and a binary mediator: Exploring the role of obesity in the association between cranial radiation therapy for childhood acut," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).

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