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Sequential Ignorability and Dismissible Treatment Components to Identify Mediation Effects

Author

Listed:
  • Yuhao Deng

    (Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA)

  • Haoyu Wei

    (Department of Economics, University of California, San Diego, La Jolla, CA 92161, USA)

  • Xia Xiao

    (Geely Automobile Holdings Limited, Hangzhou 310051, China)

  • Yuan Zhang

    (National Research Institute for Health and Family Planning, Beijing 100081, China)

  • Yuanmin Huang

    (The Center for Applied Mathematics of Guangxi, Guangxi Normal University, Guilin 541004, China)

Abstract

Mediation analysis is a useful tool to study the mechanism of how a treatment exerts effects on the outcome. Classical mediation analysis requires a sequential ignorability assumption to rule out cross-world reliance of the potential outcome of interest on the counterfactual mediator in order to identify the natural direct and indirect effects. In recent years, the separable effects framework has adopted dismissible treatment components to identify the separable direct and indirect effects. In this article, we compare the sequential ignorability and dismissible treatment components for longitudinal outcomes and time-to-event outcomes with time-varying confounding and random censoring. We argue that the dismissible treatment components assumption has advantages in interpretation and identification over sequential ignorability, whereas these two conditions lead to identical estimators for the direct and indirect effects. As an illustration, we study the effect of transplant modalities on overall survival mediated by leukemia relapse in patients undergoing allogeneic stem cell transplantation. We find that Haplo-SCT reduces the risk of overall mortality through reducing the risk of relapse, and Haplo-SCT can serve as an alternative to MSDT in allogeneic stem cell transplantation.

Suggested Citation

  • Yuhao Deng & Haoyu Wei & Xia Xiao & Yuan Zhang & Yuanmin Huang, 2024. "Sequential Ignorability and Dismissible Treatment Components to Identify Mediation Effects," Mathematics, MDPI, vol. 12(15), pages 1-20, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:15:p:2332-:d:1442993
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    References listed on IDEAS

    as
    1. Yen‐Tsung Huang, 2021. "Causal mediation of semicompeting risks," Biometrics, The International Biometric Society, vol. 77(4), pages 1143-1154, December.
    2. Yen‐Tsung Huang, 2021. "Rejoinder to “Causal mediation of semicompeting risks”," Biometrics, The International Biometric Society, vol. 77(4), pages 1170-1174, December.
    3. D. Zeng & D. Y. Lin, 2007. "Maximum likelihood estimation in semiparametric regression models with censored data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 507-564, September.
    4. Mats J. Stensrud & Miguel A. Hernán & Eric J Tchetgen Tchetgen & James M. Robins & Vanessa Didelez & Jessica G. Young, 2021. "A generalized theory of separable effects in competing event settings," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(4), pages 588-631, October.
    5. Fan Xia & Kwun Chuen Gary Chan, 2023. "Identification, Semiparametric Efficiency, and Quadruply Robust Estimation in Mediation Analysis with Treatment-Induced Confounding," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(542), pages 1272-1281, April.
    6. Fan Xia & Kwun Chuen Gary Chan, 2022. "Decomposition, identification and multiply robust estimation of natural mediation effects with multiple mediators [Generalized causal mediation analysis]," Biometrika, Biometrika Trust, vol. 109(4), pages 1085-1100.
    Full references (including those not matched with items on IDEAS)

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