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Causal mediation analysis for survival outcome with unobserved mediator–outcome confounders

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  • Luo, Peng
  • Geng, Zhi

Abstract

The indirect effect of the treatment on the survival outcome through the mediate variable and the direct effect of the treatment on the survival outcome are described. The relationships between the direct and indirect effects and the parameters of three models for survival analysis are provided. The conditions for identifying the direct and indirect effects of the treatment on the survival outcome with an unobserved mediator–outcome confounder vector are presented. Further the identifiability is illustrated via a simulation study. Finally, the proposed approaches are applied to a real data set to illustrate the methodology.

Suggested Citation

  • Luo, Peng & Geng, Zhi, 2016. "Causal mediation analysis for survival outcome with unobserved mediator–outcome confounders," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 336-347.
    • Handle: RePEc:eee:csdana:v:93:y:2016:i:c:p:336-347
    DOI: 10.1016/j.csda.2014.11.016
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    References listed on IDEAS

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    1. Li, Yan & Bienias, Julia L. & Bennett, David A., 2007. "Confounding in the estimation of mediation effects," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3173-3186, March.
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    Cited by:

    1. Cheng Zheng & Lei Liu, 2022. "Quantifying direct and indirect effect for longitudinal mediator and survival outcome using joint modeling approach," Biometrics, The International Biometric Society, vol. 78(3), pages 1233-1243, September.
    2. Antonio Calcagnì & Luigi Lombardi & Lorenzo Avanzi & Eduardo Pascali, 2020. "Multiple mediation analysis for interval-valued data," Statistical Papers, Springer, vol. 61(1), pages 347-369, February.

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