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On Causal Mediation Analysis with a Survival Outcome

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  • Tchetgen Tchetgen Eric J

Abstract

Suppose that having established a marginal total effect of a point exposure on a time-to-event outcome, an investigator wishes to decompose this effect into its direct and indirect pathways, also known as natural direct and indirect effects, mediated by a variable known to occur after the exposure and prior to the outcome. This paper proposes a theory of estimation of natural direct and indirect effects in two important semiparametric models for a failure time outcome. The underlying survival model for the marginal total effect and thus for the direct and indirect effects, can either be a marginal structural Cox proportional hazards model, or a marginal structural additive hazards model. The proposed theory delivers new estimators for mediation analysis in each of these models, with appealing robustness properties. Specifically, in order to guarantee ignorability with respect to the exposure and mediator variables, the approach, which is multiply robust, allows the investigator to use several flexible working models to adjust for confounding by a large number of pre-exposure variables. Multiple robustness is appealing because it only requires a subset of working models to be correct for consistency; furthermore, the analyst need not know which subset of working models is in fact correct to report valid inferences. Finally, a novel semiparametric sensitivity analysis technique is developed for each of these models, to assess the impact on inference, of a violation of the assumption of ignorability of the mediator.

Suggested Citation

  • Tchetgen Tchetgen Eric J, 2011. "On Causal Mediation Analysis with a Survival Outcome," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-38, September.
    • Handle: RePEc:bpj:ijbist:v:7:y:2011:i:1:n:33
    DOI: 10.2202/1557-4679.1351
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    References listed on IDEAS

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    1. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
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    Cited by:

    1. Wei Wang & Jeffrey M. Albert, 2017. "Causal mediation analysis for the Cox proportional hazards model with a smooth baseline hazard estimator," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(4), pages 741-757, August.
    2. Yen‐Tsung Huang, 2021. "Causal mediation of semicompeting risks," Biometrics, The International Biometric Society, vol. 77(4), pages 1143-1154, December.
    3. Adriana Sudzinova & Jaroslav Rosenberger & Roy E. Stewart & Jitse P. Dijk & Sijmen A. Reijneveld, 2016. "Does poorer self-rated health mediate the effect of Roma ethnicity on mortality in patients with coronary artery disease after coronaro-angiography?," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 61(3), pages 375-382, April.
    4. Cheng Zheng & Xiao-Hua Zhou, 2017. "Causal mediation analysis on failure time outcome without sequential ignorability," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 533-559, October.
    5. Tchetgen Tchetgen, Eric J. & Robins, James, 2012. "On parametrization, robustness and sensitivity analysis in a marginal structural Cox proportional hazards model for point exposure," Statistics & Probability Letters, Elsevier, vol. 82(5), pages 907-915.

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