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Defining causal mediation with a longitudinal mediator and a survival outcome

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  • Vanessa Didelez

    (Leibniz Institute for Prevention Research and Epidemiology – BIPS
    University of Bremen)

Abstract

In the context of causal mediation analysis, prevailing notions of direct and indirect effects are based on nested counterfactuals. These can be problematic regarding interpretation and identifiability especially when the mediator is a time-dependent process and the outcome is survival or, more generally, a time-to-event outcome. We propose and discuss an alternative definition of mediated effects that does not suffer from these problems, and is more transparent than the current alternatives. Our proposal is based on the extended graphical approach of Robins and Richardson (in: Shrout (ed) Causality and psychopathology: finding the determinants of disorders and their cures, Oxford University Press, Oxford, 2011), where treatment is decomposed into different components, or aspects, along different causal paths corresponding to real world mechanisms. This is an interesting alternative motivation for any causal mediation setting, but especially for survival outcomes. We give assumptions allowing identifiability of such alternative mediated effects leading to the familiar mediation g-formula (Robins in Math Model 7:1393, 1986); this implies that a number of available methods of estimation can be applied.

Suggested Citation

  • Vanessa Didelez, 2019. "Defining causal mediation with a longitudinal mediator and a survival outcome," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 593-610, October.
    • Handle: RePEc:spr:lifeda:v:25:y:2019:i:4:d:10.1007_s10985-018-9449-0
    DOI: 10.1007/s10985-018-9449-0
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    References listed on IDEAS

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    1. R. M. Daniel & B. L. De Stavola & S. N. Cousens & S. Vansteelandt, 2015. "Causal mediation analysis with multiple mediators," Biometrics, The International Biometric Society, vol. 71(1), pages 1-14, March.
    2. Zheng Wenjing & van der Laan Mark, 2017. "Longitudinal Mediation Analysis with Time-varying Mediators and Exposures, with Application to Survival Outcomes," Journal of Causal Inference, De Gruyter, vol. 5(2), pages 1-24, September.
    3. Sara Geneletti, 2007. "Identifying direct and indirect effects in a non‐counterfactual framework," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 199-215, April.
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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. Cai Xiaoxuan & Loh Wen Wei & Crawford Forrest W., 2021. "Identification of causal intervention effects under contagion," Journal of Causal Inference, De Gruyter, vol. 9(1), pages 9-38, January.
    3. Shuxi Zeng & Elizabeth C. Lange & Elizabeth A. Archie & Fernando A. Campos & Susan C. Alberts & Fan Li, 2023. "A Causal Mediation Model for Longitudinal Mediators and Survival Outcomes with an Application to Animal Behavior," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 197-218, June.
    4. Kwun Chuen Gary Chan & Fei Gao & Fan Xia, 2021. "Discussion on “Causal mediation of semicompeting risks” by Yen‐Tsung Huang," Biometrics, The International Biometric Society, vol. 77(4), pages 1155-1159, December.
    5. Mats J. Stensrud & Jessica G. Young & Torben Martinussen, 2021. "Discussion on “Causal mediation of semicompeting risks” by Yen‐Tsung Huang," Biometrics, The International Biometric Society, vol. 77(4), pages 1160-1164, December.
    6. Isabel R. Fulcher & Ilya Shpitser & Vanessa Didelez & Kali Zhou & Daniel O. Scharfstein, 2021. "Discussion on “Causal mediation of semicompeting risks” by Yen‐Tsung Huang," Biometrics, The International Biometric Society, vol. 77(4), pages 1165-1169, December.
    7. Ørnulf Borgan & Håkon K. Gjessing, 2019. "Special issue dedicated to Odd O. Aalen," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 587-592, October.
    8. Oisín Ryan & Ellen L. Hamaker, 2022. "Time to Intervene: A Continuous-Time Approach to Network Analysis and Centrality," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 214-252, March.
    9. Díaz Iván & Williams Nicholas & Rudolph Kara E., 2023. "Efficient and flexible mediation analysis with time-varying mediators, treatments, and confounders," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-17, January.
    10. Rubino, Claudio & Di Maria, Chiara & Abbruzzo, Antonino & Ferrante, Mauro, 2022. "Socio-economic inequality, interregional mobility and mortality among cancer patients: A mediation analysis approach," Socio-Economic Planning Sciences, Elsevier, vol. 82(PA).
    11. Dawid Philip, 2021. "Decision-theoretic foundations for statistical causality," Journal of Causal Inference, De Gruyter, vol. 9(1), pages 39-77, January.

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