IDEAS home Printed from https://ideas.repec.org/a/spr/lifeda/v25y2019i4d10.1007_s10985-018-9449-0.html
   My bibliography  Save this article

Defining causal mediation with a longitudinal mediator and a survival outcome

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10985-018-9449-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10985-018-9449-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Tyler J. VanderWeele & Eric J. Tchetgen Tchetgen, 2017. "Mediation analysis with time varying exposures and mediators," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 917-938, June.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    4. 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.
    5. 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).
    6. Dawid Philip, 2021. "Decision-theoretic foundations for statistical causality," Journal of Causal Inference, De Gruyter, vol. 9(1), pages 39-77, January.
    7. Marie Skov Breum & Anders Munch & Thomas A. Gerds & Torben Martinussen, 2024. "Estimation of separable direct and indirect effects in a continuous-time illness-death model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(1), pages 143-180, January.
    8. 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.
    9. 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.
    10. Matias Janvin & Jessica G. Young & Pål C. Ryalen & Mats J. Stensrud, 2024. "Causal inference with recurrent and competing events," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 30(1), pages 59-118, January.
    11. 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.
    12. 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.
    13. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Soojin Park & Peter M. Steiner & David Kaplan, 2018. "Identification and Sensitivity Analysis for Average Causal Mediation Effects with Time-Varying Treatments and Mediators: Investigating the Underlying Mechanisms of Kindergarten Retention Policy," Psychometrika, Springer;The Psychometric Society, vol. 83(2), pages 298-320, June.
    3. Wen Wei Loh & Beatrijs Moerkerke & Tom Loeys & Stijn Vansteelandt, 2022. "Nonlinear mediation analysis with high‐dimensional mediators whose causal structure is unknown," Biometrics, The International Biometric Society, vol. 78(1), pages 46-59, March.
    4. Tat-Thang Vo & Hilary Davies-Kershaw & Ruth Hackett & Stijn Vansteelandt, 2022. "Longitudinal mediation analysis of time-to-event endpoints in the presence of competing risks," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(3), pages 380-400, July.
    5. Kara E. Rudolph & Jonathan Levy & Mark J. van der Laan, 2021. "Transporting stochastic direct and indirect effects to new populations," Biometrics, The International Biometric Society, vol. 77(1), pages 197-211, March.
    6. Lan Luo, By & Shi, Chengchun & Wang, Jitao & Wu, Zhenke & Li, Lexin, 2025. "Multivariate dynamic mediation analysis under a reinforcement learning framework," LSE Research Online Documents on Economics 127112, London School of Economics and Political Science, LSE Library.
    7. Tyler J. VanderWeele & Eric J. Tchetgen Tchetgen, 2017. "Mediation analysis with time varying exposures and mediators," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 917-938, June.
    8. Chiara Di Maria & Antonino Abbruzzo & Gianfranco Lovison, 2024. "Longitudinal mediation analysis through generalised linear mixed models: a comparison of maximum-likelihood and Bayesian estimation," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(1), pages 287-302, March.
    9. Marco Doretti & Martina Raggi & Elena Stanghellini, 2022. "Exact parametric causal mediation analysis for a binary outcome with a binary mediator," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 87-108, March.
    10. Park Soojin & Kang Suyeon & Lee Chioun & Ma Shujie, 2023. "Sensitivity analysis for causal decomposition analysis: Assessing robustness toward omitted variable bias," Journal of Causal Inference, De Gruyter, vol. 11(1), pages 1-23, January.
    11. 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.
    12. Markus Frölich & Martin Huber, 2017. "Direct and indirect treatment effects–causal chains and mediation analysis with instrumental variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1645-1666, November.
    13. Xu Qin & Jonah Deutsch & Guanglei Hong, 2021. "Unpacking Complex Mediation Mechanisms And Their Heterogeneity Between Sites In A Job Corps Evaluation," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 40(1), pages 158-190, January.
    14. 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.
    15. Aleksei Opacic, 2025. "Monotonic Path-Specific Effects: Application to Estimating Educational Returns," Papers 2508.13366, arXiv.org.
    16. Emanuel Nussli & Simon Hediger & Meta-Lina Spohn & Marloes H. Maathuis, 2024. "The effect of a strict facial-mask policy on the spread of COVID-19 in Switzerland during the early phase of the pandemic," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 160(1), pages 1-20, December.
    17. Yanyi Song & Xiang Zhou & Min Zhang & Wei Zhao & Yongmei Liu & Sharon L. R. Kardia & Ana V. Diez Roux & Belinda L. Needham & Jennifer A. Smith & Bhramar Mukherjee, 2020. "Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies," Biometrics, The International Biometric Society, vol. 76(3), pages 700-710, September.
    18. Iván Díaz & Nima S. Hejazi, 2020. "Causal mediation analysis for stochastic interventions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 661-683, July.
    19. Wodtke, Geoffrey & Zhou, Xiang, 2019. "Effect Decomposition in the Presence of Treatment-induced Confounding: A Regression-with-residuals Approach," SocArXiv 86d2k, Center for Open Science.
    20. Govert E. Bijwaard & Kieron J. Barclay, 2025. "Pathways from education to mortality, mediated through income," Health Economics, John Wiley & Sons, Ltd., vol. 34(1), pages 18-44, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:lifeda:v:25:y:2019:i:4:d:10.1007_s10985-018-9449-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.