IDEAS home Printed from https://ideas.repec.org/a/bpj/causin/v5y2017i2p24n4.html
   My bibliography  Save this article

Longitudinal Mediation Analysis with Time-varying Mediators and Exposures, with Application to Survival Outcomes

Author

Listed:
  • Zheng Wenjing

    (Division of Biostatistics, University of California, 101 Havilland Hall, Berkeley, CA 94720, USA)

  • van der Laan Mark

    (Department of School of Public Health, University of California, 101 Havilland Hall, Berkeley, CA 94720, USA)

Abstract

1 In this paper, we study the effect of a time-varying exposure mediated by a time-varying intermediate variable. We consider general longitudinal settings, including survival outcomes. At a given time point, the exposure and mediator of interest are influenced by past covariates, mediators and exposures, and affect future covariates, mediators and exposures. Right censoring, if present, occurs in response to past history. To address the challenges in mediation analysis that are unique to these settings, we propose a formulation in terms of random interventions based on conditional distributions for the mediator. This formulation, in particular, allows for well-defined natural direct and indirect effects in the survival setting, and natural decomposition of the standard total effect. Upon establishing identifiability and the corresponding statistical estimands, we derive the efficient influence curves and establish their robustness properties. Applying Targeted Maximum Likelihood Estimation, we use these efficient influence curves to construct multiply robust and efficient estimators. We also present an inverse probability weighted estimator and a nested non-targeted substitution estimator for these parameters.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:causin:v:5:y:2017:i:2:p:24:n:4
    DOI: 10.1515/jci-2016-0006
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jci-2016-0006
    Download Restriction: no
    File URL: https://libkey.io/10.1515/jci-2016-0006?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    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. 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.
    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. 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.
    5. Park Soojin & Kang Suyeon & Ma Shujie & Lee Chioun, 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.
    6. Zhichao Jiang & Shu Yang & Peng Ding, 2022. "Multiply robust estimation of causal effects under principal ignorability," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(4), pages 1423-1445, September.
    7. 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.
    8. 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.

    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. 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.
    2. 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.
    3. 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.
    4. Bijlsma, Maarten J. & Wilson, Ben, 2020. "Modelling the socio-economic determinants of fertility: a mediation analysis using the parametric g-formula," LSE Research Online Documents on Economics 102414, London School of Economics and Political Science, LSE Library.
    5. David G. Lugo‐Palacios & Jonathan M. Clarke & Søren Rud Kristensen, 2023. "Back to basics: A mediation analysis approach to addressing the fundamental questions of integrated care evaluations," Health Economics, John Wiley & Sons, Ltd., vol. 32(9), pages 2080-2097, September.
    6. 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.
    7. Kara E. Rudolph & Iván Díaz, 2022. "When the ends do not justify the means: Learning who is predicted to have harmful indirect effects," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 573-589, December.
    8. 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.
    9. Alessandro Magrini, 2022. "Mediation analysis in recursive systems of distributed-lag linear regressions," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(3), pages 1535-1555, June.
    10. Annabelle Bédard & Zhen Li & Wassila Ait-hadad & Carlos A. Camargo & Bénédicte Leynaert & Christophe Pison & Orianne Dumas & Raphaëlle Varraso, 2021. "The Role of Nutritional Factors in Asthma: Challenges and Opportunities for Epidemiological Research," IJERPH, MDPI, vol. 18(6), pages 1-20, March.
    11. McKetta, Sarah & Prins, Seth J. & Hasin, Deborah & Patrick, Megan E. & Keyes, Katherine M., 2022. "Structural sexism and Women's alcohol use in the United States, 1988–2016," Social Science & Medicine, Elsevier, vol. 301(C).
    12. Park Soojin & Kang Suyeon & Ma Shujie & Lee Chioun, 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.
    13. Jessica Nisén & Maarten J. Bijlsma & Pekka Martikainen & Ben Wilson & Mikko Myrskylä, 2019. "The gendered impacts of delayed parenthood on educational and labor market outcomes: a dynamic analysis of population-level effects over young adulthood," MPIDR Working Papers WP-2019-017, Max Planck Institute for Demographic Research, Rostock, Germany.
    14. Maarten J. Bijlsma & Ben Wilson, 2017. "Modelling the socio-economic determinants of fertility: a mediation analysis using the parametric g-formula," MPIDR Working Papers WP-2017-013, Max Planck Institute for Demographic Research, Rostock, Germany.
    15. Bijlsma, Maarten J. & Tarkiainen, Lasse & Myrskylä, Mikko & Martikainen, Pekka, 2017. "Unemployment and subsequent depression: A mediation analysis using the parametric G-formula," Social Science & Medicine, Elsevier, vol. 194(C), pages 142-150.
    16. Cheng Lin & Adel Daoud & Maria Branden, 2022. "To What Extent Do Disadvantaged Neighborhoods Mediate Social Assistance Dependency? Evidence from Sweden," Papers 2206.04773, arXiv.org, revised Aug 2022.
    17. Maarten J. Bijlsma & Ben Wilson, 2020. "Modelling the socio‐economic determinants of fertility: a mediation analysis using the parametric g‐formula," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(2), pages 493-513, February.
    18. Evan Munro & David Jones & Jennifer Brennan & Roland Nelet & Vahab Mirrokni & Jean Pouget-Abadie, 2023. "Causal Estimation of User Learning in Personalized Systems," Papers 2306.00485, arXiv.org.

    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:bpj:causin:v:5:y:2017:i:2:p:24:n:4. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.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.