IDEAS home Printed from https://ideas.repec.org/a/spr/lifeda/v31y2025i2d10.1007_s10985-025-09652-3.html
   My bibliography  Save this article

A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding

Author

Listed:
  • Xinyuan Chen

    (Mississippi State University)

  • Liangyuan Hu

    (Rutgers School of Public Health)

  • Fan Li

    (Yale School of Public Health)

Abstract

In longitudinal observational studies with time-to-event outcomes, a common objective in causal analysis is to estimate the causal survival curve under hypothetical intervention scenarios. The g-formula is a useful tool for this analysis. To enhance the traditional parametric g-formula, we developed an alternative g-formula estimator, which incorporates the Bayesian Additive Regression Trees into the modeling of the time-evolving generative components, aiming to mitigate the bias due to model misspecification. We focus on binary time-varying treatments and introduce a general class of g-formulas for discrete survival data that can incorporate longitudinal balancing scores. The minimum sufficient formulation of these longitudinal balancing scores is linked to the nature of treatment strategies, i.e., static or dynamic. For each type of treatment strategy, we provide posterior sampling algorithms. We conducted simulations to illustrate the empirical performance of the proposed method and demonstrate its practical utility using data from the Yale New Haven Health System’s electronic health records.

Suggested Citation

  • Xinyuan Chen & Liangyuan Hu & Fan Li, 2025. "A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 31(2), pages 394-421, April.
    • Handle: RePEc:spr:lifeda:v:31:y:2025:i:2:d:10.1007_s10985-025-09652-3
    DOI: 10.1007/s10985-025-09652-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10985-025-09652-3
    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/s10985-025-09652-3?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. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Joseph Antonelli & Michael J. Daniels, 2019. "Discussion of PENCOMP," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 24-27, January.
    3. Corwin M. Zigler & Krista Watts & Robert W. Yeh & Yun Wang & Brent A. Coull & Francesca Dominici, 2013. "Model Feedback in Bayesian Propensity Score Estimation," Biometrics, The International Biometric Society, vol. 69(1), pages 263-273, March.
    4. Maria Josefsson & Michael J. Daniels, 2021. "Bayesian semi‐parametric G‐computation for causal inference in a cohort study with MNAR dropout and death," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 398-414, March.
    5. Joseph Antonelli & Georgia Papadogeorgou & Francesca Dominici, 2022. "Causal inference in high dimensions: A marriage between Bayesian modeling and good frequentist properties," Biometrics, The International Biometric Society, vol. 78(1), pages 100-114, March.
    6. Liangyuan Hu & Joseph W. Hogan, 2019. "Causal comparative effectiveness analysis of dynamic continuous‐time treatment initiation rules with sparsely measured outcomes and death," Biometrics, The International Biometric Society, vol. 75(2), pages 695-707, June.
    7. Bijlsma, Maarten J. & Wilson, Ben & Tarkiainen, Lasse & Myrskylä, Mikko & Martikainen, Pekka, 2019. "The impact of unemployment on antidepressant purchasing: adjusting for unobserved time-constant confounding in the g-formula," LSE Research Online Documents on Economics 101216, London School of Economics and Political Science, LSE Library.
    8. Lan Wen & Jessica G. Young & James M. Robins & Miguel A. Hernán, 2021. "Parametric g‐formula implementations for causal survival analyses," Biometrics, The International Biometric Society, vol. 77(2), pages 740-753, June.
    9. Aristide C. Achy-Brou & Constantine E. Frangakis & Michael Griswold, 2010. "Estimating Treatment Effects of Longitudinal Designs using Regression Models on Propensity Scores," Biometrics, The International Biometric Society, vol. 66(3), pages 824-833, September.
    10. Shu Yang & Guido W. Imbens & Zhanglin Cui & Douglas E. Faries & Zbigniew Kadziola, 2016. "Propensity score matching and subclassification in observational studies with multi‐level treatments," Biometrics, The International Biometric Society, vol. 72(4), pages 1055-1065, December.
    11. Tingting Zhou & Michael R. Elliott & Roderick J. A. Little, 2019. "Penalized Spline of Propensity Methods for Treatment Comparison: Rejoinder," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 35-38, January.
    12. Lan Wen & Miguel A. Hernán & James M. Robins, 2022. "Multiply robust estimators of causal effects for survival outcomes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 1304-1328, September.
    13. Georgia Papadogeorgou & Fan Li, 2019. "Discussion of “Penalized Spline of Propensity Methods for Treatment Comparison”," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 32-35, January.
    14. Alberto Caron & Gianluca Baio & Ioanna Manolopoulou, 2022. "Estimating individual treatment effects using non‐parametric regression models: A review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1115-1149, July.
    15. Tingting Zhou & Michael R. Elliott & Roderick J. A. Little, 2019. "Penalized Spline of Propensity Methods for Treatment Comparison," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(525), pages 1-19, January.
    16. Corwin Matthew Zigler & Francesca Dominici, 2014. "Uncertainty in Propensity Score Estimation: Bayesian Methods for Variable Selection and Model-Averaged Causal Effects," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 95-107, March.
    Full references (including those not matched with items on IDEAS)

    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. Tingting Zhou & Michael R. Elliott & Roderick J. A. Little, 2021. "Robust Causal Estimation from Observational Studies Using Penalized Spline of Propensity Score for Treatment Comparison," Stats, MDPI, vol. 4(2), pages 1-21, June.
    2. Antonio R. Linero, 2023. "Prior and posterior checking of implicit causal assumptions," Biometrics, The International Biometric Society, vol. 79(4), pages 3153-3164, December.
    3. Ao Yuan & Anqi Yin & Ming T. Tan, 2021. "Enhanced Doubly Robust Procedure for Causal Inference," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(3), pages 454-478, December.
    4. Ray Chambers & Setareh Ranjbar & Nicola Salvati & Barbara Pacini, 2022. "Weighting, informativeness and causal inference, with an application to rainfall enhancement," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1584-1612, October.
    5. Maria Josefsson & Michael J. Daniels, 2021. "Bayesian semi‐parametric G‐computation for causal inference in a cohort study with MNAR dropout and death," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 398-414, March.
    6. Tingting Zhou & Michael R. Elliott & Roderick J. A. Little, 2022. "Addressing Disparities in the Propensity Score Distributions for Treatment Comparisons from Observational Studies," Stats, MDPI, vol. 5(4), pages 1-17, December.
    7. Brian J. Reich & Shu Yang & Yawen Guan, 2022. "Discussion on “Spatial+: A novel approach to spatial confounding” by Dupont, Wood, and Augustin," Biometrics, The International Biometric Society, vol. 78(4), pages 1291-1294, December.
    8. Davide Viviano & Jelena Bradic, 2021. "Dynamic covariate balancing: estimating treatment effects over time with potential local projections," Papers 2103.01280, arXiv.org, revised Jan 2024.
    9. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
    10. Agboola, Oluwagbenga David & Yu, Han, 2023. "Neighborhood-based cross fitting approach to treatment effects with high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).
    11. Dingke Tang & Dehan Kong & Wenliang Pan & Linbo Wang, 2023. "Ultra‐high dimensional variable selection for doubly robust causal inference," Biometrics, The International Biometric Society, vol. 79(2), pages 903-914, June.
    12. F. Swen Kuh & Grace S. Chiu & Anton H. Westveld, 2019. "Modeling National Latent Socioeconomic Health and Examination of Policy Effects via Causal Inference," Papers 1911.00512, arXiv.org.
    13. Swen Kuh & Grace S. Chiu & Anton H. Westveld, 2020. "Latent Causal Socioeconomic Health Index," Papers 2009.12217, arXiv.org, revised Oct 2023.
    14. Antonacci,Paulo & Muhammad Khudadad Chattha, 2024. "Evaluation of Door-to-Door Tax Enforcement Strategy in Indonesia," Policy Research Working Paper Series 10901, The World Bank.
    15. Dandan Xu & Michael J. Daniels & Almut G. Winterstein, 2018. "A Bayesian nonparametric approach to causal inference on quantiles," Biometrics, The International Biometric Society, vol. 74(3), pages 986-996, September.
    16. Heejun Shin & Joseph Antonelli, 2023. "Improved inference for doubly robust estimators of heterogeneous treatment effects," Biometrics, The International Biometric Society, vol. 79(4), pages 3140-3152, December.
    17. Ganesh Karapakula, 2023. "Stable Probability Weighting: Large-Sample and Finite-Sample Estimation and Inference Methods for Heterogeneous Causal Effects of Multivalued Treatments Under Limited Overlap," Papers 2301.05703, arXiv.org, revised Jan 2023.
    18. Susan M. Shortreed & Ashkan Ertefaie, 2017. "Outcome‐adaptive lasso: Variable selection for causal inference," Biometrics, The International Biometric Society, vol. 73(4), pages 1111-1122, December.
    19. Matthew Cefalu & Francesca Dominici & Nils Arvold & Giovanni Parmigiani, 2017. "Model averaged double robust estimation," Biometrics, The International Biometric Society, vol. 73(2), pages 410-421, June.
    20. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.

    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:31:y:2025:i:2:d:10.1007_s10985-025-09652-3. 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.