IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v219y2025ics0167715225000033.html
   My bibliography  Save this article

Estimation of the optimal treatment regimes with multiple treatments under proportional hazards model

Author

Listed:
  • Fang, Yuexin
  • Tan, Xiangyong
  • Li, Qian

Abstract

We propose a novel proportional hazards model that include an unknown baseline covariate effect and the interaction between multiple treatments and covariates on censored survival data. Doubly robust estimating equations constructed by utilizing the A-learning methodology and time-dependent propensity score. The asymptotic properties of the proposed estimators are established under the correct specification of either the baseline effect model or the propensity score model. Extensive simulation studies, along with an application, demonstrate the efficacy of the proposed approach.

Suggested Citation

  • Fang, Yuexin & Tan, Xiangyong & Li, Qian, 2025. "Estimation of the optimal treatment regimes with multiple treatments under proportional hazards model," Statistics & Probability Letters, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:stapro:v:219:y:2025:i:c:s0167715225000033
    DOI: 10.1016/j.spl.2025.110357
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715225000033
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2025.110357?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Baqun Zhang & Anastasios A. Tsiatis & Eric B. Laber & Marie Davidian, 2012. "A Robust Method for Estimating Optimal Treatment Regimes," Biometrics, The International Biometric Society, vol. 68(4), pages 1010-1018, December.
    2. Rebecca Hager & Anastasios A. Tsiatis & Marie Davidian, 2018. "Optimal two‐stage dynamic treatment regimes from a classification perspective with censored survival data," Biometrics, The International Biometric Society, vol. 74(4), pages 1180-1192, December.
    3. Hunyong Cho & Shannon T Holloway & David J Couper & Michael R Kosorok, 2023. "Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring," Biometrika, Biometrika Trust, vol. 110(2), pages 395-410.
    4. Y. Q. Zhao & D. Zeng & E. B. Laber & R. Song & M. Yuan & M. R. Kosorok, 2015. "Doubly robust learning for estimating individualized treatment with censored data," Biometrika, Biometrika Trust, vol. 102(1), pages 151-168.
    5. Cheng-Han Yang & Yu-Jen Cheng, 2024. "A model-free variable screening method for optimal treatment regimes with high-dimensional survival data," Biometrika, Biometrika Trust, vol. 111(4), pages 1369-1386.
    6. Runchao Jiang & Wenbin Lu & Rui Song & Marie Davidian, 2017. "On estimation of optimal treatment regimes for maximizing t-year survival probability," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1165-1185, September.
    7. Xiaofei Bai & Anastasios A. Tsiatis & Sean M. O'Brien, 2013. "Doubly-Robust Estimators of Treatment-Specific Survival Distributions in Observational Studies with Stratified Sampling," Biometrics, The International Biometric Society, vol. 69(4), pages 830-839, December.
    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. Dana Johnson & Wenbin Lu & Marie Davidian, 2023. "A general framework for subgroup detection via one‐step value difference estimation," Biometrics, The International Biometric Society, vol. 79(3), pages 2116-2126, September.
    2. Zhang, Haixiang & Huang, Jian & Sun, Liuquan, 2020. "A rank-based approach to estimating monotone individualized two treatment regimes," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    3. Yingchao Zhong & Chang Wang & Lu Wang, 2021. "Survival Augmented Patient Preference Incorporated Reinforcement Learning to Evaluate Tailoring Variables for Personalized Healthcare," Stats, MDPI, vol. 4(4), pages 1-17, September.
    4. Giorgos Bakoyannis, 2023. "Estimating optimal individualized treatment rules with multistate processes," Biometrics, The International Biometric Society, vol. 79(4), pages 2830-2842, December.
    5. Jin Wang & Donglin Zeng & D. Y. Lin, 2022. "Semiparametric single-index models for optimal treatment regimens with censored outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(4), pages 744-763, October.
    6. He, Yizeng & Kim, Soyoung & Kim, Mi-Ok & Saber, Wael & Ahn, Kwang Woo, 2021. "Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    7. Xin Chen & Rui Song & Jiajia Zhang & Swann Arp Adams & Liuquan Sun & Wenbin Lu, 2022. "On estimating optimal regime for treatment initiation time based on restricted mean residual lifetime," Biometrics, The International Biometric Society, vol. 78(4), pages 1377-1389, December.
    8. Emily L. Butler & Eric B. Laber & Sonia M. Davis & Michael R. Kosorok, 2018. "Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules," Biometrics, The International Biometric Society, vol. 74(1), pages 18-26, March.
    9. Q. Clairon & R. Henderson & N. J. Young & E. D. Wilson & C. J. Taylor, 2021. "Adaptive treatment and robust control," Biometrics, The International Biometric Society, vol. 77(1), pages 223-236, March.
    10. Lingyun Lyu & Yu Cheng & Abdus S. Wahed, 2023. "Imputation‐based Q‐learning for optimizing dynamic treatment regimes with right‐censored survival outcome," Biometrics, The International Biometric Society, vol. 79(4), pages 3676-3689, December.
    11. Yunan Wu & Lan Wang, 2021. "Resampling‐based confidence intervals for model‐free robust inference on optimal treatment regimes," Biometrics, The International Biometric Society, vol. 77(2), pages 465-476, June.
    12. Xin Qiu & Donglin Zeng & Yuanjia Wang, 2018. "Estimation and evaluation of linear individualized treatment rules to guarantee performance," Biometrics, The International Biometric Society, vol. 74(2), pages 517-528, June.
    13. Ruoqing Zhu & Ying-Qi Zhao & Guanhua Chen & Shuangge Ma & Hongyu Zhao, 2017. "Greedy outcome weighted tree learning of optimal personalized treatment rules," Biometrics, The International Biometric Society, vol. 73(2), pages 391-400, June.
    14. Weibin Mo & Yufeng Liu, 2022. "Efficient learning of optimal individualized treatment rules for heteroscedastic or misspecified treatment‐free effect models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 440-472, April.
    15. Xiaofei Bai & Anastasios A. Tsiatis & Wenbin Lu & Rui Song, 2017. "Optimal treatment regimes for survival endpoints using a locally-efficient doubly-robust estimator from a classification perspective," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 585-604, October.
    16. Yizhe Xu & Tom H. Greene & Adam P. Bress & Brian C. Sauer & Brandon K. Bellows & Yue Zhang & William S. Weintraub & Andrew E. Moran & Jincheng Shen, 2022. "Estimating the optimal individualized treatment rule from a cost‐effectiveness perspective," Biometrics, The International Biometric Society, vol. 78(1), pages 337-351, March.
    17. Qingxia Chen & Fan Zhang & Ming-Hui Chen & Xiuyu Julie Cong, 2020. "Estimation of treatment effects and model diagnostics with two-way time-varying treatment switching: an application to a head and neck study," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 685-707, October.
    18. Yaoyao Xu & Menggang Yu & Ying‐Qi Zhao & Quefeng Li & Sijian Wang & Jun Shao, 2015. "Regularized outcome weighted subgroup identification for differential treatment effects," Biometrics, The International Biometric Society, vol. 71(3), pages 645-653, September.
    19. Muxuan Liang & Menggang Yu, 2023. "Relative contrast estimation and inference for treatment recommendation," Biometrics, The International Biometric Society, vol. 79(4), pages 2920-2932, December.
    20. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.

    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:eee:stapro:v:219:y:2025:i:c:s0167715225000033. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.