IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v79y2023i3p2116-2126.html
   My bibliography  Save this article

A general framework for subgroup detection via one‐step value difference estimation

Author

Listed:
  • Dana Johnson
  • Wenbin Lu
  • Marie Davidian

Abstract

Recent statistical methodology for precision medicine has focused on either identification of subgroups with enhanced treatment effects or estimating optimal treatment decision rules so that treatment is allocated in a way that maximizes, on average, predefined patient outcomes. Less attention has been given to subgroup testing, which involves evaluation of whether at least a subgroup of the population benefits from an investigative treatment, compared to some control or standard of care. In this work, we propose a general framework for testing for the existence of a subgroup with enhanced treatment effects based on the difference of the estimated value functions under an estimated optimal treatment regime and a fixed regime that assigns everyone to the same treatment. Our proposed test does not require specification of the parametric form of the subgroup and allows heterogeneous treatment effects within the subgroup. The test applies to cases when the outcome of interest is either a time‐to‐event or a (uncensored) scalar, and is valid at the exceptional law. To demonstrate the empirical performance of the proposed test, we study the type I error and power of the test statistics in simulations and also apply our test to data from a Phase III trial in patients with hematological malignancies.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:2116-2126
    DOI: 10.1111/biom.13711
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13711
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13711?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. Yingqi Zhao & Donglin Zeng & A. John Rush & Michael R. Kosorok, 2012. "Estimating Individualized Treatment Rules Using Outcome Weighted Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1106-1118, September.
    2. Juan Shen & Xuming He, 2015. "Inference for Subgroup Analysis With a Structured Logistic-Normal Mixture Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 303-312, March.
    3. 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.
    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. Susan Wei & Michael R Kosorok, 2018. "The change-plane Cox model," Biometrika, Biometrika Trust, vol. 105(4), pages 891-903.
    6. 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.
    7. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355, May.
    8. Baqun Zhang & Anastasios A. Tsiatis & Eric B. Laber & Marie Davidian, 2013. "Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions," Biometrika, Biometrika Trust, vol. 100(3), pages 681-694.
    9. 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.
    10. 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.
    11. Chengchun Shi & Wenbin Lu & Rui Song, 2020. "A Sparse Random Projection-Based Test for Overall Qualitative Treatment Effects," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1201-1213, July.
    12. Yilun Sun & Lu Wang, 2021. "Stochastic Tree Search for Estimating Optimal Dynamic Treatment Regimes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(533), pages 421-432, January.
    13. Ailin Fan & Rui Song & Wenbin Lu, 2017. "Change-Plane Analysis for Subgroup Detection and Sample Size Calculation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 769-778, April.
    14. 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.
    15. Lihui Zhao & Lu Tian & Tianxi Cai & Brian Claggett & L. J. Wei, 2013. "Effectively Selecting a Target Population for a Future Comparative Study," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 527-539, June.
    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. 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.
    2. Giorgos Bakoyannis, 2023. "Estimating optimal individualized treatment rules with multistate processes," Biometrics, The International Biometric Society, vol. 79(4), pages 2830-2842, December.
    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. 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.
    5. Ying Huang & Juhee Cho & Youyi Fong, 2021. "Threshold‐based subgroup testing in logistic regression models in two‐phase sampling designs," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 291-311, March.
    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. Jingxiang Chen & Yufeng Liu & Donglin Zeng & Rui Song & Yingqi Zhao & Michael R. Kosorok, 2016. "Comment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 942-947, July.
    8. 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.
    9. 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.
    10. 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.
    11. Wei Liu & Zhiwei Zhang & Lei Nie & Guoxing Soon, 2017. "A Case Study in Personalized Medicine: Rilpivirine Versus Efavirenz for Treatment-Naive HIV Patients," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1381-1392, October.
    12. Shuai Chen & Lu Tian & Tianxi Cai & Menggang Yu, 2017. "A general statistical framework for subgroup identification and comparative treatment scoring," Biometrics, The International Biometric Society, vol. 73(4), pages 1199-1209, December.
    13. 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.
    14. 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).
    15. Caiyun Fan & Wenbin Lu & Rui Song & Yong Zhou, 2017. "Concordance-assisted learning for estimating optimal individualized treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1565-1582, November.
    16. Michael P. Wallace & Erica E. M. Moodie, 2015. "Doubly‐robust dynamic treatment regimen estimation via weighted least squares," Biometrics, The International Biometric Society, vol. 71(3), pages 636-644, September.
    17. 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.
    18. Wallace, Michael P. & Moodie, Erica E. M. & Stephens, David A., 2017. "Dynamic Treatment Regimen Estimation via Regression-Based Techniques: Introducing R Package DTRreg," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 80(i02).
    19. Peng Jin & Wenbin Lu & Yu Chen & Mengling Liu, 2023. "Change‐plane analysis for subgroup detection with a continuous treatment," Biometrics, The International Biometric Society, vol. 79(3), pages 1920-1933, September.
    20. 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.

    More about this item

    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:bla:biomet:v:79:y:2023:i:3:p:2116-2126. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.