IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v73y2017i4p1355-1365.html
   My bibliography  Save this article

Bayesian population finding with biomarkers in a randomized clinical trial

Author

Listed:
  • Satoshi Morita
  • Peter Müller

Abstract

The identification of good predictive biomarkers allows investigators to optimize the target population for a new treatment. We propose a novel utility‐based Bayesian population finding (BaPoFi) method to analyze data from a randomized clinical trial with the aim of finding a sensitive patient population. Our approach is based on casting the population finding process as a formal decision problem together with a flexible probability model, Bayesian additive regression trees (BART), to summarize observed data. The proposed method evaluates enhanced treatment effects in patient subpopulations based on counter‐factual modeling of responses to new treatment and control for each patient. In extensive simulation studies, we examine the operating characteristics of the proposed method. We compare with a Bayesian regression‐based method that implements shrinkage estimates of subgroup‐specific treatment effects. For illustration, we apply the proposed method to data from a randomized clinical trial.

Suggested Citation

  • Satoshi Morita & Peter Müller, 2017. "Bayesian population finding with biomarkers in a randomized clinical trial," Biometrics, The International Biometric Society, vol. 73(4), pages 1355-1365, December.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:4:p:1355-1365
    DOI: 10.1111/biom.12677
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.12677
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.12677?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. Yanxun Xu & Peter Müller & Abdus S. Wahed & Peter F. Thall, 2016. "Bayesian Nonparametric Estimation for Dynamic Treatment Regimes With Sequential Transition Times," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 921-950, July.
    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. Satoshi Morita & Peter Müller & Hiroyasu Abe, 2021. "A semiparametric Bayesian approach to population finding with time‐to‐event and toxicity data in a randomized clinical trial," Biometrics, The International Biometric Society, vol. 77(2), pages 634-648, June.

    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. 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.
    2. Arman Oganisian & Nandita Mitra & Jason A. Roy, 2021. "A Bayesian nonparametric model for zero‐inflated outcomes: Prediction, clustering, and causal estimation," Biometrics, The International Biometric Society, vol. 77(1), pages 125-135, March.
    3. 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.
    4. Casarin Roberto & Peruzzi Antonio, 2024. "A Dynamic Latent-Space Model for Asset Clustering," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(2), pages 379-402, April.
    5. Zhen Li & Jie Chen & Eric Laber & Fang Liu & Richard Baumgartner, 2023. "Optimal Treatment Regimes: A Review and Empirical Comparison," International Statistical Review, International Statistical Institute, vol. 91(3), pages 427-463, December.
    6. 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.
    7. Peter Müeller & Fernando A. Quintana & Garritt Page, 2018. "Nonparametric Bayesian inference in applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 175-206, June.
    8. Baojiang Chen & Ao Yuan & Jing Qin, 2022. "Pool adjacent violators algorithm–assisted learning with application on estimating optimal individualized treatment regimes," Biometrics, The International Biometric Society, vol. 78(4), pages 1475-1488, December.

    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:73:y:2017:i:4:p:1355-1365. 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.