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Bayesian treatment screening and selection using subgroup‐specific utilities of response and toxicity

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  • Juhee Lee
  • Peter F. Thall
  • Pavlos Msaouel

Abstract

A Bayesian design is proposed for randomized phase II clinical trials that screen multiple experimental treatments compared to an active control based on ordinal categorical toxicity and response. The underlying model and design account for patient heterogeneity characterized by ordered prognostic subgroups. All decision criteria are subgroup specific, including interim rules for dropping unsafe or ineffective treatments, and criteria for selecting optimal treatments at the end of the trial. The design requires an elicited utility function of the two outcomes that varies with the subgroups. Final treatment selections are based on posterior mean utilities. The methodology is illustrated by a trial of targeted agents for metastatic renal cancer, which motivated the design methodology. In the context of this application, the design is evaluated by computer simulation, including comparison to three designs that conduct separate trials within subgroups, or conduct one trial while ignoring subgroups, or base treatment selection on estimated response rates while ignoring toxicity.

Suggested Citation

  • Juhee Lee & Peter F. Thall & Pavlos Msaouel, 2023. "Bayesian treatment screening and selection using subgroup‐specific utilities of response and toxicity," Biometrics, The International Biometric Society, vol. 79(3), pages 2458-2473, September.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:2458-2473
    DOI: 10.1111/biom.13738
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    References listed on IDEAS

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    1. Yanxun Xu & Lorenzo Trippa & Peter Müller & Yuan Ji, 2016. "Subgroup-Based Adaptive (SUBA) Designs for Multi-arm Biomarker Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(1), pages 159-180, June.
    2. Malka Gorfine & Li Hsu, 2011. "Frailty-Based Competing Risks Model for Multivariate Survival Data," Biometrics, The International Biometric Society, vol. 67(2), pages 415-426, June.
    3. Elena M. Buzaianu & Pinyuen Chen & Lifang Hsu, 2022. "A Curtailed Procedure for Selecting Among Treatments With Two Bernoulli Endpoints," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 320-339, May.
    4. Juhee Lee & Peter F. Thall & Katy Rezvani, 2019. "Optimizing natural killer cell doses for heterogeneous cancer patients on the basis of multiple event times," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 68(2), pages 461-474, February.
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