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A semiparametric Bayesian approach to population finding with time‐to‐event and toxicity data in a randomized clinical trial

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  • Satoshi Morita
  • Peter Müller
  • Hiroyasu Abe

Abstract

A utility‐based Bayesian population finding (BaPoFi) method was proposed by Morita and Müller to analyze data from a randomized clinical trial with the aim of identifying good predictive baseline covariates for optimizing the target population for a future study. The approach casts the population finding process as a formal decision problem together with a flexible probability model using a random forest to define a regression mean function. BaPoFi is constructed to handle a single continuous or binary outcome variable. In this paper, we develop BaPoFi‐TTE as an extension of the earlier approach for clinically important cases of time‐to‐event (TTE) data with censoring, and also accounting for a toxicity outcome. We model the association of TTE data with baseline covariates using a semiparametric failure time model with a Pólya tree prior for an unknown error term and a random forest for a flexible regression mean function. We define a utility function that addresses a trade‐off between efficacy and toxicity as one of the important clinical considerations for population finding. We examine the operating characteristics of the proposed method in extensive simulation studies. For illustration, we apply the proposed method to data from a randomized oncology clinical trial. Concerns in a preliminary analysis of the same data based on a parametric model motivated the proposed more general approach.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:77:y:2021:i:2:p:634-648
    DOI: 10.1111/biom.13289
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    References listed on IDEAS

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    1. Hanson, Timothy E., 2006. "Inference for Mixtures of Finite Polya Tree Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1548-1565, December.
    2. Patrick M. Schnell & Qi Tang & Walter W. Offen & Bradley P. Carlin, 2016. "A Bayesian credible subgroups approach to identifying patient subgroups with positive treatment effects," Biometrics, The International Biometric Society, vol. 72(4), pages 1026-1036, December.
    3. 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.
    4. Stephen Walker & Bani K. Mallick, 1999. "A Bayesian Semiparametric Accelerated Failure Time Model," Biometrics, The International Biometric Society, vol. 55(2), pages 477-483, June.
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