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Bayesian credible subgroup identification for treatment effectiveness in time-to-event data

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Listed:
  • Duy Ngo
  • Richard Baumgartner
  • Shahrul Mt-Isa
  • Dai Feng
  • Jie Chen
  • Patrick Schnell

Abstract

Due to differential treatment responses of patients to pharmacotherapy, drug development and practice in medicine are concerned with personalized medicine, which includes identifying subgroups of population that exhibit differential treatment effect. For time–to–event data, available methods only focus on detecting and testing treatment–by–covariate interactions and may not consider multiplicity. In this work, we introduce the Bayesian credible subgroups approach for time–to–event endpoints. It provides two bounding subgroups for the true benefiting subgroup: one which is likely to be contained by the benefiting subgroup and one which is likely to contain the benefiting subgroup. A personalized treatment effect is estimated by two common measures of survival time: the hazard ratio and restricted mean survival time. We apply the method to identify benefiting subgroups in a case study of prostate carcinoma patients and a simulated large clinical dataset.

Suggested Citation

  • Duy Ngo & Richard Baumgartner & Shahrul Mt-Isa & Dai Feng & Jie Chen & Patrick Schnell, 2020. "Bayesian credible subgroup identification for treatment effectiveness in time-to-event data," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-19, February.
    • Handle: RePEc:plo:pone00:0229336
    DOI: 10.1371/journal.pone.0229336
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    References listed on IDEAS

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    1. 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.
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