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

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ID: 99264
2020
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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.
Reference Key
ngo2020bayesianplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ngo, Duy;Baumgartner, Richard;Mt-Isa, Shahrul;Feng, Dai;Chen, Jie;Schnell, Patrick;
Journal PloS one
Year 2020
DOI
10.1371/journal.pone.0229336
URL
Keywords
occupational health tuberculosis healthcare workers laboratory workers occupational health surveillance

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