Open AccessBayesian credible subgroup identification for treatment effectiveness in time-to-event data
Author(s) -
Duy Hoa Ngo,
Richard N. Baumgartner,
Shahrul MtIsa,
Feng Dai,
Jie Chen,
Patrick Schnell
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0229336
Subject(s) - bayesian probability , event data , event (particle physics) , statistics , identification (biology) , subgroup analysis , bayes' theorem , computational biology , computer science , medicine , econometrics , mathematics , biology , confidence interval , physics , quantum mechanics , covariate , botany
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.