A Bayesian credible subgroups approach to identifying patient subgroups with positive treatment effects.

ABSTRACT: Many new experimental treatments benefit only a subset of the population. Identifying the baseline covariate profiles of patients who benefit from such a treatment, rather than determining whether or not the treatment has a population-level effect, can substantially lessen the risk in undertaking a clinical trial and expose fewer patients to treatments that do not benefit them. The standard analyses for identifying patient subgroups that benefit from an experimental treatment either do not account for multiplicity, or focus on testing for the presence of treatment-covariate interactions rather than the resulting individualized treatment effects. We propose a Bayesian credible subgroups method to identify two bounding subgroups for the benefiting subgroup: one for which it is likely that al

SUBMITTER: Schnell PM 

PROVIDER: S-EPMC5102829 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature