A Bayesian Credible Subgroups Approach to Identifying Patient Subgroups with Positive Treatment Effects
*Patrick M Schnell, University of Minnesota
Keywords: Bayesian inference; Clinical trials; Heterogeneous treatment effect; Linear model; Simultaneous inference; Subgroup identification
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 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 make separate marginal inferences on each individual, which raises multiplicity issues, or focus inappropriately on the presence or absence of treatment-covariate interactions. We propose a Bayesian credible subgroups method to identify two bounding subgroups for the benefiting subgroup: one for which it is likely that all members simultaneously have a treatment effect exceeding a specified threshold and another for which it is likely that no members do. We illustrate the approach using data from an Alzheimer’s disease treatment trial and conclude with a discussion of the advantages and limitations of this approach to identifying patients for whom the treatment is beneficial.