Monte Carlo approaches to frequentist multiplicity-adjusted benefiting subgroup identification.

Abstract

One common goal of subgroup analyses is to determine the subgroup of the population for which a given treatment is effective. Like most problems in subgroup analyses, this benefiting subgroup identification requires careful attention to multiple testing considerations, especially Type I error inflation. To partially address these concerns, the credible subgroups approach provides a pair of bounding subgroups for the benefiting subgroup, constructed so that with high posterior probability one is contained by the benefiting subgroup while the other contains the benefiting subgroup. To date, this approach has been presented within the Bayesian paradigm only, and requires sampling from the posterior of a Bayesian model. Additionally, in many cases, such as regulatory submission, guarantees of frequentist operating characteristics are helpful or necessary. We present Monte Carlo approaches to constructing confidence subgroups, frequentist analogues to credible subgroups that replace the posterior distribution with an estimate of the joint distribution of personalized treatment effect estimates, and yield frequentist interpretations and coverage guarantees. The estimated joint distribution is produced using either draws from asymptotic sampling distributions of estimated model parameters, or bootstrap resampling schemes. The approach is applied to a publicly available dataset from randomized trials of Alzheimer's disease treatments.