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

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.

Gatekeeping strategies for clinical trials that do not require all primary effects to be significant.

In this paper we describe methods for addressing multiplicity issues arising in the analysis of clinical trials with multiple endpoints and/or multiple dose levels. Efficient 'gatekeeping strategies' for multiplicity problems of this kind are developed. One family of hypotheses (comprising the primary objectives) is treated as a 'gatekeeper', and the other family or families (comprising secondary and tertiary objectives) are tested only if one or more gatekeeper hypotheses have been rejected.