A subgroup cluster-based Bayesian adaptive design for precision medicine

In precision medicine, a patient is treated with targeted therapies that are predicted to be effective based on the patient's baseline characteristics such as biomarker profiles. Oftentimes, patient subgroups are unknown and must be learned through inference using observed data. We present SCUBA, a Subgroup ClUster-based Bayesian Adaptive design aiming to fulfill two simultaneous goals in a clinical trial, 1) to treatments enrich the allocation of each subgroup of patients to their precision and desirable treatments and 2) to report multiple subgroup-treatment pairs (STPs). Using random partitions and semiparametric Bayesian models, SCUBA provides coherent and probabilistic assessment of potential patient subgroups and their associated targeted therapies. Each STP can then be used for future confirmatory studies for regulatory approval. Through extensive simulation studies, we present an application of SCUBA to an innovative clinical trial in gastroesphogeal cancer.