Using Subject Level Covariate Information in Bayesian Mixture Models for Basket Trials.

Basket trials are gaining importance with advancements in precision medicine. A basket trial evaluates one or more treatments for efficacy among more than one cancer type (histology) in a single clinical trial. Compared to traditional designs, basket trials can reduce the time required for testing and, by pooling across cancer types, they also allow the drugs to be tested for rare cancers. However, the potential for heterogeneity in treatment efficacy in different cancer types poses modeling challenges. Our model aims to assist the cancer type level go/no-go decisions in the initial phases of the trial through a latent cluster structure that incorporates subject-level covariate information. We model subjects' responses using a Bayesian mixture model where the mixture weights depend on a measure of similarly among subjects' covariate values. A simulation study demonstrates that our proposed Bayesian Partition Model with Covariates (BPMx) robustly estimates basket-level mean response and can provide insight about the latent cluster structure. We further illustrate the model using response data from a published basket trial.