A Nonparametric Bayesian Design for Drug Combination Cancer Trials

Abstract: We propose an adaptive design for early phase drug combination cancer trials with the goal of estimating the maximum tolerated dose (MTD). A nonparametric Bayesian model, using beta priors truncated to the set of partially ordered dose combinations, is used to describe the probability of dose limiting toxicity (DLT). Dose allocation between successive cohorts of patients is estimated using a modified Continual Reassessment scheme. The updated probabilities of DLT are calculated with a Gibbs sampler that employs a weighting mechanism to calibrate the influence of data versus the prior. At the end of the trial, we recommend one or more dose combinations as the MTD based on our proposed algorithm. The design operating characteristics indicate that our method is comparable with existing methods. As an illustration, we apply our method to a phase I clinical trial of CB-839 and Gemcitabine.