COCA: a randomized Bayesian design integrating dose optimization and component contribution assessment for combination therapies

In cancer treatment, the development of combination therapies requires demonstrating the contribution of each individual drug and optimizing the dose during early-phase trials. This necessitates a large sample size, presenting formidable obstacles for drug developers. To address this issue, we propose a 2-stage randomized phase II design that seamlessly integrates combination dose optimization with component contribution assessment. In stage 1, the optimal combination dose is determined by maximizing the risk-benefit tradeoff across multiple candidate combination doses. In stage 2, a multi-arm randomized phase is initiated to evaluate the contribution of each component within the combination therapy. To increase trial efficiency and reduce the sample size, efficacy data from both stages are adaptively combined using a Bayesian logistic regression model with a spike-and-slab prior. The sample size and decision cutoffs of the proposed design are systematically determined based on a novel calibration procedure to achieve desired operating characteristics. Extensive simulation studies show that the proposed design achieves the dual goals of dose optimization and contribution assessment, while yielding substantial sample size savings compared to competing designs.