A Bayesian Adaptive Umbrella Trial Design with Robust Information Borrowing for Screening Multiple Combination Therapies

In immuno-oncology, developing combination therapies to overcome resistance to single agent or induce synergistic effects has become a new focus. To accelerate the screening process to identify promising combinations based on objective response rates, we propose a Bayesian adaptive Umbrella Trial design to simultaneously evaluate combinations of an investigational compound with different backbones, where information borrowing across combinations is allowed to increase trial efficiency. A robust borrowing approach is developed to strike a balance between borrowing and not borrowing by accounting for different configurations of homogeneity of treatment effects using Bayesian model averaging. Unlike existing methods that use the response rates to measure the degree of homogeneity by assuming all arms share a common control rate, an advantage of our approach is that it uses relative treatment effects to determine the degree of homogeneity by adjusting for different control effects across combinations. In the proposed design, Bayesian adaptive interim analyses are implemented to drop futile combinations and graduate early efficacious combinations. Simulation studies demonstrate that the proposed design with robust information borrowing outperforms some existing approaches. It improves power when treatment effects are homogeneous and maintains reasonable arm-wise Type I error rates when heterogeneity is present across combinations.