Bayesian Multi-Arm De-Intensification Designs

Abstract

In recent years, new cancer treatments have improved survival in multiple histologies. Some of these therapeutics, and in particular treatment combinations, are often associated with severe treatment-related adverse events (AEs). Therefore, It is important to identify alternative de-intensified therapies, such as dose-reduced therapies with reduced AEs and similar efficacy. We introduce a sequential design for multi-arm de-intensification studies. Based on joint modeling of toxicity and efficacy endpoints, the design evaluates multiple de-intensified therapies at different dose levels, one at a time. We study the utility of the design in oropharynx cancer de-intensification studies. We use a joint Bayesian model for efficacy and toxicity outcomes to define decision rules at interim and final analyses. Interim decisions include early termination of the study due to inferior survival of experimental arms compared to a standard of care (SOC), and the transitions from one de-intensified treatment arm to another with a further reduced dose when there is sufficient evidence of non-inferior survival. We evaluate the operating characteristics of the design using data from recent de-intensification studies in oropharynx cancer.