A model-based approach for simulating adaptive clinical studies with surrogate endpoints used for interim decision-making

In clinical trials, when exploring multiple dose groups to establish efficacy and safety on one or more selected doses, adaptive designs with interim dose selection are often used for dropping less effective dose groups. When it takes a long time to observe primary outcomes, utilizing information on a surrogate endpoint available at an earlier interim may be preferred for selecting which dose to continue. We propose a Bayesian model-based approach where historical data can be leveraged to incorporate a correlation model for investigating the design's operating characteristics. Simulation studies were conducted and the method can be readily applied for power and sample size calculations.