Adaptively leveraging external data with robust meta‐analytical‐predictive prior using empirical Bayes

Abstract The robust meta‐analytical‐predictive (rMAP) prior is a popular method to robustly leverage external data. However, a mixture coefficient would need to be pre‐specified based on the anticipated level of prior‐data conflict. This can be very challenging at the study design stage. We propose a novel empirical Bayes robust MAP (EB‐rMAP) prior to address this practical need and adaptively leverage external/historical data. Built on Box's prior predictive p ‐value, the EB‐rMAP prior framework balances between model parsimony and flexibility through a tuning parameter. The proposed framework can be applied to binomial, normal, and time‐to‐event endpoints. Implementation of the EB‐rMAP prior is also computationally efficient. Simulation results demonstrate that the EB‐rMAP prior is robust in the presence of prior‐data conflict while preserving statistical power. The proposed EB‐rMAP prior is then applied to a clinical dataset that comprises 10 oncology clinical trials, including the prospective study.