  We propose a novel empirical Bayes robust MAP (EB-rMAP) prior to 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 binary,
normal, and time-to-event endpoints. Computational aspects of the framework are
efficient. Simulations results with different endpoints 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 of ten oncology clinical trials, including the
perspective study.
