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

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.     

Sample size re-estimation in Phase 2 dose-finding: Conditional power versus Bayesian predictive power

Unblinded sample size re-estimation (SSR) is often planned in a clinical trial when there is large uncertainty about the true treatment effect. For Proof-of Concept (PoC) in a Phase II dose finding study, contrast test can be adopted to leverage information from all treatment groups. In this article, we propose two-stage SSR designs using frequentist conditional power (CP) and Bayesian predictive power (PP) for both single and multiple contrast tests. The Bayesian SSR can be implemented under a wide range of prior settings to incorporate different prior knowledge. Taking the adaptivity into account, all type I errors of final analysis in this paper are rigorously protected. Simulation studies are carried out to demonstrate the advantages of unblinded SSR in multi-arm trials.