Adaptively leveraging external data with robust meta-analytical-predictive prior using empirical Bayes |
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| Authors: | Hongtao Zhang Yueqi Shen Judy Li Han Ye Alan Y. Chiang |
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| Affiliation: | 1. Biostatistics and Research Decision Sciences, Merck & Co., Inc., North Wales, Pennsylvania, USA;2. Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA;3. GBDS, Bristol Myers Squibb, San Diego, California, USA;4. College of Business, Lehigh University, Bethlehem, Pennsylvania, USA;5. Biometrics, Lyell Immunopharma, Seattle, Washington, USA |
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| 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. |
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| Keywords: | empirical Bayes meta-analytical-predictive prior prior-data conflict robustness |
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