An Empirical Bayes Robust Meta-Analytical-Predictive Prior to Adaptively Leverage External Data
Methodology
2021-12-09 v2
Abstract
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.
Cite
@article{arxiv.2109.10237,
title = {An Empirical Bayes Robust Meta-Analytical-Predictive Prior to Adaptively Leverage External Data},
author = {Hongtao Zhang and Yueqi Shen and Alan Y Chiang and Judy Li},
journal= {arXiv preprint arXiv:2109.10237},
year = {2021}
}