Addressing prior-data conflict with empirical meta-analytic-predictive priors in clinical studies with historical information

ABSTRACT

A common question in clinical studies is how to use historical data from earlier studies, leveraging relevant information into the design and analysis of a new study. Bayesian approaches are particularly well-suited to this task, with their natural ability to borrow strength across data sources. In this paper, we propose an eMAP approach for incorporating historical data into the analysis of clinical studies, and we discuss an application of this method to the analysis of observational safety studies for a class of products for patients with hemophilia A. The eMAP prior approach is flexible and robust to prior-data conflict. We conducted simulations to compare the frequentist operating characteristics of three approaches under different prior-data conflict assumptions and sample size scenarios.

KEY WORDS:

• Bayesian method
• historical information
• prior-data conflict
• safety study

Funding

This work was supported by the FDA Office of Women’s Health. This project was supported in part by an appointment to the ORISE Research Participation Program at the Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and FDA/CBER. This work used the computational resources of the HPC clusters at the U.S. Food and Drug Administration, Center for Devices and Radiological Health (CDRH).

Disclaimer

This article reflects the views of the authors and should not be construed to represent U.S. Food and Drug Administration’s views or policies.

Additional information

Funding

This work was supported by the FDA Office of Women’s Health. This project was supported in part by an appointment to the ORISE Research Participation Program at the Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and FDA/CBER. This work used the computational resources of the HPC clusters at the U.S. Food and Drug Administration, Center for Devices and Radiological Health (CDRH).