Elastic Meta-Analytic-Predictive Prior for Dynamically Borrowing Information from Historical Data with Application to Biosimilar Clinical Trials

• Activity Number: 318 - Adaptive (and Other) Clinical Trial Designs
• Type: Contributed
• Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Wen Zhang* and Ying Yuan and Zhiying Pan
• Companies: University of Texas Health Science Center at Houston and Department of Biostatistics, University of Texas MD Anderson Cancer Center and Amgen, Inc.
• Keywords: Meta-analytic predictive prior; information borrowing ; type I error; power

Abstract

Focusing on two-arm randomized clinical trials that aim to establish the equivalence between a test biosimilar product and the reference product, we propose the elastic meta-analytic-predictive (EMAP) prior method to leverage rich historical data available on the reference product to improve the power of the biosimilar trials. We first extract the prior information from multiple historical studies through meta-analysis, and then we discount the resulting meta-analytic-predictive (MAP) prior adaptively according to the congruence between the historical reference data and the trial reference arm data. We measure the congruence between the historical reference data and the trial reference arm data using the posterior predictive probability, and achieve dynamic information borrowing by discounting the MAP prior using the elastic function of the congruence measure. Extensive simulation studies show that the EMAP prior outperforms existing methods. The EMAP prior generates comparable or higher power and provides better-controlled type I errors. We illustrate the proposed methodology using two trial examples.