Utilizing Bayesian predictive power in clinical trial design. (8th October 2020)

• Record Type: Journal Article
• Authors: Harari, Ofir; Hsu, Grace; Dron, Louis; Park, Jay J. H.; Thorlund, Kristian; Mills, Edward J.
• Is Part Of: Pharmaceutical statistics. Volume 20:Number 2(2021)
• Page Start: 256
• Page End: 271
• Publication Date: 2020-10-08
• Subjects: Bayesian predictive power -- clinical trials -- sample size; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.2073
• Languages: English
• ISSNs: 1539-1604

Abstract

SUMMARY: The Bayesian paradigm provides an ideal platform to update uncertainties and carry them over into the future in the presence of data. Bayesian predictive power (BPP) reflects our belief in the eventual success of a clinical trial to meet its goals. In this paper we derive mathematical expressions for the most common types of outcomes, to make the BPP accessible to practitioners, facilitate fast computations in adaptive trial design simulations that use interim futility monitoring, and propose an organized BPP‐based phase II‐to‐phase III design framework.