Utilizing Bayesian predictive power in clinical trial design Journal Articles

abstract

• SUMMARYThe 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.

authors

• Harari, Ofir
• Hsu, Grace
• Dron, Louis
• Park, Jay JH
• Thorlund, Kristian
• Mills, Edward Joseph

status

• published 

publication date

• March 2021

has subject area

• 0104 Statistics (FoR)
• 1115 Pharmacology and Pharmaceutical Sciences (FoR)
• Statistics & Probability (Science Metrix)

published in

• Pharmaceutical Statistics  Journal

keywords

• Bayesian predictive power
• clinical trials
• sample size

Digital Object Identifier (DOI)

• 10.1002/pst.2073

PubMed ID

• 33090634

start page

• 256

end page

• 271

volume

• 20

issue

• 2