Practical experiences of adopting assurance as a quantitative framework to support decision making in drug development

• Published in: Pharmaceutical statistics : the journal of the pharmaceutical industry Vol. 17; no. 4; pp. 317 - 328
• Main Authors: Crisp, Adam, Miller, Sam, Thompson, Douglas, Best, Nicky
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.07.2018
• Subjects: Animals; Bayesian clinical trial design; Case-Control Studies; Clinical Trials as Topic - methods; Clinical Trials as Topic - statistics & numerical data; Decision Making; Drug development; Drug Development - methods; Drug Development - statistics & numerical data; Drug Industry - methods; Drug Industry - statistics & numerical data; Humans; informative priors; prior elicitation; probability of success; Success; prior elicitation; probability of success; Bayesian clinical trial design; informative priors
• ISSN: 1539-1604; 1539-1612; 1539-1612
• DOI: 10.1002/pst.1856

All clinical trials are designed for success of their primary objectives. Hence, evaluating the probability of success (PoS) should be a key focus at the design stage both to support funding approval from sponsor governance boards and to inform trial design itself. Use of assurance—that is, expected success probability averaged over a prior probability distribution for the treatment effect—to quantify PoS of a planned study has grown across the industry in recent years, and has now become routine within the authors' company. In this paper, we illustrate some of the benefits of systematically adopting assurance as a quantitative framework to support decision making in drug development through several case‐studies where evaluation of assurance has proved impactful in terms of trial design and in supporting governance‐board reviews of project proposals. In addition, we describe specific features of how the assurance framework has been implemented within our company, highlighting the critical role that prior elicitation plays in this process, and illustrating how the overall assurance calculation may be decomposed into a sequence of conditional PoS estimates which can provide greater insight into how and when different development options are able to discharge risk.