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Activity Number: 228
Type: Topic Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304488
Title: Use of Bayesian Methods to Assess Probability of Achieving Critical Success Factors (CSFs) in Clinical Trials
Author(s): Cory Heilmann*+
Companies: Eli Lilly and Company
Address: 7011 Bluffridge Way, Indianapolis, IN, 46278, United States
Keywords: Bayesian ; Probability of Study Success ; Critical Success Factor ; Clincal Trial ; Prior Information

Early in the drug development process, the amount of data available is limited, but decisions must be made about advancement of candidate compounds and the design of future studies. In making such decisions, an important consideration is the likelihood of study success, defined by a set of criteria called the CSFs. With sparse data in the early stages of drug development and high stakes, Bayesian analyses can be useful. Frequently, prior information is available from compounds in the same class or based on the method of action. Bayesian methods can use the prior information and the small amount of early phase data to give the probability of a parameter, defined within the CSF, lying within a desired range (such as the probability of treatment A having a greater effect than treatment B by x unit). Future studies can be simulated under these assumptions to give the unconditional probability of these studies showing superiority or non-inferiority of endpoints. We describe a case study where Bayesian methodology was used to perform such an assessment and highlight some education and implementation issues to using Bayesian methods for CSF assessment in clinical trials.

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