An Algorithm to Evaluate Probability of Success for Decisionmaking in Early Drug Development

• Activity Number: 135
• Type: Contributed
• Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Computing
• Author(s): Annie Wang*+ and Narinder Nangia
• Companies: Abbott Laboratories and Abbott Laboratories
• Keywords: Bayesian ; Probability of Success ; R2WinBUGS

Abstract

In the new drug development paradigm, learning stage studies (a proof of concept or a dose-ranging study) are expected to provide clear evidence of the drug candidate meeting desired target product profile as decisions to continue or halt development of a compound must be made at the end of these studies. Relying solely on p-values for testing hypothesis of no treatment effect at the end of the study is an inefficient approach as these studies are generally powered with little or no information on the unknown treatment effect. This presentation illustrates an approach that exploits totality of accumulated data/knowledge in a Bayesian framework. Implementation of this approach using R2WinBUGS will be discussed for evaluation of probability of success for a drug candidate in meeting the target product profile.