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Activity Number: 128 - SPEED: Biometrics and Biostatistics Part 1
Type: Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #304433 Presentation 1 Presentation 2
Title: Quantitative Decision Making (QDM) in Phase I/II Studies
Author(s): Kevin Gan* and Jonathan Haddad
Companies: GlaxoSmithKline and GlaxoSmithKline
Keywords: Bayesian; adaptive; Quantitative; clinical trial

The cost of discovering and developing new medicines has increased dramatically in recent years. Bayesian approaches have the potential to reduce development costs and increase the trial success rate. These approaches are well suited to flexible development strategies tailored to meet an asset’s unique challenges. Specific questions can be addressed using much more focused studies prior to large investments into full development programs. Bayesian approaches provide a process for balancing risk mitigation and cost vs. speed of development. Specifically, Bayesian-based adaptive designs can be used in the conduct of Phase I/II clinical trials to (i) effectively incorporate into the design current knowledge on the efficacy and safety of candidate medicines; (ii) predict possible outcomes from candidate clinical trial designs and select the design yielding the highest probability of reaching a decision; and (iii) stop the trial early when accumulating data are sufficient to reach a decision. The basis for QDM (Quantitative decision making) must be established as part of the clinical trials design discussions. Examples based on HIV agents will be presented.

Authors who are presenting talks have a * after their name.

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