Abstract Number:
3211
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
JIANJUN GAN (1)
Institutions:
(1) GlaxoSmithKline, N/A
First Author:
Presenting Author:
Abstract Text:
Optimizing the design and dosing regimen for Phase 2b dose-ranging studies is crucial to achieve an optimal benefit-risk balance for patients. It is also essential for ensuring a seamless and successful transition to Phase 3, with the identification of the right dose and optimal design. Our proposal involves the integration of a Bayesian-based Quantitative Decision-Making (QDM) framework into Phase 2b design, enabling the incorporation of informative prior information. This shift from p-value-based to probability-based decision-making enhances the efficiency of our decisions. In line with the Commit to Medicine Development (C2MD) milestone, our focus extends to measuring the conditional probability of success in Phase 3 studies. This measurement reflects the design's ability to de-risk later phases based on our current prior belief and pre-defined success criteria. To illustrate this process, we will present results from a simulation involving an HIV asset, demonstrating the effectiveness of our approach.
Keywords:
Bayesian|Decision making|Clinical trial|Adaptive| |
Sponsors:
Biopharmaceutical Section
Tracks:
Adaptive Designs
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