Print  Close

Quantitative Decision Making (QDM) in Phase 2b studies

Abstract Number:

3211 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

JIANJUN GAN (1)

Institutions:

(1) GlaxoSmithKline, N/A

First Author:

JIANJUN GAN  
GlaxoSmithKline

Presenting Author:

JIANJUN GAN  
GlaxoSmithKline

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

Can this be considered for alternate subtype?

Yes

Are you interested in volunteering to serve as a session chair?

Yes

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is non-refundable.

I understand