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Activity Number: 208 - Substantial Evidence Using Bayesian Statistics in New Drug Approvals
Type: Invited
Date/Time: Monday, July 31, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #322085 View Presentation
Title: A Global Oncology Platform Trial for Personalized Medicine: GBM AGILE
Author(s): Jason Connor*
Companies: Berry Consultants
Keywords: Bayesian ; adaptive ; platform trial ; oncology
Abstract:

The Glioblastoma Multiforme Adaptive Global Innovative Learning Environment (GBM AGILE) Trial is a multinational academic, industry, and patient advocacy group partnership seeking to both test and confirm the effectiveness of therapies for GBM.

This platform trial includes multiple treatments and seeks to identify not just whether treatments are effective, but also to match patients to the therapies they are most likely to benefit from. As such, different patients will have differing randomization probabilities to the range of available treatments based upon their characteristics such as newly diagnoses vs. recurrent disease, MGMT-methylation status, and potentially other therapy specific biomarkers. Treatments may graduate from the screening phase of the trial by achieving prospectively defined success thresholds in one or more disease 'signatures' and may then potentially move, within the same GBM AGILE infrastructure, to a confirmation stage. The trial design includes many statistical innovations such incorporation of historical and concurrent controls, longitudinal modeling, and early endpoint measures to predict the treatment effect in terms of survival.


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