|
Activity Number:
|
410
- Bayesian Adaptive Designs and Novel Strategies for Dose Optimization in Cellular Therapy Drug Development
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Wednesday, August 10, 2022 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Biopharmaceutical Section
|
|
Abstract #323607
|
|
|
Title:
|
PMED: Optimal Bayesian Design for Platform Trials with Multiple Endpoints
|
|
Author(s):
|
Rachael Liu* and Tian He and Meizi Liu and Jianchang Lin
|
|
Companies:
|
Takeda Pharamaceuticals and Indiana University Purdue University Indianapolis and Takeda pharmaceuticals and Takeda Pharmaceuticals
|
|
Keywords:
|
master protocol;
platform design;
Bayesian borrowing;
optimal dose selection;
benefit risk
|
|
Abstract:
|
In drug development especially in Oncology and Cell Therapy, indication selection and optimal dose identification are the primary objectives and could significantly impact the future success. Master protocol has become popular considering the connection of trial designs with multiple indications and treatment candidates. However, most of the available designs are developed with efficacy endpoint only for estimation and testing, ignoring the safety signal detection which often could put future development at risk. Additionally, it often lacks of quantitative framework to allow optimal treatment selection. We propose a novel optimal Bayesian design for platform trial (PMED) targeting on both safety and efficacy to characterize the benefit risk profile. We further extend the design to allow treatment and indication selection within and across arms with interim looks and dynamic borrowing to increase the efficiency and accuracy of estimation. We propose a hierarchical hypothesis structure to achieve optimal indication and treatment combination selection by controlling family wise error. Simulation studies will be provided to demonstrate the advantage of PMED.
|
Authors who are presenting talks have a * after their name.
