A Bayesian Predictive Platform Design for Proof of Concept and Dose-Finding Using Early and Late Endpoints

• Activity Number: 138 - Dose Optimization in Drug Development: Where We Are, and Where We Want to Be
• Type: Topic Contributed
• Date/Time: Monday, August 8, 2022 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Ruitao Lin and Li Wang* and Peter Thall and Ying Yuan
• Companies: MD Anderson and Abbvie and The University of Texas MD Anderson Cancer Center and the University of Texas MD Anderson Cancer Center
• Keywords: Bayesian adaptive design; Biomarker; Cubic B-spline; Dose optimization; Long-term response; platform trial

Abstract

Evaluating long-term benefits of potential new treatments for chronic diseases can be very time-consuming and costly. We propose a Bayesian predictive platform design that provides a unified framework for evaluating multiple investigational agents in a multistage, randomized controlled trial. The design expedites the drug evaluation process and reduces development costs by including dose-finding, futility and superiority monitoring, and enrichment, while avoiding over-allocating patients to a shared placebo or active control arm. To facilitate making real-time interim group sequential decisions, unobserved long-term responses are treated as missing values and imputed from longitudinal biomarker measurements. Design parameters as well as the maximum sample size are calibrated to obtain good frequentist properties. The proposed design is illustrated by a trial of three targeted agents for systemic lupus erythematosus, evaluated by their 24-week response rates. Extensive simulations show that the proposed design compares favorably to several conventional platform designs.