A Bayesian Platform Trial Design to Simultaneously Evaluate Multiple Drugs in Multiple Indications with Mixed Endpoints

• Activity Number: 318 - Adaptive (and Other) Clinical Trial Designs
• Type: Contributed
• Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Yujie Zhao* and Rui (Sammi) Tang and Yeting Du and Ying Yuan
• Companies: Department of Biostatistics, University of Texas MD Anderson Cancer Center and Servier Pharmaceuticals and Servier Pharmaceuticals and Department of Biostatistics, University of Texas MD Anderson Cancer Center
• Keywords: Master protocol; Platform design; Multiple indication combination therapy; Bayesian hierarchical model; Phase II trials

Abstract

In the era of targeted therapies and immunotherapies, the traditional drug development paradigm of testing one drug at a time in one indication has become increasingly inefficient. Motivated by a real-world application, we propose a master-protocol-based Bayesian platform trial design to simultaneously evaluate multiple drugs in multiple indications with mixed endpoints (PDME), where different subsets of efficacy measures (e.g., objective response and landmark progression-free survival) may be used by different indications as primary or co-primary endpoints. We propose a Bayesian hierarchical model to accommodate mixed endpoints and reflect the trial structure that indications are nested within treatments. We develop a two-stage approach that first clusters the indications into homogeneous subgroups and then applies the Bayesian hierarchical model to each subgroup to achieve precision information borrowing. Patients are enrolled in a group-sequential way and adaptively assigned to treatments according to their efficacy estimates. Simulations show that the PDME design has desirable operating characteristics, compared to existing method.