A Phase I Bayesian Adaptive Design to Simultaneously Optimize Dose and Schedule Among and Within Patients

• Activity Number: 404
• Type: Contributed
• Date/Time: Tuesday, July 31, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Jin Zhang*+ and Thomas M Braun
• Companies: University of Michigan and University of Michigan
• Keywords: Adaptive design ; Bayesian statistics ; Cure model ; Dose-finding study ; Phase I trial

Abstract

In traditional schedule or dose-schedule finding designs, patients are assumed to receive their assigned dose-schedule combination throughout the trial even though the combination may later be found to be too toxic or inefficacious, which contradicts actual clinical practice. There has not been any systematic approach to optimize the intra-patient dose-schedule assignment. To meet this unfilled need, we propose a novel Phase I clinical trial design that extends the traditional design that only optimizes dose and schedule among patients by adaptively varying the intra-patient dose-schedule assignment as the study proceeds. Our design is based on a Bayesian non-mixture cure rate model that incorporates multiple administrations each patient receives with the per-administration dose included as a covariate. Simulations demonstrate that our design identifies safe dose and schedule combinations as well as the traditional method that does not allow for intra-patient dose-schedule reassignments, but with a larger number of patients assigned to those combinations.