Objective Calibration of the Bayesian Continual Reassessment Method

• Activity Number: 147
• Type: Invited
• Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
• Sponsor: ENAR
• Author(s): Ken Cheung*+ and Shing M. Lee
• Companies: Columbia University and Columbia University
• Keywords: Dose finding ; sample size ; design calibration

Abstract

The continual reassessment method (CRM) is a Bayesian model-based design for percentile estimation in sequential dose finding trials. The main idea of the CRM is to treat the next incoming patient at a recent posterior update of the target percentile. This approach is intuitive and ethically appealing on a conceptual level. However, the performance of the CRM can be sensitive to how the CRM model is specified. In addition, since the specified model directly affect the generation of the design points in the trial, sensitivity analysis may not be feasible after the data are collected.

As there are infinitely many ways to specify a CRM model, the process of model calibration, typically done by trial and error in practice, can be complicated and time-consuming. In my talk, I will first introduce a theoretical framework that characterizes the design components in the Bayesian CRM, and then describe some semi-automated algorithms that allow us to specify a CRM design in a timely and reproducible manner. Simulation results will be given to illustrate this calibration process in the context of some real trial examples.