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Objective Calibration of the Bayesian Continual Reassessment Method

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If you have a question about this talk, please contact Mustapha Amrani.

Design and Analysis of Experiments

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 (or group of patients) 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 review the system of model parameters in the CRM , and then describe some semi-automated algorithms to specify these parameters based on existing dose finding theory. Simulation results will be given to illustrate this semi-automated calibration process in the context of some real trial examples.

This talk is part of the Isaac Newton Institute Seminar Series series.

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