title: Adaptive prior variance calibration in the Bayesian continual reassessment method.
abstract: The use of the continual reassessment method (CRM) and other model-based approaches to design Phase I clinical trials has increased owing to the ability of the CRM to identify the maximum tolerated dose better than the 3 + 3 method. However, the CRM can be sensitive to the variance selected for the prior distribution of the model parameter, especially when a small number of patients are enrolled. Although methods have emerged to adaptively select skeletons and to calibrate the prior variance only at the beginning of a trial, there has not been any approach developed to adaptively calibrate the prior variance throughout a trial. We propose three systematic approaches to adaptively calibrate the prior variance during a trial and compare them via simulation with methods proposed to calibrate the variance at the beginning of a trial.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA. zhjin@umich.edu
authors.forename: Jin
authors.initials: J
authors.lastname: Zhang
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Braun
authors.forename: Jeremy M G
authors.initials: JM
authors.lastname: Taylor
mesh_terms: D001499:Bayes Theorem; D017321:Clinical Trials, Phase I as Topic / Q000379:methods*; D003198:Computer Simulation; D020714:Maximum Tolerated Dose; D015233:Models, Statistical