title: A hierarchical Bayesian design for phase I trials of novel combinations of cancer therapeutic agents.
abstract: We propose a hierarchical model for the probability of dose-limiting toxicity (DLT) for combinations of doses of two therapeutic agents. We apply this model to an adaptive Bayesian trial algorithm whose goal is to identify combinations with DLT rates close to a prespecified target rate. We describe methods for generating prior distributions for the parameters in our model from a basic set of information elicited from clinical investigators. We survey the performance of our algorithm in a series of simulations of a hypothetical trial that examines combinations of four doses of two agents. We also compare the performance of our approach to two existing methods and assess the sensitivity of our approach to the chosen prior distribution.
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA. tombraun@umich.edu
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Braun
authors.forename: Shufang
authors.initials: S
authors.lastname: Wang
mesh_terms: D000971:Antineoplastic Combined Chemotherapy Protocols; D001499:Bayes Theorem; D017321:Clinical Trials, Phase I as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D054796:Drug Dosage Calculations; D006801:Humans; D020714:Maximum Tolerated Dose