title: Optimal Bayesian design for patient selection in a clinical study.
abstract: Bayesian experimental design for a clinical trial involves specifying a utility function that models the purpose of the trial, in this case the selection of patients for a diagnostic test. The best sample of patients is selected by maximizing expected utility. This optimization task poses difficulties due to a high-dimensional discrete design space and, also, to an expected utility formula of high complexity. A simulation-based optimal design method is feasible in this case. In addition, two deterministic algorithms that perform a systematic search over the design space are developed to address the computational issues.
authors.affiliation: Department of Biostatistics, MedImmune, Gaithersburg, Maryland 20878, USA. buzoianum@medimmune.com
authors.forename: Manuela
authors.initials: M
authors.lastname: Buzoianu
authors.forename: Joseph B
authors.initials: JB
authors.lastname: Kadane
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D008954:Models, Biological; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D018579:Patient Selection; D012107:Research Design