title: Bayesian adaptive D-optimal design with delayed responses.
abstract: In some disease areas such as diabetes and obesity, patients may need to be studied for several weeks or months for a drug effect to emerge, where longitudinal data are normally collected for each patient. These delayed responses provide challenges to current adaptive design methods. Fu and Manner ( 2010 ) provided an integrated two-component prediction (ITP) model for delayed response adaptive design. In this paper, we extend their ITP model to incorporate a dose-response model in it and propose an ITP Emax model. Furthermore, we propose a utility function for decision making and this utility function is based on D-optimal design theory. We demonstrate our method by a simulation study, and potential sample size reduction is also discussed.
authors.affiliation: Department of Statistics, Virginia Tech, Blacksburg, VA, USA. jieli@vt.edu
authors.forename: Jie
authors.initials: J
authors.lastname: Li
authors.forename: Haoda
authors.initials: H
authors.lastname: Fu
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D017322:Clinical Trials, Phase II as Topic / Q000706:statistics & numerical data; D017326:Clinical Trials, Phase III as Topic / Q000706:statistics & numerical data; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D006801:Humans; D008137:Longitudinal Studies; D008390:Markov Chains; D009010:Monte Carlo Method; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D012107:Research Design / Q000706:statistics & numerical data*