title: Multiple-objective designs in a dose-response experiment.
abstract: This article is an extension of the work of Huang and Wong (1), who found dual-objective designs for models with a continuous outcome. We consider quantal dose-response experiments with a binary outcome and develop multiple-objective designs for two or more Bayesian optimality criteria. Using the logit model as an illustrative example, we construct numerically optimal designs for estimating model parameters and percentiles, with possibly unequal interest in each of the objectives. We also show that the popular equal dosage assignment rule can be a rather inefficient design for estimating model parameters under a Bayesian setup.
authors.affiliation: Department of Applied Mathematics and Statistics, State University of New York, Stony Brook 11794-3600, USA.
authors.forename: W
authors.initials: W
authors.lastname: Zhu
authors.forename: W K
authors.initials: WK
authors.lastname: Wong
mesh_terms: D001499:Bayes Theorem; D017321:Clinical Trials, Phase I as Topic / Q000379:methods; D017322:Clinical Trials, Phase II as Topic / Q000379:methods; D004305:Dose-Response Relationship, Drug; D006801:Humans; D007928:Lethal Dose 50; D016015:Logistic Models; D008432:Mathematical Computing; D008954:Models, Biological; D012107:Research Design