title: Bayesian fitting of a logistic dose-response curve with numerically derived priors.
abstract: In this report we describe the Bayesian analysis of a logistic dose-response curve in a Phase I study, and we present two simple and intuitive numerical approaches to construction of prior probability distributions for the model parameters. We combine these priors with the expert prior opinion and compare the results of the analyses with those obtained from the use of alternative prior formulations.
authors.affiliation: Biostatistics Group, F. Hoffman La-Roche, Welwyn, UK. les.huson@roche.com
authors.forename: L W
authors.initials: LW
authors.lastname: Huson
authors.forename: N
authors.initials: N
authors.lastname: Kinnersley
mesh_terms: D001499:Bayes Theorem; D001699:Biometry / Q000379:methods; D017321:Clinical Trials, Phase I as Topic / Q000706:statistics & numerical data*; D004305:Dose-Response Relationship, Drug; D006801:Humans; D016015:Logistic Models; D011336:Probability; D013678:Technology, Pharmaceutical / Q000379:methods