title: Nonparametric Bayesian methods for benchmark dose estimation.
abstract: The article proposes and investigates the performance of two Bayesian nonparametric estimation procedures in the context of benchmark dose estimation in toxicological animal experiments. The methodology is illustrated using several existing animal dose-response data sets and is compared with traditional parametric methods available in standard benchmark dose estimation software (BMDS), as well as with a published model-averaging approach and a frequentist nonparametric approach. These comparisons together with simulation studies suggest that the nonparametric methods provide a lot of flexibility in terms of model fit and can be a very useful tool in benchmark dose estimation studies, especially when standard parametric models fail to fit to the data adequately.
authors: Department of Mathematics and Statistics, University of Maryland Baltimore County, Baltimore, MD, USA.
authors: Nilabja
authors: N
authors: Guha
authors: Anindya
authors: A
authors: Roy
authors: Leonid
authors: L
authors: Kopylev
authors: John
authors: J
authors: Fox
authors: Maria
authors: M
authors: Spassova
authors: Paul
authors: P
authors: White
mesh_terms: D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001681:Biological Assay / Q000379:methods; D003198:Computer Simulation; D004249:DNA Damage; D004305:Dose-Response Relationship, Drug; D015233:Models, Statistical; D009010:Monte Carlo Method; D011336:Probability; D018570:Risk Assessment / Q000379:methods*; D012984:Software; D013223:Statistics as Topic; D018675:Toxicity Tests
keywords: BMDL; BMDS software; dirichlet distribution; integrated Brownian motion