title: A Bayesian approach to the analysis of quantal bioassay studies using nonparametric mixture models.
abstract: We develop a Bayesian nonparametric mixture modeling framework for quantal bioassay settings. The approach is built upon modeling dose-dependent response distributions. We adopt a structured nonparametric prior mixture model, which induces a monotonicity restriction for the dose-response curve. Particular emphasis is placed on the key risk assessment goal of calibration for the dose level that corresponds to a specified response. The proposed methodology yields flexible inference for the dose-response relationship as well as for other inferential objectives, as illustrated with two data sets from the literature.
authors.affiliation: Department of Statistics, Rice University, Houston, Texas, U.S.A.
authors.forename: Kassandra
authors.initials: K
authors.lastname: Fronczyk
authors.forename: Athanasios
authors.initials: A
authors.lastname: Kottas
mesh_terms: D001499:Bayes Theorem; D001681:Biological Assay / Q000379:methods*; D018929:Cell Culture Techniques; D003037:Cobalt Radioisotopes / Q000633:toxicity*; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D006801:Humans; D048629:Micronuclei, Chromosome-Defective; D015233:Models, Statistical; D018570:Risk Assessment / Q000379:methods*; D014344:Trypanocidal Agents / Q000627:therapeutic use; D014345:Trypanosoma / Q000254:growth & development; D014352:Trypanosomiasis / Q000188:drug therapy
chemical_list: D003037:Cobalt Radioisotopes; D014344:Trypanocidal Agents
keywords: Calibration; Cytogenetic dosimetry; Dependent Dirichlet process; Dose-response curve; Markov chain Monte Carlo; Nonparametric mixture models