title: A nonparametric Bayesian modeling approach for cytogenetic dosimetry.
abstract: In cytogenetic dosimetry, samples of cell cultures are exposed to a range of doses of a given agent. In each sample at each dose level, some measure of cell disability is recorded. The objective is to develop models that explain cell response to dose. Such models can be used to predict response at unobserved doses. More important, such models can provide inference for unknown exposure doses given the observed responses. Typically, cell disability is viewed as a Poisson count, but in the present work, a more appropriate response is a categorical classification. In the literature, modeling in this case is very limited. What exists is purely parametric. We propose a fully Bayesian nonparametric approach to this problem. We offer comparison with a parametric model through a simulation study and the analysis of a real dataset modeling blood cultures exposed to radiation where classification is with regard to number of micronuclei per cell.
authors.affiliation: Institute of Statistics and Decision Sciences, Duke University, Durham, North Carolina 27708-0251, USA. thanos@stat.duke.edu
authors.forename: Athanasios
authors.initials: A
authors.lastname: Kottas
authors.forename: Márcia D
authors.initials: MD
authors.lastname: Branco
authors.forename: Alan E
authors.initials: AE
authors.lastname: Gelfand
mesh_terms: D001499:Bayes Theorem; D001699:Biometry; D001773:Blood Cells / Q000528:radiation effects; D003582:Cytogenetics / Q000706:statistics & numerical data*; D004307:Dose-Response Relationship, Radiation; D006801:Humans; D066298:In Vitro Techniques; D015162:Micronucleus Tests / Q000706:statistics & numerical data; D018709:Statistics, Nonparametric