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 so-called calibration models which explain cell response to dose. Such models can be used to predict response at unobserved doses. More importantly, 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 natural 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, offering comparison with a parametric model. We examine a dataset modeling blood cultures exposed to radiation where classification is with regard to number of micronuclei per cell.

Key Words: Auxiliary variables; Dirichlet process; dose-response; logistic regression; polytomous response.

The manuscript is available in postscript and pdf formats.