A Bayesian Nonparametric Modeling Framework for Developmental Toxicity Studies

• Activity Number: 567
• Type: Topic Contributed
• Date/Time: Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Kassandra Fronczyk*+ and Athanasios Kottas
• Companies: University of California at Santa Cruz and University of California at Santa Cruz
• Keywords: Dependent Dirichlet process ; Developmental toxicology data ; Dirichlet process mixture models ; Gaussian process ; Markov chain Monte Carlo ; Risk assessment.

Abstract

We develop a Bayesian nonparametric mixture modeling framework for replicated count responses in dose-response settings. We explore this methodology for modeling and risk assessment in developmental toxicity studies, where the primary objective is to determine the relationship between the level of exposure to a toxic chemical and the probability of a physiological or biochemical response, or death. Data from these experiments typically involve features that can not be captured by standard parametric approaches. To provide flexibility in the functional form of both the response distribution and the probability of positive response, the proposed mixture model is built from a dependent Dirichlet process prior, with the dependence of the mixing distributions governed by the dose level. The methodology is tested with a simulation study, which involves also comparison with semiparametric Bayesian approaches to highlight the practical utility of the dependent Dirichlet process nonparametric mixture model. Further illustration is provided through the analysis of data from two developmental toxicity studies.