A Bayesian Nonparametric Modeling Framework for Developmental Toxicity Studies

• 成果类型: Article
• 署名作者: Fronczyk, Kassandra; Kottas, Athanasios
• 署名单位: University of Texas System; UTMD Anderson Cancer Center; University of California System; University of California Santa Cruz
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2013.830445
• 发表日期: 2014
• 页码: 873-888
• 关键词: exchangeable binary data; risk-assessment; dirichlet processes; continuous outcomes; hierarchical Bayes; DENSITY-ESTIMATION; clustered binary; response model; mixtures; priors

摘要

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 cannot 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.