Risk Assessment for Toxicity Experiments with Discrete and Continuous Outcomes: A Bayesian Nonparametric Approach
| Autor: | Athanasios Kottas, Kassandra Fronczyk |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Statistics and Probability Multivariate statistics Computer science Applied Mathematics Nonparametric statistics Inference Mixture model 01 natural sciences Agricultural and Biological Sciences (miscellaneous) Dirichlet process 010104 statistics & probability 03 medical and health sciences 030104 developmental biology Parametric model Statistics 0101 mathematics Statistics Probability and Uncertainty General Agricultural and Biological Sciences Categorical variable General Environmental Science Parametric statistics |
| Zdroj: | Journal of Agricultural, Biological and Environmental Statistics. 22:585-601 |
| ISSN: | 1537-2693 1085-7117 |
| Popis: | We present a Bayesian nonparametric modeling approach to inference and risk assessment for developmental toxicity studies. The primary objective of these studies is to determine the relationship between the level of exposure to a toxic chemical and the probability of a physiological or biochemical response. We consider a general data setting involving clustered categorical responses on the number of prenatal deaths, the number of live pups, and the number of live malformed pups from each laboratory animal, as well as continuous outcomes (e.g., body weight) on each of the live pups. We utilize mixture modeling to provide flexibility in the functional form of both the multivariate response distribution and the various dose–response curves of interest. The nonparametric model is built from a structured mixture kernel and a dose-dependent Dirichlet process prior for the mixing distribution. The modeling framework enables general inference for the implied dose–response relationships and for dose-dependent correlations between the different endpoints, features which provide practical advances relative to traditional parametric models for developmental toxicology. We use data from a toxicity experiment that investigated the toxic effects of an organic solvent (diethylene glycol dimethyl ether) to demonstrate the range of inferences obtained from the nonparametric mixture model, including comparison with a parametric hierarchical model. Supplementary materials accompanying this paper appear on-line. |
| Databáze: | OpenAIRE |
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