Flexible Bayesian modelling for clustered categorical responses in developmental toxicology
Flexible Bayesian modelling for clustered categorical responses in developmental toxicology
Developmental toxicity studies investigate birth defects caused by toxic chemicals. This chapter develops a Bayesian nonparametric modelling approach for risk assessment in developmental toxicity studies. The model is built from a mixture with a product Binomial kernel, to capture the nested structure of the responses, and a dependent Dirichlet process (DDP) prior for the dose-dependent mixing distributions. The resulting nonparametric DDP mixture model provides rich inference for the response distributions as well as for the dose-response curves. Data from a toxicity experiment involving a plasticizing agent were used to illustrate the scientifically relevant features of the DDP mixture model with regard to estimation of different dose-response relationships for different endpoints, including non-monotonic dose-response curves.
Keywords: developmental toxicity studies, birth defects, toxic chemicals, Bayesian nonparametric mixture model, risk assessment, dependent Dirichlet process prior, mixture modelling, dose-response relationships
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