Flexible Bayesian modelling for clustered categorical responses in developmental toxicology
Athanasios Kottas, Fronczyk Kassandra · Oxford University Press eBooks · 2013
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.