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Bayesian Theory and Applications$
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Paul Damien, Petros Dellaportas, Nicholas G. Polson, and David A. Stephens

Print publication date: 2013

Print ISBN-13: 9780199695607

Published to Oxford Scholarship Online: May 2013

DOI: 10.1093/acprof:oso/9780199695607.001.0001

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Flexible Bayesian modelling for clustered categorical responses in developmental toxicology

Flexible Bayesian modelling for clustered categorical responses in developmental toxicology

Chapter:
(p.70) 5 Flexible Bayesian modelling for clustered categorical responses in developmental toxicology
Source:
Bayesian Theory and Applications
Author(s):

Kottas Athanasios

Fronczyk Kassandra

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199695607.003.0005

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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