Bayesian Nonparametric Risk Assessment in Developmental Toxicity Studies with Ordinal Responses

Athanasios Kottas Co-Author
University of California-Santa Cruz
 
Jizhou Kang Speaker
 
Monday, Aug 4: 10:35 AM - 10:55 AM
Topic-Contributed Paper Session 
Music City Center 
We develop a nonparametric Bayesian modeling framework for clustered ordinal responses in developmental toxicity studies, which typically exhibit extensive heterogeneity. The primary focus of these studies is to examine the dose-response relationship, which is depicted by the (conditional) probability of an endpoint across the dose (toxin) levels. Standard parametric approaches, limited in terms of the response distribution and/or the dose-response relationship, hinder reliable uncertainty quantification in this context. We propose nonparametric mixture models that are built from dose-dependent stick-breaking process priors, leveraging the continuation-ratio logits representation of the multinomial distribution to formulate the mixture kernel. We further elaborate the modeling approach, amplifying the mixture models with an overdispersed kernel which offers enhanced control of variability. We conduct a simulation study to demonstrate the benefits of both the discrete nonparametric mixing structure and the overdispersed kernel in delivering coherent uncertainty quantification. Further illustration is provided with different forms of risk assessment, using data from a toxicity experiment on the effects of ethylene glycol.

Keywords

Bayesian nonparametrics

Developmental toxicology

Dose-response relationship

Logit stick-breaking prior

Overdispersion