Bayesian Nonparametric Risk Assessment in Developmental Toxicity Studies with Ordinal Responses

TL;DR

This paper introduces a Bayesian nonparametric framework for analyzing clustered ordinal responses in developmental toxicity studies, improving uncertainty quantification over traditional parametric methods.

Contribution

It develops dose-dependent stick-breaking mixture models with overdispersed kernels for better modeling heterogeneity and uncertainty in toxicity dose-response data.

Findings

Enhanced uncertainty quantification demonstrated in simulations

Effective modeling of heterogeneity in toxicity responses

Application to ethylene glycol toxicity data shows practical utility

Abstract

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.