A Bayesian Hierarchical method for fitting multiple health
endpoints in a toxicity study
Taeryon Choi, Mark J. Schervish, Ketra A. Schmitt, Mitchell J.
Small, Annie M. Jarabek and Sandra J. S. Baird
Bayesian hierarchical models are built to fit multiple health
endpoints from a dose-response study of a toxic chemical,
perchlorate. Perchlorate exposure results in iodine uptake inhibition in
the thyroid, with health effects manifested by changes in blood hormone
concentrations and histopathological effects on the thyroid. We propose
linked empirical models to fit blood hormone concentration and thyroid
histopathology data for rats exposed to perchlorate in the 90-day study of
Springborn Laboratory Inc. (1998), based upon the assumed toxicological
relationships between dose and the various endpoints.
All of the models are fit in a Bayesian
framework, and predictions about each endpoint in response to dose
are simulated based on the posterior predictive distribution. A
hierarchical model tries to exploit possible similarities between
different combinations of sex and exposure duration, and it allows us to
produce more stable estimates of dose-response curves. We also
illustrate how the hierarchical model allows us to address additional
questions that arise after the analysis.
Dose-response study; Perchlorate; Hierarchical prior
distribution; Logistic regression; Multivariate regression; MCMC; Optimal