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Bayesian Hierarchical Analysis for Multiple Health Endpoints in a Toxicity Study

Authors
Choi, TaeryonSchervish, Mark J.Schmitt, Ketra A.Small, Mitchell J.
Issue Date
Sep-2010
Publisher
SPRINGER
Keywords
Dose-response study; Hierarchical prior distribution; Logistic regression; MCMC; Multivariate regression; Optimal design point; Perchlorate
Citation
JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, v.15, no.3, pp.290 - 307
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS
Volume
15
Number
3
Start Page
290
End Page
307
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/115720
DOI
10.1007/s13253-010-0019-5
ISSN
1085-7117
Abstract
Bayesian hierarchical models are built to fit multiple health endpoints from a dose-response study of a chemical contaminant, 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 empirical models to fit blood hormone concentration and thyroid histopathology data for rats exposed to perchlorate in the 90-day study of Springborn Laboratories Inc. (1998), based upon a mechanistic model derived from 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 Bayesian model specification allows us to address additional questions that arise after the analysis.
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College of Political Science & Economics (Department of Statistics)
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