Bayesian Hierarchical Analysis for Multiple Health Endpoints in a Toxicity Study
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Taeryon | - |
dc.contributor.author | Schervish, Mark J. | - |
dc.contributor.author | Schmitt, Ketra A. | - |
dc.contributor.author | Small, Mitchell J. | - |
dc.date.accessioned | 2021-09-08T00:22:02Z | - |
dc.date.available | 2021-09-08T00:22:02Z | - |
dc.date.created | 2021-06-14 | - |
dc.date.issued | 2010-09 | - |
dc.identifier.issn | 1085-7117 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/115720 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.title | Bayesian Hierarchical Analysis for Multiple Health Endpoints in a Toxicity Study | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Taeryon | - |
dc.identifier.doi | 10.1007/s13253-010-0019-5 | - |
dc.identifier.scopusid | 2-s2.0-84867180276 | - |
dc.identifier.wosid | 000284912600002 | - |
dc.identifier.bibliographicCitation | JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, v.15, no.3, pp.290 - 307 | - |
dc.relation.isPartOf | JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS | - |
dc.citation.title | JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS | - |
dc.citation.volume | 15 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 290 | - |
dc.citation.endPage | 307 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Life Sciences & Biomedicine - Other Topics | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Biology | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordAuthor | Dose-response study | - |
dc.subject.keywordAuthor | Hierarchical prior distribution | - |
dc.subject.keywordAuthor | Logistic regression | - |
dc.subject.keywordAuthor | MCMC | - |
dc.subject.keywordAuthor | Multivariate regression | - |
dc.subject.keywordAuthor | Optimal design point | - |
dc.subject.keywordAuthor | Perchlorate | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.