Persistent Link:
http://hdl.handle.net/10150/316773
Title:
Hierarchical Bayesian Benchmark Dose Analysis
Author:
Fang, Qijun
Issue Date:
2014
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
An important objective in statistical risk assessment is estimation of minimum exposure levels, called Benchmark Doses (BMDs) that induce a pre-specified Benchmark Response (BMR) in a target population. Established inferential approaches for BMD analysis typically involve one-sided, frequentist confidence limits, leading in practice to what are called Benchmark Dose Lower Limits (BMDLs). Appeal to hierarchical Bayesian modeling and credible limits for building BMDLs is far less developed, however. Indeed, for the few existing forms of Bayesian BMDs, informative prior information is seldom incorporated. Here, a new method is developed by using reparameterized quantal-response models that explicitly describe the BMD as a target parameter. This potentially improves the BMD/BMDL estimation by combining elicited prior belief with the observed data in the Bayesian hierarchy. Besides this, the large variety of candidate quantal-response models available for applying these methods, however, lead to questions of model adequacy and uncertainty. Facing this issue, the Bayesian estimation technique here is further enhanced by applying Bayesian model averaging to produce point estimates and (lower) credible bounds. Implementation is facilitated via a Monte Carlo-based adaptive Metropolis (AM) algorithm to approximate the posterior distribution. Performance of the method is evaluated via a simulation study. An example from carcinogenicity testing illustrates the calculations.
Type:
text; Electronic Dissertation
Keywords:
Benchmark analysis; Dose-response analysis; Model uncertainty; Multimodel inference; Quantitative risk analysis; Statistics; Bayesian BMDL
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Statistics
Degree Grantor:
University of Arizona
Advisor:
Piegorsch, Walter W.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleHierarchical Bayesian Benchmark Dose Analysisen_US
dc.creatorFang, Qijunen_US
dc.contributor.authorFang, Qijunen_US
dc.date.issued2014-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractAn important objective in statistical risk assessment is estimation of minimum exposure levels, called Benchmark Doses (BMDs) that induce a pre-specified Benchmark Response (BMR) in a target population. Established inferential approaches for BMD analysis typically involve one-sided, frequentist confidence limits, leading in practice to what are called Benchmark Dose Lower Limits (BMDLs). Appeal to hierarchical Bayesian modeling and credible limits for building BMDLs is far less developed, however. Indeed, for the few existing forms of Bayesian BMDs, informative prior information is seldom incorporated. Here, a new method is developed by using reparameterized quantal-response models that explicitly describe the BMD as a target parameter. This potentially improves the BMD/BMDL estimation by combining elicited prior belief with the observed data in the Bayesian hierarchy. Besides this, the large variety of candidate quantal-response models available for applying these methods, however, lead to questions of model adequacy and uncertainty. Facing this issue, the Bayesian estimation technique here is further enhanced by applying Bayesian model averaging to produce point estimates and (lower) credible bounds. Implementation is facilitated via a Monte Carlo-based adaptive Metropolis (AM) algorithm to approximate the posterior distribution. Performance of the method is evaluated via a simulation study. An example from carcinogenicity testing illustrates the calculations.en_US
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectBenchmark analysisen_US
dc.subjectDose-response analysisen_US
dc.subjectModel uncertaintyen_US
dc.subjectMultimodel inferenceen_US
dc.subjectQuantitative risk analysisen_US
dc.subjectStatisticsen_US
dc.subjectBayesian BMDLen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineStatisticsen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorPiegorsch, Walter W.en_US
dc.contributor.committeememberPiegorsch, Walter W.en_US
dc.contributor.committeememberBhattacharya, Rabindra N.en_US
dc.contributor.committeememberBillheimer, D. Deanen_US
dc.contributor.committeememberHu, Chengchengen_US
dc.contributor.committeememberBarnes, Katherine Y.en_US
dc.contributor.committeememberWestveld, Antonen_US
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