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Modeling times of maximum biomarker excretion

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Text
TitleInfo (ID = T-1)
Title
Modeling times of maximum biomarker excretion
SubTitle
PartName
PartNumber
NonSort
Identifier (displayLabel = ); (invalid = )
ETD_2443
Identifier (type = hdl)
http://hdl.rutgers.edu/1782.2/rucore10003400001.ETD.000052346
Language (objectPart = )
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eng
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theses
Subject (ID = SBJ-1); (authority = RUETD)
Topic
Public Health
Subject (ID = SBJ-2); (authority = ETD-LCSH)
Topic
Biochemical markers--Evaluation
Subject (ID = SBJ-3); (authority = ETD-LCSH)
Topic
Automobiles--Motors (Diesel)--Exhaust gas--Health aspects
Subject (ID = SBJ-4); (authority = ETD-LCSH)
Topic
Diesel motor exhaust gas--Health aspects
Abstract
Analysis of biomarkers to detect levels of chemical exposure in humans is an important risk evaluation tool. For example, urine biomarkers such as 1-aminopyrene can be used to assess exposure levels to diesel exhaust (DE), an important public health concern. Toxic chemicals contained in DE and DE particles have demonstrated genotoxic and carcinogenic properties in experimental animals. A recent experiment evaluating the urine concentration of DE biomarkers was the impetus for this dissertation. One goal of the experiment, and the focus of this dissertation, was to characterize the excretion time course of the biomarker 1-aminopyrene. The times of maximum concentration in plasma or maximum excretion in urine have been typically summarized using non-parametric or asymptotic techniques based on individual subject-level values; however, there is limited information addressing confidence interval generation when sparse subject-level samples requiring population-modeling approaches are present. Therefore, there was a need to generate and evaluate an appropriate confidence interval approach when sparse sampling is present.
Pharmacokinetic (PK) modeling was used to fit a standard one-compartment urine excretion model to the data for estimation of the time of maximum excretion. Several variations of the PK model were explored and a model based on cumulative excretion rates was selected. Several statistical techniques for modeling PK data and calculating confidence intervals for the time of maximum excretion were compared including confidence intervals based on the first and second order delta methods, derived for this dissertation.
A comparison of confidence interval methods showed that when using: (1) within-subject Tmax values, coverages obtained using the non-parametric method were highest and often provide coverages close to the nominal 95% level; and (2) population-average Tmax values, confidence intervals generated using the first-order delta method provided the highest coverages, at approximately 93% when numerical approximation estimation methods were used. Subject response profiles for the 1-aminopyrene biomarker data were varied and led to a hypothesis that a mixture of more than one distribution of profiles may be present. Future exploration with data collected for more than 24-hours would be needed to further explore this hypothesis fully.
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electronic resource
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ix, 176 p. : ill.
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Dr.P.H.
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Includes bibliographical references (p. 169-173)
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by Susan Huyck
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Huyck
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Susan
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1959-
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Susan Huyck
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Ohman-Strickland
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Pamela
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chair
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Advisory Committee
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Pamela Ohman-Strickland
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Zhang
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Junfeng
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Advisory Committee
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Junfeng Zhang
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Liu
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Junfeng
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Advisory Committee
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Junfeng Liu
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Lin
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Yong
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Yong Lin
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Xu
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Xu
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Xu Steven Xu
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Rutgers University
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degree grantor
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Bloustein Grad. School of Planning and Pub. Policy
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school
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2010
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2010-01
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xx
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Rutgers University Electronic Theses and Dissertations
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ETD
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Bloustein Grad. School of Planning and Pub. Policy ETD Collection
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rucore10003400001
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NjNbRU
Identifier (type = doi)
doi:10.7282/T3HH6K6X
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work.
Copyright
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Copyright protected
Notice
Note
Availability
Status
Open
Reason
Permission or license
Note
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Huyck
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Susan
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2010-01-21 11:21:52
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Susan Huyck
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Rutgers University. Bloustein Grad. School of Planning and Pub. Policy
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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