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Activity Number:
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75
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Type:
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Contributed
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Date/Time:
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Sunday, August 6, 2006 : 4:00 PM to 5:50 PM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #306212 |
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Title:
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The Analysis of Mixed-Effects Compartmental Systems Using Bayesian and non-Bayesian Methods
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Author(s):
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Yi Wang*+ and Kent M. Eskridge and Shunpu Zhang
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Companies:
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University of Nebraska-Lincoln and University of Nebraska-Lincoln and University of Nebraska-Lincoln
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Address:
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623 S. 18th Street, Apt. 24, Lincoln, NE, 68508,
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Keywords:
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compartmental analysis ; mixed-effects modeling ; ordinary differential equations ; Bayesian and non-Bayesian methods ; minimal model
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Abstract:
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Compartmental analysis is used to model dynamic biological systems and widely applied to the kinetics of drugs in the body. We use mixed-effects modeling, which quantifies between- and within-subject variability, and pharmacokinetic models with solutions from systems of ordinary differential equations (ODEs) to analyze population data. Non-Bayesian software (nlme and nlmeODE in R or NLINMIX in SAS) and Bayesian software (WinBUGS and WBDiff) enable the mixed-effect analysis of complicated systems of ODEs with and without a closed-form solution. The aim is to use several examples (particularly glucose-insulin minimal model) to illustrate the applicability of Bayesian and non-Bayesian methods for compartmental analysis of population data. Our results indicate that the two methods are numerically stable and provide accurate parameter estimates for standard population data used in this paper.
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