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Activity Number:
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287
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #309385 |
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Title:
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A Hierarchical Bayesian Approach in Viral Dynamic System-Based Differential Equation Models with Application to AIDS Studies
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Author(s):
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Yangxin Huang*+ and Hulin Wu
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Companies:
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University of South Florida and University of Rochester
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Address:
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Department of Epidemiology and Biostat, Tampa, FL, 33612,
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Keywords:
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Bayesian mixed-effects models ; long-term HIV dynamics ; longitudinal data ; MCMC ; time-varying drug efficacy
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Abstract:
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HIV dynamic studies have significantly contributed to the understanding of HIV pathogenesis and treatment strategies. However, the models of existing studies are mostly developed to quantify short-term dynamics and may not correctly describe long-term virological response due to the difficulty of establishing a relationship of antiviral response with multiple treatment factors. We develop a mechanism-based nonlinear differential equation models with incorporating PK, drug resistance and adherence for characterizing long-term viral dynamics. A Bayesian nonlinear mixed-effects modeling approach is investigated for estimating dynamic parameters by fitting the model to viral load data from an AIDS trial. Some interesting results are presented. These results suggest that dynamic parameters play an important role in understanding HIV pathogenesis, designing new AIDS treatment strategies.
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