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Activity Number:
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30
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Type:
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Contributed
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Date/Time:
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Sunday, August 6, 2006 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #306041 |
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Title:
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Modeling Long-Term HIV Dynamics: a Bayesian Approach
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Author(s):
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Dacheng Liu*+ and Hulin Wu and Yangxin Huang Huang
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Companies:
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Boehringer Ingelheim and University of Rochester and University of South Florida
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Address:
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Biometrics and Data Management, Ridgefield, CT, 06877,
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Keywords:
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antiretroviral drug therapy ; Bayesian mixed-effects models ; drug exposure ; drug resistance ; long term HIV dynamics ; MCMC
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Abstract:
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HIV dynamics studies have significantly contributed to the understanding of HIV infection and antiviral treatment strategies. But most studies are limited to short-term viral dynamics due to the difficulty of establishing a relationship of antiviral response with multiple treatment factors such as drug exposure and drug susceptibility during long-term treatment. We propose a mechanism-based dynamic model for characterizing long-term viral dynamics with antiretroviral therapy. We directly incorporate drug concentration, adherence, and drug susceptibility into a function of treatment efficacy, defined as an inhibition rate of virus replication. We investigate a Bayesian approach under the framework of hierarchical Bayesian (mixed-effects) models for estimating unknown parameters. We run simulation studies and apply the methodology to a data set from an AIDS clinical trial.
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