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Activity Number:
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235
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #300784 |
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Title:
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Long-Term HIV Dynamic Models Incorporating Drug Adherence and Resistance for Prediction of Virologic Responses
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Author(s):
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Yangxin Huang*+
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Companies:
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University of South Florida
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Address:
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College of Public Health, MDC 56, Tampa, FL, 33612,
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Keywords:
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Adherence ; Bayesian nonlinear mixed-effects models ; time-varying treatment efficacy ; deviance information criterion ; long-term HIV dynamics ; longitudinal data
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Abstract:
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Imperfect adherence and drug resistance (DR) to prescribed antiretroviral (ARV) therapies are important factors explaining the resurgence of virus. A better understanding of the factors responsible for the virologic failure is critical for the development of new treatment strategies. We here develop a mechanism-based reparameterized differential equation models with incorporating adherence (MEMS or questionnaires) and DR for characterizing long-term viral dynamics with ARV therapy. A Bayesian NLME modeling approach is investigated for estimating parameters and comparing effects of different adherence assessments based on an AIDS trial dataset. The results indicate that the drug adherence combined with confounding factor, DR significantly predicts virologic responses (VR). Our study suggests that our models are effective in establishing a relationship of VR with drug adherence and DR.
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