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Biom J. 2010 Aug;52(4):470-86. doi: 10.1002/bimj.200900173.

Hierarchical Bayesian inference for HIV dynamic differential equation models incorporating multiple treatment factors.

Author information

  • 1Department of Epidemiology and Biostatistics, College of Public Health, MDC 56, University of South Florida, Tampa, FL 33612, USA. yhuang@health.usf.edu

Abstract

Studies on HIV dynamics in AIDS research are very important in understanding the pathogenesis of HIV-1 infection and also in assessing the effectiveness of antiretroviral (ARV) treatment. Viral dynamic models can be formulated through a system of nonlinear ordinary differential equations (ODE), but there has been only limited development of statistical methodologies for inference. This article, motivated by an AIDS clinical study, discusses a hierarchical Bayesian nonlinear mixed-effects modeling approach to dynamic ODE models without a closed-form solution. In this model, we fully integrate viral load, medication adherence, drug resistance, pharmacokinetics, baseline covariates and time-dependent drug efficacy into the data analysis for characterizing long-term virologic responses. Our method is implemented by a data set from an AIDS clinical study. The results suggest that modeling HIV dynamics and virologic responses with consideration of time-varying clinical factors as well as baseline characteristics may be important for HIV/AIDS studies in providing quantitative guidance to better understand the virologic responses to ARV treatment and to help the evaluation of clinical trial design in response to existing therapies.

PMID:
20661953
[PubMed - indexed for MEDLINE]
PMCID:
PMC3507994
Free PMC Article
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