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


  • Yangxin Huang,

    Corresponding author
    1. Department of Epidemiology and Biostatistics, College of Public Health, MDC 56, University of South Florida, Tampa, FL 33612, USA
    • Phone: +1-813-974-8209, Fax: +1-813-974-4719
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  • Hulin Wu,

    1. Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, NY 14642, USA
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  • Edward P. Acosta

    1. University of Alabama at Birmingham School of Medicine, Division of Clinical Pharmacology, 1530 3rd Avenue South, VH 116, Birmingham, AL 35294, USA
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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.