Hierarchical Bayesian inference for HIV dynamic differential equation models incorporating multiple treatment factors.
Hierarchical Bayesian inference for HIV dynamic differential equation models incorporating multiple treatment factors.
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DOI:
10.1002/bimj.200900173
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发表时间:
2010-08
影响因子:
1.7
通讯作者:
Acosta, Edward P.
中科院分区:
文献类型:
--
作者:
Huang, Yangxin;Wu, Hulin;Acosta, Edward P.
关键词:
Studies of 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 paper, 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 evaluation of clinical trial design in response to existing therapies.
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