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.
Acosta, Edward P.
中科院分区:
生物学3区
文献类型:
--
作者:
Huang, Yangxin;Wu, Hulin;Acosta, Edward P.

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艾滋病研究中的 HIV 动态研究对于了解 HIV-1 感染的发病机制以及评估抗逆转录病毒 (ARV) 治疗的有效性非常重要。病毒动力学模型可以通过非线性常微分方程(ODE)系统来制定,但用于推理的统计方法的发展有限。本文受艾滋病临床研究的启发,讨论了动态 ODE 模型的分层贝叶斯非线性混合效应建模方法,无需封闭式解决方案。在该模型中,我们将病毒载量、药物依从性、耐药性、药代动力学、基线协变量和时间依赖性药物疗效完全整合到数据分析中,以表征长期病毒学反应。我们的方法是通过艾滋病临床研究的数据集来实现的。结果表明,在考虑随时间变化的临床因素以及基线特征的情况下对艾滋病毒动态和病毒学反应进行建模可能对于艾滋病毒/艾滋病研究很重要,因为它可以提供定量指导,以更好地了解抗逆转录病毒治疗的病毒学反应,并帮助评估针对现有疗法的临床试验设计。
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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