A Bayesian approach for estimating antiviral efficacy in HIV dynamic models
A Bayesian approach for estimating antiviral efficacy in HIV dynamic models
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DOI：
10.1080/02664760500250552
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发表时间：
2006-03-01
影响因子：
1.5
通讯作者：
Wu, HL
中科院分区：
文献类型：
--
作者：
Huang, YX;Wu, HL
HIV动态研究是近年来艾滋病研究中最重要的进展之一。它使人们对艾滋病毒感染的发病机制有了新的认识。尽管 HIV 动力学的重要发现已发表在著名的科学期刊上，但这些论文中使用的参数估计和模型拟合的统计方法显得令人惊讶地粗糙，并且尚未得到更详细的研究。例如，无法识别的参数只是通过先前研究的平均估计值来估算，并且在建模中没有考虑重要的药理学/临床因素。本文开发了病毒动力学模型来评估药代动力学变化、耐药性和依从性对抗病毒反应的影响。在此模型的背景下，我们研究了非线性混合效应（NLME）模型框架下的贝叶斯建模方法。特别是，我们的建模策略使我们能够通过结合药物暴露和药物敏感性信息来估计治疗方案在整个治疗过程中随时间变化的抗病毒功效。给出了模拟和真实临床数据示例来说明所提出的方法。贝叶斯方法在病毒动力学建模的许多方面具有巨大的应用潜力，因为它使我们能够拟合复杂的动态模型并识别所有模型参数。我们的结果表明，用于估计 HIV 动态模型参数的贝叶斯方法是灵活且强大的。
The study of HIV dynamics is one of the most important developments in recent AIDS research. It has led to a new understanding of the pathogenesis of HIV infection. Although important findings in HIV dynamics have been published in prestigious scientific journals, the statistical methods for parameter estimation and model-fitting used in those papers appear surprisingly crude and have not been studied in more detail. For example, the unidentifiable parameters were simply imputed by mean estimates from previous studies, and important pharmacological/ clinical factors were not considered in the modelling. In this paper, a viral dynamic model is developed to evaluate the effect of pharmacokinetic variation, drug resistance and adherence on antiviral responses. In the context of this model, we investigate a Bayesian modelling approach under a non-linear mixed-effects (NLME) model framework. In particular, our modelling strategy allows us to estimate time-varying antiviral efficacy of a regimen during the whole course of a treatment period by incorporating the information of drug exposure and drug susceptibility. Both simulated and real clinical data examples are given to illustrate the proposed approach. The Bayesian approach has great potential to be used in many aspects of viral dynamics modelling since it allow us to fit complex dynamic models and identify all the model parameters. Our results suggest that Bayesian approach for estimating parameters in HIV dynamic models is flexible and powerful.