Model selection and mixed-effects modeling of HIV infection dynamics
Model selection and mixed-effects modeling of HIV infection dynamics
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DOI：
10.1007/s11538-006-9084-x
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发表时间：
2006-11-01
影响因子：
3.5
通讯作者：
Nelson, P. W.
中科院分区：
文献类型：
--
作者：
Bortz, D. M.;Nelson, P. W.
我们提出了一个模型选择方法和应用程序的数学模型在体内HIV感染动力学的介绍。我们考虑六个以前发表的确定性模型，并比较他们的能力，以代表HIV感染患者接受逆转录酶单药治疗。在创建统计模型时，采用分层混合效应建模方法来表征患者人群中的个体间和个体内变异性。我们估计人口参数的最大似然函数的制定，然后用于计算基于信息论的模型选择标准，提供了各种模型的能力，以代表患者数据的排名。此外，这些模型生成的参数拟合为Louie等人[AIDS 17：1151-1156，2003]中较高的病毒清除率c提供了统计学支持。在候选模型中，我们的结果表明哪些数学结构，例如，线性与非线性，最好地描述了我们正在建模的数据，并说明了其他人在建模传染病时要考虑的框架。
We present an introduction to a model selection methodology and an application to mathematical models of in vivo HIV infection dynamics. We consider six previously published deterministic models and compare them with respect to their ability to represent HIV-infected patients undergoing reverse transcriptase mono-therapy. In the creation of the statistical model, a hierarchical mixed-effects modeling approach is employed to characterize the inter- and intra-individual variability in the patient population. We estimate the population parameters in a maximum likelihood function formulation, which is then used to calculate information theory based model selection criteria, providing a ranking of the abilities of the various models to represent patient data. The parameter fits generated by these models, furthermore, provide statistical support for the higher viral clearance rate c in Louie et al. [AIDS 17:1151-1156, 2003]. Among the candidate models, our results suggest which mathematical structures, e.g., linear versus nonlinear, best describe the data we are modeling and illustrate a framework for others to consider when modeling infectious diseases.