Mixed-Effects Models with Skewed Distributions for Time-Varying Decay Rate in HIV Dynamics.
Mixed-Effects Models with Skewed Distributions for Time-Varying Decay Rate in HIV Dynamics.
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DOI：
10.1080/03610918.2013.873129
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Huang Y
中科院分区：
文献类型：
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作者：
Chen R;Huang Y
治疗开始后，HIV病毒载量具有多相变化，说明病毒衰减率是一个时变过程。文献中已经提出了具有不同时变衰减率函数的混合效应模型。然而，有两个尚未解决的关键问题：(I)不清楚哪种模型更适合实际使用，(Ii)模型随机误差通常被假定服从正态分布，这可能是不现实的，可能会掩盖被试内部和之间差异的重要特征。由于HIV病毒载量数据的不对称性即使在转换后仍然很明显，因此使用更普遍的分布族是很重要的，这样可以放松不切实际的正态假设。通过考虑模型的随机误差服从斜椭圆(SE)分布，建立了斜椭圆(SE)贝叶斯混合效应模型。我们比较了具有不同时变衰减率函数的五个SE模型的性能。对于每个模型，我们还对比了不同模型随机误差假设下的性能，如正态分布、学生t分布、偏正态分布或偏t分布。使用两个艾滋病临床试验数据集对所提出的模型和方法进行了说明。结果表明，病毒衰减率随时间变化且具有两个指数分量的模型是首选的。在四种分布假设中，斜t模型和斜正态模型比正态模型或学生t模型对数据的拟合效果更好，这表明假设模型具有偏态分布是重要的，以便在数据表现出偏态时获得合理的结果。
After initiation of treatment, HIV viral load has multiphasic changes, which indicates that the viral decay rate is a time-varying process. Mixed-effects models with different time-varying decay rate functions have been proposed in literature. However, there are two unresolved critical issues: (i) it is not clear which model is more appropriate for practical use, and (ii) the model random errors are commonly assumed to follow a normal distribution, which may be unrealistic and can obscure important features of within- and among-subject variations. Because asymmetry of HIV viral load data is still noticeable even after transformation, it is important to use a more general distribution family that enables the unrealistic normal assumption to be relaxed. We developed skew-elliptical (SE) Bayesian mixed-effects models by considering the model random errors to have an SE distribution. We compared the performance among five SE models that have different time-varying decay rate functions. For each model, we also contrasted the performance under different model random error assumption such as normal, Student-t, skew-normal or skew-t distribution. Two AIDS clinical trial data sets were used to illustrate the proposed models and methods. The results indicate that the model with a time-varying viral decay rate that has two exponential components is preferred. Among the four distribution assumptions, the skew-t and skew-normal models provided better fitting to the data than normal or Student-t model, suggesting that it is important to assume a model with a skewed distribution in order to achieve reasonable results when the data exhibit skewness.