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Mixed-effects Models with Skewed Distributions forTime-varying Decay Rate in HIV Dynamics

机译:HIV动态中具有时变衰减率的偏态分布的混合效应模型

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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 assumptions such as normal, Student-t, skew-normal, or skew-t distribution. Two AIDS clinical trial datasets 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.
机译:在开始治疗后,HIV病毒载量具有多相变化,这表明病毒衰减率是一个随时间变化的过程。文献中提出了具有不同时变衰减率函数的混合效应模型。但是,存在两个未解决的关键问题:(i)尚不清楚哪种模型更适合实际使用;(ii)通常假定模型的随机误差服从正态分布,这可能是不现实的并且可能使重要信息难以理解主题内和主题间变化的特征。由于即使在转化后,HIV病毒载量数据的不对称性仍然很明显,因此使用更通用的分布族以放松不现实的正常假设非常重要。通过考虑模型随机误差具有SE分布,我们开发了斜椭圆(SE)贝叶斯混合效应模型。我们比较了五个具有不同时变衰减率函数的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(例如正态分布,Student-t,偏态正态或偏态t分布)下的性能。使用两个艾滋病临床试验数据集来说明所提出的模型和方法。结果表明,具有两个指数成分的病毒衰减率随时间变化的模型是首选。在这四个分布假设中,与正常或Student-t模型相比,偏斜t模型和偏斜正态模型对数据的拟合效果更好,这表明为获得合理的结果,假设偏斜分布的模型很重要。数据显示偏斜。

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