Segmental modeling of viral load changes for HIV longitudinal data with skewness and detection limits.
Segmental modeling of viral load changes for HIV longitudinal data with skewness and detection limits.
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具有偏度和检测限的 HIV 纵向数据病毒载量变化的分段建模。
DOI：
10.1002/sim.5527
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发表时间：
2013
影响因子：
2
通讯作者：
Huang,Yangxin
中科院分区：
文献类型：
--
作者：
Huang,Yangxin
虽然在文献中使用非线性混合效应或非参数混合效应模型分析复杂的HIV纵向数据是一种常见的做法，但以下问题可能会突出。(i)在临床实践中，每个受试者的病毒反应可能遵循“断棒”式轨迹，表明反应的下降和增加有多个阶段。这种多阶段（变化点）可能是帮助量化治疗效果和改善患者护理管理的重要指标。由于模型公式结构复杂，非线性混合效应或非参数混合效应模型难以估计变化点。（ii）通常假设模型随机误差的分布是正态分布，但这种假设可能不切实际地掩盖了主题变化的重要特征。（iii）响应观察（病毒载量）可能由于检测的限制而受到左审查。当具有不对称（偏斜）特征和左删减的数据与变化点作为模型中的未知参数一起观察时，推断过程可能会非常复杂。同时涉及所有这些特征的工作相对较少。本文提出了在贝叶斯框架下响应过程（左删节）的带有偏态分布的分段混合效应模型。最后用一个实际的数据实例说明了所提出的方法。版权所有©2012 John Wiley & Sons， Ltd。
Although it is a common practice to analyze complex HIV longitudinal data using nonlinear mixed‐effects or nonparametric mixed‐effects models in literature, the following issues may standout. (i) In clinical practice, the profile of each subject's viral response may follow a ‘broken‐stick’‐like trajectory, indicating multiple phases of decline and increase in response. Such multiple phases (change points) may be an important indicator to help quantify treatment effect and improve management of patient care. To estimate change points, nonlinear mixed‐effects or nonparametric mixed‐effects models become a challenge because of complicated structures of model formulations. (ii) The commonly assumed distribution for model random errors is normal, but this assumption may unrealistically obscure important features of subject variations. (iii) The response observations (viral load) may be subject to left censoring due to a limit of detection. Inferential procedures can be complicated dramatically when data with asymmetric (skewed) characteristics and left censoring are observed in conjunction with change points as unknown parameters into models. There is relatively little work concerning all these features simultaneously. This article proposes segmental mixed‐effects models with skew distributions for the response process (with left censoring) under a Bayesian framework. A real data example is used to illustrate the proposed methods. Copyright © 2012 John Wiley & Sons, Ltd.