Boscardin W J, Taylor J M, Law N
Department of Biostatistics, UCLA 90095-1772, USA.
Stat Methods Med Res. 1998 Mar;7(1):13-27. doi: 10.1177/096228029800700103.
Over the past decade, researchers have put a great amount of effort into developing suitable models for the analysis of longitudinal CD4 data and other markers of AIDS progression. These models must be general enough to allow for different patterns of change in the marker data. In this paper, we review the existing literature including our preferred models which involve mixed effects, stochastic terms and independent measurement error. Adding stochastic terms to standard mixed effects models gives an interpretable and parsimonious method for generalizing the covariance structure of the measurement error and short-term variability. We focus on univariate and bivariate models with integrated Ornstein-Uhlenbeck (IOU) stochastic terms. The IOU process allows for a range of biologically plausible derivative tracking that encompasses both random trajectory and Brownian motion behaviour. We illustrate these modelling techniques on longitudinal CD4 and viral RNA data.
在过去十年中，研究人员投入了大量精力来开发适用于分析纵向CD4数据和艾滋病进展其他标志物的模型。这些模型必须足够通用，以允许标志物数据出现不同的变化模式。在本文中，我们回顾了现有文献，包括我们首选的涉及混合效应、随机项和独立测量误差的模型。在标准混合效应模型中添加随机项，为概括测量误差的协方差结构和短期变异性提供了一种可解释且简洁的方法。我们专注于具有积分奥恩斯坦 - 乌伦贝克（IOU）随机项的单变量和双变量模型。IOU过程允许一系列生物学上合理的导数跟踪，包括随机轨迹和布朗运动行为。我们用纵向CD4和病毒RNA数据说明了这些建模技术。
