Joint penalized spline modeling of multivariate longitudinal data, with application to HIV-1 RNA load levels and CD4 cell counts.
Joint penalized spline modeling of multivariate longitudinal data, with application to HIV-1 RNA load levels and CD4 cell counts.
复制标题
多变量纵向数据的联合惩罚样条模型，应用于 HIV-1 RNA 负载水平和 CD4 细胞计数。
作者：
Zhao,Lihui;Chen,Tom;Novitsky,Vladimir;Wang,Rui
由于需要对原发感染阶段的 HIV 病毒载量水平和 CD4 计数的纵向轨迹进行联合建模，我们提出了一种联合惩罚样条建模方法，可用于同时对不同类型（例如连续、二元）的多个生物标志物的重复测量进行建模。这种方法允许每个标记的灵活轨迹，考虑标记之间潜在的随时间变化的相关性，并且对于结的错误指定具有鲁棒性。尽管有其优点，但多元惩罚样条模型的应用，特别是当生物标志物可能具有不同的数据类型时，由于其实现上看似复杂，在一定程度上受到了限制。为了克服这个问题，我们描述了一种将多变量设置转换为单变量设置的过程，然后利用惩罚样条模型的广义线性混合效应模型表示来促进其通过标准统计软件的实现。我们进行了模拟研究，通过与单变量建模方法相比纵向测量的相关生物标志物的联合建模来评估有效性和效率。我们将这种建模方法应用于来自南部非洲队列的纵向 HIV-1 RNA 载量和 CD4 计数数据，以估计联合分布的特征，例如随着时间的推移，相关性以及具有高病毒载量水平和高 CD4 细胞计数的受试者的比例。
Motivated by the need to jointly model the longitudinal trajectories of HIV viral load levels and CD4 counts during the primary infection stage, we propose a joint penalized spline modeling approach that can be used to model the repeated measurements from multiple biomarkers of various types (eg, continuous, binary) simultaneously. This approach allows for flexible trajectories for each marker, accounts for potentially time-varying correlation between markers, and is robust to misspecification of knots. Despite its advantages, the application of multivariate penalized spline models, especially when biomarkers may be of different data types, has been limited in part due to its seemingly complexity in implementation. To overcome this, we describe a procedure that transforms the multivariate setting to the univariate one, and then makes use of the generalized linear mixed effect model representation of a penalized spline model to facilitate its implementation with standard statistical software. We performed simulation studies to evaluate the validity and efficiency through joint modeling of correlated biomarkers measured longitudinally compared to the univariate modeling approach. We applied this modeling approach to longitudinal HIV-1 RNA load and CD4 count data from Southern African cohorts to estimate features of the joint distributions such as the correlation and the proportion of subjects with high viral load levels and high CD4 cell counts over time.