Rizopoulos Dimitris
Department of Biostatistics, Erasmus Medical Center, PO Box 2040, 3000 CA Rotterdam, the Netherlands.
Biometrics. 2011 Sep;67(3):819-29. doi: 10.1111/j.1541-0420.2010.01546.x. Epub 2011 Feb 9.
In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest. This type of research question has given rise to a rapidly developing field of biostatistics research that deals with the joint modeling of longitudinal and time-to-event data. In this article, we consider this modeling framework and focus particularly on the assessment of the predictive ability of the longitudinal marker for the time-to-event outcome. In particular, we start by presenting how survival probabilities can be estimated for future subjects based on their available longitudinal measurements and a fitted joint model. Following we derive accuracy measures under the joint modeling framework and assess how well the marker is capable of discriminating between subjects who experience the event within a medically meaningful time frame from subjects who do not. We illustrate our proposals on a real data set on human immunodeficiency virus infected patients for which we are interested in predicting the time-to-death using their longitudinal CD4 cell count measurements.
在纵向研究中，常常会关注一个随时间重复测量的标志物与感兴趣事件发生时间之间的关联。这类研究问题催生了生物统计学研究中一个迅速发展的领域，该领域致力于纵向数据和事件发生时间数据的联合建模。在本文中，我们考虑这种建模框架，并特别关注纵向标志物对事件发生时间结局的预测能力评估。具体而言，我们首先展示如何基于未来受试者可用的纵向测量数据和拟合的联合模型来估计其生存概率。接下来，我们在联合建模框架下推导准确性度量，并评估该标志物在医学上有意义的时间框架内区分经历事件的受试者和未经历事件的受试者的能力有多强。我们在一个关于人类免疫缺陷病毒感染患者的真实数据集上阐述我们的提议，对于该数据集，我们感兴趣的是利用患者纵向的CD4细胞计数测量值来预测死亡时间。
