Merck Research Lab, Merck & Co, North Wales, Pennsylvania.
Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina.
Stat Med. 2019 Oct 30;38(24):4804-4818. doi: 10.1002/sim.8334. Epub 2019 Aug 6.
This paper is motivated by combining serial neurocognitive assessments and other clinical variables for monitoring the progression of Alzheimer's disease (AD). We propose a novel framework for the use of multiple longitudinal neurocognitive markers to predict the progression of AD. The conventional joint modeling longitudinal and survival data approach is not applicable when there is a large number of longitudinal outcomes. We introduce various approaches based on functional principal component for dimension reduction and feature extraction from multiple longitudinal outcomes. We use these features to extrapolate the health outcome trajectories and use scores on these features as predictors in a Cox proportional hazards model to conduct predictions over time. We propose a personalized dynamic prediction framework that can be updated as new observations collected to reflect the patient's latest prognosis, and thus intervention could be initiated in a timely manner. Simulation studies and application to the Alzheimer's Disease Neuroimaging Initiative dataset demonstrate the robustness of the method for the prediction of future health outcomes and risks of target events under various scenarios.
本文旨在结合一系列神经认知评估和其他临床变量来监测阿尔茨海默病（AD）的进展。我们提出了一种新的框架，用于使用多个纵向神经认知标志物来预测 AD 的进展。当存在大量纵向结果时，传统的联合建模纵向和生存数据方法并不适用。我们引入了基于功能主成分的各种方法，用于从多个纵向结果中进行降维和特征提取。我们使用这些特征来推断健康结果轨迹，并将这些特征上的得分作为 Cox 比例风险模型中的预测因子，以随时间进行预测。我们提出了一种个性化的动态预测框架，可以随着新观察结果的收集进行更新，以反映患者的最新预后，从而可以及时进行干预。模拟研究和对阿尔茨海默病神经影像学倡议数据集的应用表明，该方法在各种情况下预测未来健康结果和目标事件风险的稳健性。
