论文标题
生存和多元稀疏功能数据的联合模型，并应用于阿尔茨海默氏病的研究
Joint Model for Survival and Multivariate Sparse Functional Data with Application to a Study of Alzheimer's Disease
论文作者
论文摘要
对阿尔茨海默氏病（AD）的研究通常会收集多个纵向临床结果，这些结局与AD进展相关且可预测。研究结果与广告发作时间之间的关联是很大的科学兴趣。我们将多个纵向结果建模为多元稀疏功能数据，并提出了将多元功能数据与事件时间数据联系起来的功能联合模型。特别是，我们提出了一个多元功能混合模型（MFMM），以识别结果的共享进程模式和特定于结果的进程模式，从而使结果与AD发作之间的关联更加可解释。所提出的方法应用于阿尔茨海默氏病神经影像学倡议研究（ADNI），功能联合模型阐明了五种纵向结果及其与AD发作的关联的新启示。仿真研究还证实了所提出的模型的有效性。
Studies of Alzheimer's disease (AD) often collect multiple longitudinal clinical outcomes, which are correlated and predictive of AD progression. It is of great scientific interest to investigate the association between the outcomes and time to AD onset. We model the multiple longitudinal outcomes as multivariate sparse functional data and propose a functional joint model linking multivariate functional data to event time data. In particular, we propose a multivariate functional mixed model (MFMM) to identify the shared progression pattern and outcome-specific progression patterns of the outcomes, which enables more interpretable modeling of associations between outcomes and AD onset. The proposed method is applied to the Alzheimer's Disease Neuroimaging Initiative study (ADNI) and the functional joint model sheds new light on inference of five longitudinal outcomes and their associations with AD onset. Simulation studies also confirm the validity of the proposed model.
