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 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. Data used in preparation of this article were obtained from the ADNI database.

中文翻译：

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用于阿尔茨海默病研究的生存和多变量稀疏功能数据联合模型

阿尔茨海默病 (AD) 的研究通常会收集多个纵向临床结果，这些结果与 AD 进展相关并可以预测。研究结果与 AD 发病时间之间的关联具有重大的科学意义。我们将多个纵向结果建模为多元稀疏功能数据，并提出了一种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，我们提出了一个多变量功能混合模型来识别结果的共享进展模式和结果特定的进展模式，这使得结果和 AD 发病之间的关联模型更具可解释性。所提出的方法应用于阿尔茨海默氏症 s 疾病神经影像学倡议研究 (ADNI) 和功能性联合模型揭示了五种纵向结果的推断及其与 AD 发病的关联。仿真研究也证实了所提出模型的有效性。本文所用数据来自 ADNI 数据库。