Department of Biostatistics, Yale University, New Haven, Connecticut, USA.
Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA.
Biometrics. 2022 Jun;78(2):435-447. doi: 10.1111/biom.13427. Epub 2021 Feb 5.
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.
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 进展相关且具有预测性。研究这些结果与 AD 发病时间之间的关联具有重要的科学意义。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。具体来说，我们提出了一种多元功能混合模型，以识别结果的共享进展模式和特定结果的进展模式，从而能够更具解释性地对结果与 AD 发病之间的关联进行建模。该方法应用于阿尔茨海默病神经影像学倡议研究（ADNI），功能联合模型为五个纵向结果及其与 AD 发病之间的关联提供了新的推断。模拟研究也证实了所提出模型的有效性。本文准备数据来自 ADNI 数据库。
