Joint model for survival and multivariate sparse functional data with application to a study of Alzheimer's Disease.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Li, C; Xiao, L; Luo, S

Published Date

  • June 2022

Published In

Volume / Issue

  • 78 / 2

Start / End Page

  • 435 - 447

PubMed ID

  • 33501651

Pubmed Central ID

  • PMC8310894

Electronic International Standard Serial Number (EISSN)

  • 1541-0420

Digital Object Identifier (DOI)

  • 10.1111/biom.13427


  • eng

Conference Location

  • United States