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Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease.

Publication ,  Journal Article
Zou, H; Li, K; Zeng, D; Luo, S; Alzheimer's Disease Neuroimaging Initiative,
Published in: Stat Med
December 30, 2021

Alzheimer's disease (AD) is a severe neurodegenerative disorder impairing multiple domains, for example, cognition and behavior. Assessing the risk of AD progression and initiating timely interventions at early stages are critical to improve the quality of life for AD patients. Due to the heterogeneous nature and complex mechanisms of AD, one single longitudinal outcome is insufficient to assess AD severity and disease progression. Therefore, AD studies collect multiple longitudinal outcomes, including cognitive and behavioral measurements, as well as structural brain images such as magnetic resonance imaging (MRI). How to utilize the multivariate longitudinal outcomes and MRI data to make efficient statistical inference and prediction is an open question. In this article, we propose a multivariate joint model with functional data (MJM-FD) framework that relates multiple correlated longitudinal outcomes to a survival outcome, and use the scalar-on-function regression method to include voxel-based whole-brain MRI data as functional predictors in both longitudinal and survival models. We adopt a Bayesian paradigm to make statistical inference and develop a dynamic prediction framework to predict an individual's future longitudinal outcomes and risk of a survival event. We validate the MJM-FD framework through extensive simulation studies and apply it to the motivating Alzheimer's Disease Neuroimaging Initiative (ADNI) study.

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Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

December 30, 2021

Volume

40

Issue

30

Start / End Page

6855 / 6872

Location

England

Related Subject Headings

  • Statistics & Probability
  • Quality of Life
  • Neuroimaging
  • Magnetic Resonance Imaging
  • Humans
  • Disease Progression
  • Cognitive Dysfunction
  • Brain
  • Bayes Theorem
  • Alzheimer Disease
 

Citation

APA
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Zou, H., Li, K., Zeng, D., Luo, S., & Alzheimer’s Disease Neuroimaging Initiative, . (2021). Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease. Stat Med, 40(30), 6855–6872. https://doi.org/10.1002/sim.9214
Zou, Haotian, Kan Li, Donglin Zeng, Sheng Luo, and Sheng Alzheimer’s Disease Neuroimaging Initiative. “Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease.Stat Med 40, no. 30 (December 30, 2021): 6855–72. https://doi.org/10.1002/sim.9214.
Zou H, Li K, Zeng D, Luo S, Alzheimer’s Disease Neuroimaging Initiative. Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease. Stat Med. 2021 Dec 30;40(30):6855–72.
Zou, Haotian, et al. “Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease.Stat Med, vol. 40, no. 30, Dec. 2021, pp. 6855–72. Pubmed, doi:10.1002/sim.9214.
Zou H, Li K, Zeng D, Luo S, Alzheimer’s Disease Neuroimaging Initiative. Bayesian inference and dynamic prediction of multivariate joint model with functional data: An application to Alzheimer's disease. Stat Med. 2021 Dec 30;40(30):6855–6872.
Journal cover image

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

December 30, 2021

Volume

40

Issue

30

Start / End Page

6855 / 6872

Location

England

Related Subject Headings

  • Statistics & Probability
  • Quality of Life
  • Neuroimaging
  • Magnetic Resonance Imaging
  • Humans
  • Disease Progression
  • Cognitive Dysfunction
  • Brain
  • Bayes Theorem
  • Alzheimer Disease