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Dynamic prediction of Alzheimer's disease progression using features of multiple longitudinal outcomes and time-to-event data.

Publication ,  Journal Article
Li, K; Luo, S
Published in: Stat Med
October 30, 2019

This paper is motivated by combining serial neurocognitive assessments and other clinical variables for monitoring the progression of Alzheimer's disease (AD). We propose a novel framework for the use of multiple longitudinal neurocognitive markers to predict the progression of AD. The conventional joint modeling longitudinal and survival data approach is not applicable when there is a large number of longitudinal outcomes. We introduce various approaches based on functional principal component for dimension reduction and feature extraction from multiple longitudinal outcomes. We use these features to extrapolate the health outcome trajectories and use scores on these features as predictors in a Cox proportional hazards model to conduct predictions over time. We propose a personalized dynamic prediction framework that can be updated as new observations collected to reflect the patient's latest prognosis, and thus intervention could be initiated in a timely manner. Simulation studies and application to the Alzheimer's Disease Neuroimaging Initiative dataset demonstrate the robustness of the method for the prediction of future health outcomes and risks of target events under various scenarios.

Duke Scholars

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Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

October 30, 2019

Volume

38

Issue

24

Start / End Page

4804 / 4818

Location

England

Related Subject Headings

  • Survival Analysis
  • Statistics & Probability
  • Proportional Hazards Models
  • Prognosis
  • Predictive Value of Tests
  • Neuroimaging
  • Longitudinal Studies
  • Humans
  • Disease Progression
  • Biomarkers
 

Citation

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Li, K., & Luo, S. (2019). Dynamic prediction of Alzheimer's disease progression using features of multiple longitudinal outcomes and time-to-event data. Stat Med, 38(24), 4804–4818. https://doi.org/10.1002/sim.8334
Li, Kan, and Sheng Luo. “Dynamic prediction of Alzheimer's disease progression using features of multiple longitudinal outcomes and time-to-event data.Stat Med 38, no. 24 (October 30, 2019): 4804–18. https://doi.org/10.1002/sim.8334.
Li, Kan, and Sheng Luo. “Dynamic prediction of Alzheimer's disease progression using features of multiple longitudinal outcomes and time-to-event data.Stat Med, vol. 38, no. 24, Oct. 2019, pp. 4804–18. Pubmed, doi:10.1002/sim.8334.
Journal cover image

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

October 30, 2019

Volume

38

Issue

24

Start / End Page

4804 / 4818

Location

England

Related Subject Headings

  • Survival Analysis
  • Statistics & Probability
  • Proportional Hazards Models
  • Prognosis
  • Predictive Value of Tests
  • Neuroimaging
  • Longitudinal Studies
  • Humans
  • Disease Progression
  • Biomarkers