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Alzheimers Dement (Amst). 2018 Aug 29;10:657-668. doi: 10.1016/j.dadm.2018.07.008. eCollection 2018.

Bayesian latent time joint mixed-effects model of progression in the Alzheimer's Disease Neuroimaging Initiative.

Author information

1
Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, CA, USA.
2
Department of Statistics, University of Ghana, Legon-Accra, Ghana.
3
Department of Psychiatry, University of California, San Diego, CA, USA.
4
Department of Neuroscience, University of California, San Diego, CA, USA.

Abstract

Introduction:

We characterize long-term disease dynamics from cognitively healthy to dementia using data from the Alzheimer's Disease Neuroimaging Initiative.

Methods:

We apply a latent time joint mixed-effects model to 16 cognitive, functional, biomarker, and imaging outcomes in Alzheimer's Disease Neuroimaging Initiative. Markov chain Monte Carlo methods are used for estimation and inference.

Results:

We find good concordance between latent time and diagnosis. Change in amyloid positron emission tomography shows a moderate correlation with change in cerebrospinal fluid tau (ρ = 0.310) and phosphorylated tau (ρ = 0.294) and weaker correlation with amyloid-β 42 (ρ = 0.176). In comparison to amyloid positron emission tomography, change in volumetric magnetic resonance imaging summaries is more strongly correlated with cognitive measures (e.g., ρ = 0.731 for ventricles and Alzheimer's Disease Assessment Scale). The average disease trends are consistent with the amyloid cascade hypothesis.

Discussion:

The latent time joint mixed-effects model can (1) uncover long-term disease trends; (2) estimate the sequence of pathological abnormalities; and (3) provide subject-specific prognostic estimates of the time until onset of symptoms.

KEYWORDS:

Alzheimer's disease; Hierarchical Bayesian models; Joint mixed-effects models; Latent disease time; Multicohort longitudinal data; Multiple outcomes

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