Bayesian latent time joint mixed‐effects model of progression in the Alzheimer's Disease Neuroimaging Initiative. Issue 1 (29th August 2018) - Li, Dan; Iddi, Samuel; Thompson, Wesley K.; Rafii, Michael S.; Aisen, Paul S.; Donohue, Michael C.; ,
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.