Bayesian latent time joint mixed effect models for multicohort longitudinal data

Abstract

Characterization of long-term disease dynamics, from disease-free to end-stage, is integral to understanding the course of neurodegenerative diseases such as Parkinson's and Alzheimer's; and ultimately, how best to intervene. Natural history studies typically recruit multiple cohorts at different stages of disease and follow them longitudinally for a relatively short period of time. We propose a latent time joint mixed effects model to characterize long-term disease dynamics using this short-term data. Markov chain Monte Carlo methods are proposed for estimation, model selection, and inference. We apply the model to detailed simulation studies and data from the Alzheimer's Disease Neuroimaging Initiative.

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Cited by in corpus (4)

• DIVE: A spatiotemporal progression model of brain pathology in neurodegenerative disorders
• Progression models for repeated measures: Estimating novel treatment effects in progressive diseases
• Describing complex disease progression using joint latent class models for multivariate longitudinal markers and clinical endpoints
• Disease progression model anchored around clinical diagnosis in longitudinal cohorts: example of Alzheimer's disease and related dementia