Bayesian latent time joint mixed effect models for multicohort longitudinal data. (March 2019)

• Record Type: Journal Article
• Authors: Li, Dan; Iddi, Samuel; Thompson, Wesley K; Donohue, Michael C
• Is Part Of: Statistical methods in medical research. Volume 28:Number 3(2019)
• Page Start: 835
• Page End: 845
• Publication Date: 2019-03
• Subjects: Hierarchical Bayesian models -- joint mixed effects models -- latent time shift -- multicohort longitudinal data; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280217737566
• Languages: English
• ISSNs: 0962-2802

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.