Bayesian latent time joint mixed effect models for multicohort longitudinal data

Dan Li, Samuel Iddi, Wesley K Thompson, Michael C Donohue
2017 Statistical Methods in Medical Research  
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
more » ... m 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.
doi:10.1177/0962280217737566 pmid:29168432 fatcat:a2thrur7everhdwi5ggedui5q4