%0 Journal Article
%T Bayesian latent time joint mixed effect models for multicohort longitudinal data
%A Dan Li
%A Michael C Donohue
%A Samuel Iddi
%A Wesley K Thompson
%A null
%J Statistical Methods in Medical Research
%@ 1477-0334
%D 2019
%R 10.1177/0962280217737566
%X 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
%K Hierarchical Bayesian models
%K joint mixed effects models
%K latent time shift
%K multicohort longitudinal data
%U https://journals.sagepub.com/doi/full/10.1177/0962280217737566