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Title: Latent time joint mixed effect models Authors:  Michael Donohue - University of Southern California (United States) [presenting]
Dan Li - University of Southern California (United States)
Samuel Iddi - University of Southern California (United States)
Wesley Thompson - Institute of Biological Psychiatry (Denmark)
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 data from the Alzheimers disease neuroimaging initiative, and discuss applications to Alzheimer's prognosis and clinical trial simulations.