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Stat Methods Med Res. 2019 Mar;28(3):835-845. doi: 10.1177/0962280217737566. Epub 2017 Nov 23.

Bayesian latent time joint mixed effect models for multicohort longitudinal data.

Author information

1
1 Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, CA, USA.
2
2 Department of Statistics, University of Ghana, Legon-Accra, Ghana.
3
3 Department of Psychiatry, University of California, San Diego, CA, USA.

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.

KEYWORDS:

Hierarchical Bayesian models; joint mixed effects models; latent time shift; multicohort longitudinal data

PMID:
29168432
DOI:
10.1177/0962280217737566

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