Bayesian latent time joint mixed effect models for multicohort longitudinal data

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dc.contributor.authorLi, D.
dc.contributor.authorIddi, S.
dc.contributor.authorThompson, W.K.
dc.contributor.authorDonohue, M.C.
dc.contributor.authorfor the Alzheimer's Disease Neuroimaging Initiative
dc.date.accessioned2019-05-31T14:44:12Z
dc.date.available2019-05-31T14:44:12Z
dc.date.issued2017-11
dc.description.abstractCharacterization 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.en_US
dc.identifier.otherhttps://doi.org/10.1177/0962280217737566
dc.identifier.otherVolume: 28 issue: 3, page(s): 835-845
dc.identifier.urihttp://ugspace.ug.edu.gh/handle/123456789/30454
dc.language.isoenen_US
dc.publisherStatistical Methods in Medical Researchen_US
dc.subjectHierarchical Bayesian modelsen_US
dc.subjectJoint mixed effects modelsen_US
dc.subjectLatent time shiften_US
dc.subjectMulticohort longitudinal dataen_US
dc.titleBayesian latent time joint mixed effect models for multicohort longitudinal dataen_US
dc.typeArticleen_US

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