Dynamic predictions in Bayesian functional joint models for longitudinal and time-to-event data: An application to Alzheimer's disease

• Published in: Statistical methods in medical research Vol. 28; no. 2; p. 327
• Main Authors: Li, Kan, Luo, Sheng
• Format: Journal Article
• Language: English
• Published: England 01.02.2019
• Subjects: Alzheimer Disease - diagnostic imaging; Alzheimer Disease - physiopathology; Bayes Theorem; Disease Progression; Humans; Longitudinal Studies; Markov Chains; Models, Statistical; Monte Carlo Method; Neuroimaging; penalized B-spline; functional data analysis; Markov Chain Monte Carlo; Alzheimer’s Disease Neuroimaging Initiative study; personalized prediction

In the study of Alzheimer's disease, researchers often collect repeated measurements of clinical variables, event history, and functional data. If the health measurements deteriorate rapidly, patients may reach a level of cognitive impairment and are diagnosed as having dementia. An accurate pr...