Predicting the multi-domain progression of Parkinson’s disease: a Bayesian multivariate generalized linear mixed-effect model

Predicting the multi-domain progression of Parkinson’s disease: a Bayesian multivariate generalized linear mixed-effect model

Abstract Background It is challenging for current statistical models to predict clinical progression of Parkinson’s disease (PD) because of the involvement of multi-domains and longitudinal data. Methods Past univariate longitudinal or multivariate analyses from cross-sectional trials have limited p...

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Bibliographic Details
Main Authors: Ming Wang, Zheng Li, Eun Young Lee, Mechelle M. Lewis, Lijun Zhang, Nicholas W. Sterling, Daymond Wagner, Paul Eslinger, Guangwei Du, Xuemei Huang
Format: Article
Language:English
Published: BMC 2017-09-01
Series:BMC Medical Research Methodology
Subjects:
Parkinson’s disease
Multivariate longitudinal data
Generalized linear mixed-effect model
Dynamic prediction
Motor symptoms
Non-motor symptoms
Online Access:http://link.springer.com/article/10.1186/s12874-017-0415-4

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http://link.springer.com/article/10.1186/s12874-017-0415-4

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