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

Overview of attention for article published in BMC Medical Research Methodology, September 2017
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Title
Predicting the multi-domain progression of Parkinson’s disease: a Bayesian multivariate generalized linear mixed-effect model
Published in
BMC Medical Research Methodology, September 2017
DOI 10.1186/s12874-017-0415-4
Pubmed ID
Authors

Ming Wang, Zheng Li, Eun Young Lee, Mechelle M. Lewis, Lijun Zhang, Nicholas W. Sterling, Daymond Wagner, Paul Eslinger, Guangwei Du, Xuemei Huang

Abstract

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. Past univariate longitudinal or multivariate analyses from cross-sectional trials have limited power to predict individual outcomes or a single moment. The multivariate generalized linear mixed-effect model (GLMM) under the Bayesian framework was proposed to study multi-domain longitudinal outcomes obtained at baseline, 18-, and 36-month. The outcomes included motor, non-motor, and postural instability scores from the MDS-UPDRS, and demographic and standardized clinical data were utilized as covariates. The dynamic prediction was performed for both internal and external subjects using the samples from the posterior distributions of the parameter estimates and random effects, and also the predictive accuracy was evaluated based on the root of mean square error (RMSE), absolute bias (AB) and the area under the receiver operating characteristic (ROC) curve. First, our prediction model identified clinical data that were differentially associated with motor, non-motor, and postural stability scores. Second, the predictive accuracy of our model for the training data was assessed, and improved prediction was gained in particularly for non-motor (RMSE and AB: 2.89 and 2.20) compared to univariate analysis (RMSE and AB: 3.04 and 2.35). Third, the individual-level predictions of longitudinal trajectories for the testing data were performed, with ~80% observed values falling within the 95% credible intervals. Multivariate general mixed models hold promise to predict clinical progression of individual outcomes in PD. The data was obtained from Dr. Xuemei Huang's NIH grant R01 NS060722 , part of NINDS PD Biomarker Program (PDBP). All data was entered within 24 h of collection to the Data Management Repository (DMR), which is publically available ( https://pdbp.ninds.nih.gov/data-management ).

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 30%
Student > Master 6 26%
Student > Bachelor 3 13%
Unspecified 3 13%
Student > Doctoral Student 3 13%
Other 1 4%
Readers by discipline Count As %
Unspecified 5 22%
Medicine and Dentistry 4 17%
Computer Science 4 17%
Neuroscience 3 13%
Nursing and Health Professions 2 9%
Other 5 22%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 September 2017.
All research outputs
#10,493,875
of 11,841,124 outputs
Outputs from BMC Medical Research Methodology
#923
of 1,004 outputs
Outputs of similar age
#228,409
of 270,536 outputs
Outputs of similar age from BMC Medical Research Methodology
#18
of 23 outputs
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