Integrating independent spatio-temporal replications to assess population trends in disease spread

Glaucoma is the second leading cause of blindness in the USA. A visual field test (perimetry) is used to sample and quantitate visual field function in preselected regions in the eye. These regions can be considered a spatial field with replications across independently measured individuals. At retu...

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Bibliographic Details
Published in:Statistics in medicine 2016-12, Vol.35 (28), p.5210-5221
Main Authors: VanBuren, John, Oleson, Jacob J., Zamba, Gideon K. D., Wall, Michael
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian
Bayesian analysis
Datasets
dimension reduction
Disease transmission
Glaucoma
Glaucoma - epidemiology
hierarchical models
Humans
Matrix
Models, Statistical
Population
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Summary:Glaucoma is the second leading cause of blindness in the USA. A visual field test (perimetry) is used to sample and quantitate visual field function in preselected regions in the eye. These regions can be considered a spatial field with replications across independently measured individuals. At return visits, a new set of visual field measurements is obtained producing a subject specific spatio‐temporal dataset. We develop a Bayesian hierarchical modeling framework to analyze these spatio‐temporal datasets both for individual level spread and as aggregate population level trends. Our model extends previous research utilizing a dimension reduction matrix and individual specific latent variables. Human characteristics are incorporated into the model to help explain glaucoma progression. One beneficial product of our model is smoothed estimates for individuals. We also specify how progression rates are computed for monitoring purposes so that clinicians can track changes and predict forward in time. Copyright © 2016 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.7056