title: Integrating independent spatio-temporal replications to assess population trends in disease spread.
abstract: 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.
authors.affiliation: Department of Biostatistics, The University of Iowa, Iowa City, IA, U.S.A.. jvanburen88@gmail.com.
authors.forename: John
authors.initials: J
authors.lastname: VanBuren
authors.affiliation: Department of Biostatistics, The University of Iowa, Iowa City, IA, U.S.A.
authors.forename: Jacob J
authors.initials: JJ
authors.lastname: Oleson
authors.affiliation: Department of Biostatistics, The University of Iowa, Iowa City, IA, U.S.A.
authors.forename: Gideon K D
authors.initials: GK
authors.lastname: Zamba
authors.affiliation: Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, IA, U.S.A.
authors.forename: Michael
authors.initials: M
authors.lastname: Wall
mesh_terms: D001499:Bayes Theorem; D005901:Glaucoma / Q000453:epidemiology*; D006801:Humans; D015233:Models, Statistical
keywords: Bayesian; dimension reduction; glaucoma; hierarchical models