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Activity Number: 515
Type: Invited
Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
Sponsor: WNAR
Abstract #318309
Title: Integrating Independent Spatio-Temporal Models to Assess Population Trends
Author(s): John VanBuren* and Jacob J. Oleson
Companies: University of Iowa and University of Iowa
Keywords: Bayesian ; Hierarchical Models ; Dimension Reduction ; Glaucoma

Our interest in spatio-temporal models of disease spread focuses on how a disease spreads within a body region, and using independent replications across individuals to better understand population level dynamics of disease spread. Our Bayesian hierarchical model incorporates independent spatio-temporal datasets to estimate population level parameters. A dimension reduction propagator matrix is used to identify the most variable spatial regions which are then related to a set of latent variables and covariates. Posterior estimates of parameters allow us to create a smoothed estimate of the overall disease evolution process for each individual. In addition, individual level rates of deterioration can be estimated and predictions of future spread are made. The motivating example for this model stems from a study of visual loss in participants with glaucoma. Participants' vision was recorded across a grid covering the central part of the eye at baseline plus eight follow-up visits every 6 months. We use these spatio-temporal replications of independent participants to determine how human characteristics and demographics collectively affect the spread of glaucoma.

Authors who are presenting talks have a * after their name.

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