Our interest in spatio-temporal models focuses on how a disease spreads within a body region. We use 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 predicted 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 and progression of glaucoma. Our introduced model is available in the DROIIDS R package. We account for missing data through our model with a Bayesian imputation method.
| Identifer | oai:union.ndltd.org:uiowa.edu/oai:ir.uiowa.edu:etd-7149 |
| Date | 01 May 2016 |
| Creators | VanBuren, John Matthew |
| Contributors | Oleson, Jacob J. |
| Publisher | University of Iowa |
| Source Sets | University of Iowa |
| Language | English |
| Detected Language | English |
| Type | dissertation |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | Copyright © 2016 John Matthew VanBuren |
Page generated in 0.0333 seconds