Joshua L. Warren
Assistant Professor of Biostatistics
Yale University
Spatiotemporal Statistical Methods for Monitoring Glaucoma Progression Using Visual Field Data
Abstract
Diagnosing glaucoma progression early is critical for limiting irreversible vision loss. A common method for assessing glaucoma progression relies on a longitudinal series of visual fields (VF) acquired from a patient at regular intervals. VF data are characterized by a complex spatiotemporal correlation structure due to the data generating process and ocular anatomy. Thus, advanced statistical methods are needed to make clinical determinations regarding progression status and for monitoring the disease over time. We introduce a spatiotemporal boundary detection model that allows the underlying anatomy of the optic disc to define the spatial structure of the VF data across time. Based on this model, we define a diagnostic metric and verify that it explains a novel pathway in glaucoma progression. A spatially varying change points model is also developed to facilitate the prediction of VF data and to estimate the timing of future vision loss. Models are applied to data from the Vein Pulsation Study Trial in Glaucoma and the Lions Eye Institute trial registry. Simulations are presented, showing that the proposed methodology is preferred over existing models. This is joint work with Samuel I. Berchuck and Jean-Claude Mwanza.
DATE: Wednesday, November 14, 2018
TIME: 4:00 pm
PLACE: Philip E. Austin Bldg., Rm. 108
Coffee will be served at 3:30 pm in the Noether Lounge (AUST 326)
For more information, contact: Tracy Burke at tracy.burke@uconn.edu