A Spatially Varying Change Points Model for Monitoring Glaucoma Progression Using Visual Field Data

• Activity Number: 649 - Recent Advances in Spatial and Spatial-Temporal Methods
• Type: Topic Contributed
• Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Joshua Warren* and Samuel Berchuck and Jean-Claude Mwanza
• Companies: Yale University and Duke University and UNC Chapel Hill
• Keywords: Bayesian hierarchical models; Boundary detection; Multivariateconditional autoregressive model; Spatially varying change points

Abstract

Glaucoma disease progression is often defined by periods of relative stability followed by an abrupt decrease in visual ability. Determining the transition point to a more severe state is important for avoiding irreversible vision loss. We present a framework that permits prediction of the timing and spatial location of future vision loss and informs clinical decisions regarding disease progression. The developed method incorporates anatomical information to create a biologically plausible data-generating model. We accomplish this by introducing a spatially varying coefficients model that includes spatially varying change points to detect structural shifts in the VF data across both space and time. The VF location-specific change point represents the underlying, and potentially censored, timing of true change in disease trajectory while a multivariate spatial boundary detection structure is introduced that accounts for the complex spatial connectivity of the VF and optic disc. We show that our method improves estimation and prediction of multiple aspects of disease management in comparison to existing methods. The R package spCP implements the new methodology.