A spatially varying change points model for monitoring glaucoma progression using visual field data

Abstract

Glaucoma disease progression, as measured by visual field (VF) data, is often defined by periods of relative stability followed by an abrupt decrease in visual ability at some point in time. Determining the transition point of the disease trajectory to a more severe state is important clinically for disease management and for avoiding irreversible vision loss. Based on this, we present a unified statistical modeling 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 both the mean and variance process of 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 through simulation and real data application. The R package spCP implements the new methodology.

PyVisualFields: A Python Package for Visual Field Analysis

“…As mentioned before, two available open-source R packages ( vfprogression and visualField ) wrapped in our library. There are also more R packages related to VF analysis and simulation, such as binovisualfields 15 , 16 and spCP 17 , 18 and adding their functionalities to our package is a milestone for future developments.…”

A multivariate spatiotemporal change-point model of opioid overdose deaths in Ohio

“…Warren, Pingali and Weinberger (2017) developed a spatially-varying change-point regression model to model the probability that invasive pneumococcal disease is caused by a targeted serotype. Berchuck, Mwanza and Warren (2019) used a similar change-point regression model to study glaucoma progression. Our proposed model extends these to the case of multivariate outcomes.…”