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Glaucoma disease progression, as measured by visual field (VF) data, is oftendefined by periods of relative stability followed by an abrupt decrease invisual ability at some point in time. Determining the transition point of thedisease trajectory to a more severe state is important clinically for diseasemanagement and for avoiding irreversible vision loss. Based on this, we presenta unified statistical modeling framework that permits prediction of the timingand spatial location of future vision loss and informs clinical decisionsregarding disease progression. The developed method incorporates anatomicalinformation to create a biologically plausible data-generating model. Weaccomplish this by introducing a spatially varying coefficients model thatincludes spatially varying change points to detect structural shifts in boththe mean and variance process of VF data across both space and time. The VFlocation-specific change point represents the underlying, and potentiallycensored, timing of true change in disease trajectory while a multivariatespatial boundary detection structure is introduced that accounts for thecomplex spatial connectivity of the VF and optic disc. We show that our methodimproves estimation and prediction of multiple aspects of disease management incomparison to existing methods through simulation and real data application.The R package spCP implements the new methodology.

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