  Diagnosing glaucoma progression is critical for limiting irreversible vision
loss. A common method for assessing glaucoma progression uses a longitudinal
series of visual fields (VF) acquired at regular intervals. VF data are
characterized by a complex spatiotemporal structure due to the data generating
process and ocular anatomy. Thus, advanced statistical methods are needed to
make clinical determinations regarding progression status. We introduce a
spatiotemporal boundary detection model that allows the underlying anatomy of
the optic disc to dictate the spatial structure of the VF data across time. We
show that our new method provides novel insight into vision loss that improves
diagnosis of glaucoma progression using data from the Vein Pulsation Study
Trial in Glaucoma and the Lions Eye Institute trial registry. Simulations are
presented, showing the proposed methodology is preferred over existing spatial
methods for VF data. Supplementary materials for this article are available
online and the method is implemented in the R package womblR.
