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Diagnosing glaucoma progression is critical for limiting irreversible visionloss. A common method for assessing glaucoma progression uses a longitudinalseries of visual fields (VF) acquired at regular intervals. VF data arecharacterized by a complex spatiotemporal structure due to the data generatingprocess and ocular anatomy. Thus, advanced statistical methods are needed tomake clinical determinations regarding progression status. We introduce aspatiotemporal boundary detection model that allows the underlying anatomy ofthe optic disc to dictate the spatial structure of the VF data across time. Weshow that our new method provides novel insight into vision loss that improvesdiagnosis of glaucoma progression using data from the Vein Pulsation StudyTrial in Glaucoma and the Lions Eye Institute trial registry. Simulations arepresented, showing the proposed methodology is preferred over existing spatialmethods for VF data. Supplementary materials for this article are availableonline and the method is implemented in the R package womblR.

Diagnosing Glaucoma Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method