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

• 成果类型: Article
• 署名作者: Berchuck, Samuel I.; Mwanza, Jean-Claude; Warren, Joshua L.
• 署名单位: Duke University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; Yale University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2018.1537911
• 发表日期: 2019
• 页码: 1063-1074
• 关键词: disease; MODEL

摘要

Diagnosing glaucoma progression is critical for limiting irreversible vision loss. A common method for assessing glaucoma progression uses a longitudinal series of visual fields (VFs) 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. for this article are available online and the method is implemented in the R package womblR.