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CLASSIFYING VISUAL FIELD DATA
Author(s) -
HILTON STERLING,
KATZ JOANNE,
ZEGER SCOTT
Publication year - 1996
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19960715)15:13<1349::aid-sim270>3.0.co;2-b
Subject(s) - visual field , glaucoma , logistic regression , variance (accounting) , field (mathematics) , statistics , visual field loss , regression , correlation , regression analysis , optometry , computer science , artificial intelligence , mathematics , pattern recognition (psychology) , medicine , ophthalmology , geometry , accounting , pure mathematics , business
We develop a prediction model for classifying an eye according to glaucoma status based on its visual field. We develop measures of both diffuse and localized defects in the visual field as potential predictors of glaucoma. To identify predictors of abnormal fields, we must describe the variability in the fields of normal eyes, hence we first model the mean, variance and correlation structures of normal fields with use of generalized estimating equations. The best measures of diffuse loss include a field's mean level, contrasts of the upper and lower halves, and contrasts of the nasal and temporal halves. Local loss is measured by the depth, area, volume and location of the field's largest defect. We develop logistic regression models to classify eyes as having glaucoma or not. We present ROC curves of the results that are highly competitive with current clinical methods of classification.