title: Classifying visual field data.
abstract: 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.
authors.affiliation: Department of Biostatistics, Johns Hopkins School of Hygiene and Public Health, Baltimore, Maryland 21205-2103, USA.
authors.forename: S
authors.initials: S
authors.lastname: Hilton
authors.forename: J
authors.initials: J
authors.lastname: Katz
authors.forename: S
authors.initials: S
authors.lastname: Zeger
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D000704:Analysis of Variance; D015982:Bias; D016022:Case-Control Studies; D005163:Factor Analysis, Statistical; D005901:Glaucoma / Q000145:classification* / Q000175:diagnosis*; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D058609:Visual Field Tests / Q000592:standards*; D014794:Visual Fields