PURPOSE: To compare the specificity and sensitivity of several different methods for using pointwise linear regression (PLR) to detect progression (deterioration) in visual fields. METHODS: First, theoretical results were derived to predict which of the considered PLR methods would be the most specific and hence the least sensitive. Then, a "Virtual Eye" simulation model was developed that simulates series of sensitivity readings for a point over time. The model adds normally distributed noise (estimated from published results) to the sensitivity at each point to produce a series of fields to be analyzed using each method. Stable and deteriorating eyes were simulated, with the latter defined to have a noise-free loss of 2 dB/y at a significant cluster of points over the series. RESULTS: The most sensitive method tested was to flag a visual field as progressing if it had a point that exhibited a statistically significant slope (at the 1% level) of at least -1 dB/y in the sensitivity. The most specific was a new "Three-Omitting" method that is being proposed, using two confirmation fields in a novel way. Current methods of using confirmation fields to verify a significant slope incorrectly flagged up to twice as many stable eyes as having progressing fields as did our new method. CONCLUSIONS: Using the new proposed PLR method is recommended in preference to current PLR methods in any applications when a high degree of specificity is the main priority.
PURPOSE: To compare the specificity and sensitivity of several different methods for using pointwise linear regression (PLR) to detect progression (deterioration) in visual fields. METHODS: First, theoretical results were derived to predict which of the considered PLR methods would be the most specific and hence the least sensitive. Then, a "Virtual Eye" simulation model was developed that simulates series of sensitivity readings for a point over time. The model adds normally distributed noise (estimated from published results) to the sensitivity at each point to produce a series of fields to be analyzed using each method. Stable and deteriorating eyes were simulated, with the latter defined to have a noise-free loss of 2 dB/y at a significant cluster of points over the series. RESULTS: The most sensitive method tested was to flag a visual field as progressing if it had a point that exhibited a statistically significant slope (at the 1% level) of at least -1 dB/y in the sensitivity. The most specific was a new "Three-Omitting" method that is being proposed, using two confirmation fields in a novel way. Current methods of using confirmation fields to verify a significant slope incorrectly flagged up to twice as many stable eyes as having progressing fields as did our new method. CONCLUSIONS: Using the new proposed PLR method is recommended in preference to current PLR methods in any applications when a high degree of specificity is the main priority.
Authors: Shaban Demirel; Carlos Gustavo V De Moraes; Stuart K Gardiner; Jeffrey M Liebmann; George A Cioffi; Robert Ritch; Mae O Gordon; Michael A Kass Journal: Invest Ophthalmol Vis Sci Date: 2012-01-25 Impact factor: 4.799
Authors: Carlos Gustavo De Moraes; Shaban Demirel; Stuart K Gardiner; Jeffrey M Liebmann; George A Cioffi; Robert Ritch; Mae O Gordon; Michael A Kass Journal: Invest Ophthalmol Vis Sci Date: 2012-04-02 Impact factor: 4.799
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Authors: Felipe A Medeiros; Robert N Weinreb; Grant Moore; Jeffrey M Liebmann; Christopher A Girkin; Linda M Zangwill Journal: Ophthalmology Date: 2012-01-21 Impact factor: 12.079
Authors: Pooyan Kazemian; Mariel S Lavieri; Mark P Van Oyen; Chris Andrews; Joshua D Stein Journal: Ophthalmology Date: 2017-12-02 Impact factor: 12.079