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From The Nottingham Trent University, Nottingham, United Kingdom.
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.
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