Examination of different pointwise linear regression methods for determining visual field progression

Abstract

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.