Table 2.

Results of the multivariate logistic regression using the best subset of variables selected with elastic net regression. The elastic net approach allows selection of a parsimonious set of predictor variables that do not demonstrate significant collinearity.

Predictor	Coefficient
Combined GCIPL, cp-RNFL and clinical data
Rates of cp-RNFL change, superior temporal sector	−0.016
Rates of GCIPL change at 3.4° eccentricity	−0.030
Rates of GCIPL change at 5.6° eccentricity	−0.066
Rates of GCIPL change, superpixel 5.3	−0.040
Rates of GCIPL change, superpixel 5.6	−0.024
Rates of GCIPL change, superpixel 4.3	−0.008
Rates of GCIPL change, superpixel 4.2	−0.016
Rates of GCIPL change, superpixel 2.5	−0.010
Only GCIPL and clinical data
Rates of GCIPL change at 3.4° eccentricity	−0.008
Rates of GCIPL change at 5.6° eccentricity	−0.058
Rates of GCIPL change, superpixel 5.3	−0.050
Rates of GCIPL change, superpixel 5.6	−0.030
Rates of GCIPL change, superpixel 4.3	−0.007
Rates of GCIPL change, superpixel 4.2	−0.019
Rates of GCIPL change, superpixel 2.5	−0.008

SE = standard error; cp-RNFL = circumpapillary retinal nerve fiber layer; GCIPL = ganglion cell/inner plexiform layer.