Figure 2 shows the absolute prediction errors associated with OLSLR, M-robust, MM-robust, Skipped, Deepest, and Lasso regression. The prediction error for MM-robust regression with VF
1–3 could not be calculated because a leverage point could not be calculated. Absolute prediction errors became smaller as the number of VF tests included in the regression increased. There was no significant improvement in error by applying M-robust, MM-robust, Skipped, and Deepest, compared to using the OLSLR at any time point (
P > 0.05, repeated ANOVA with Benjamini's correction for multiple testing
39), except for M-robust with VF
1–8 (
P = 0.028, repeated ANOVA with Benjamini's correction for multiple testing
39). The absolute prediction errors with the Lasso model were significantly better than OLSLR when VF
1–3 to VF
1–8 were used for prediction (
P <0.0001, <0.0001, <0.0001, <0.0001, <0.0001, 0.0017, 0.016, repeated ANOVA with Benjamini's correction for multiple testing
39). Among the 513 eyes, 234 eyes showed progression faster than −0.25 dB/y. Interestingly, as shown in
Figure 3, prediction accuracy tended to be large in eyes with mTD progression rate < −0.25 dB/y. A significant improvement was observed when applying Lasso, compared to OLSLR, when the initial one or two VFs were used to predict (
P = 0.007, 0.035, repeated ANOVA with Benjamini's correction for multiple testing
39).