Purpose: To evaluate the minimum number of visual field (VF) tests required to precisely predict future VF results using ordinary least squares linear regression (OLSLR), quadratic regression, exponential regression, logistic regression, and M-estimator robust regression model.
Methods: Series of 15 VFs (Humphrey Field Analyzer 24-2 SITA standard) were analyzed from 247 eyes of 155 open-angle glaucoma patients. Future point-wise (PW) VF results and mean VF sensitivities were predicted with varying numbers of VFs in each regression method.
Results: In PW-OLSLR, as expected, the minimum absolute prediction error was obtained using the maximum number of VFs in the regression (14 VFs); mean absolute prediction error was equal to 2.4 ± 0.9 dB. Ten VFs were required to reach the 95% confidence interval (CI) of the minimum absolute prediction error. Prediction errors associated with the exponential and quadratic regression models were significantly larger than those from PW-OLSLR, whereas errors from logistic regression were not significantly smaller than those from PW-OLSLR; however, the absolute prediction error from the M-estimator robust regression model was significantly smaller than those associated with PW-OLSLR (P < 0.01, paired Wilcoxon test). Like PW-OLSLR, 10 VFs were needed to obtain the minimum absolute prediction error of mean VF sensitivity, but there were no significant differences in errors using the different regression methods.
Conclusions: Approximately 10 VFs, are needed to achieve an accurate prediction of PW VF sensitivity and mean sensitivity. Prediction error of PW VF sensitivity can be significantly minimized using the M-estimator robust regression model compared with conventional OLSLR.