Purpose: We have shown previously that a nonlinear exponential model fits longitudinal series of mean deviation (MD) better than a linear model. This study extends that work to investigate the mode (linear versus nonlinear) of change for pointwise sensitivities. Methods: Data from 475 eyes of 244 clinically managed participants were analyzed. Sensitivity estimates at each test location were fitted using two-level linear and nonlinear mixed effects models. Sensitivity on the last test date was forecast using a model fit from the earlier test dates in the series. The means of the absolute prediction errors were compared to assess accuracy, and the root means square (RMS) of the prediction errors were compared to assess precision. Results: Overall, the exponential model provided a significantly better fit (P , 0.05) to the data at the majority of test locations (69%). The exponential model fitted the data significantly better at 85% of locations in the upper hemifield and 58% of locations in the lower hemifield. The rate of visual field (VF) deterioration in the upper hemifield was more rapid (mean, À0.21 dB/y; range, À0.28 to À0.13) than in the lower hemifield (mean, À0.14 dB/y; range, À0.2 to À0.09). Conclusions: An exponential model may more accurately track pointwise VF change, at locations damaged by glaucoma. This was more noticeable in the upper hemifield where the VF changed more rapidly. However, linear and exponential models were similar in their ability to forecast future VF status
