PURPOSE: Our aim was to compare fit and predictive performance effectiveness of four pointwise regression models in measuring the visual field (VF) decay rate of progression in patients with open-angle glaucoma. METHODS: We selected Humphrey VF data of patients with open-angle glaucoma with a minimum follow-up time of 6 years. For each eye (n = 798 from 588 patients), we regressed threshold sensitivity (y) at each VF test location for the entire VF series against follow-up time (x), with four candidate first-order regression models: (1) ordinary least-squares linear regression model (y = β 0 + β 1 x); (2) nondecay exponential regression model (y = β 0 + β 1e (x) ); (3) decay exponential regression model ([Formula: see text]); (4) Tobit-censored, maximum-likelihood linear regression model (y* = [Formula: see text], ε ~ N(0, σ(2))), where x is follow-up time and y is threshold sensitivity. RESULTS: The average [± standard deviation (SD)] baseline VF mean deviation (MD) was -8.2 (±5.5) dB, the mean follow-up was 8.7 (±1.9) years, and the number of follow-up VFs was 14.7 (±4.4). The decay exponential model was the best-fitting (42.7 % of locations) and best-forecasting (65.5 % of locations) model. The decay exponential model was the best prediction model in all categories of severity. CONCLUSIONS: It is not clear that the ordinary least-squares linear regression model is always the favored model for fitting and forecasting VF data in patients with glaucoma. The pointwise decay exponential regression (PER) model was the best-fitting and best-predicting model across a wide range of glaucoma severity and can be readily understood by clinicians.
PURPOSE: Our aim was to compare fit and predictive performance effectiveness of four pointwise regression models in measuring the visual field (VF) decay rate of progression in patients with open-angle glaucoma. METHODS: We selected Humphrey VF data of patients with open-angle glaucoma with a minimum follow-up time of 6 years. For each eye (n = 798 from 588 patients), we regressed threshold sensitivity (y) at each VF test location for the entire VF series against follow-up time (x), with four candidate first-order regression models: (1) ordinary least-squares linear regression model (y = β 0 + β 1 x); (2) nondecay exponential regression model (y = β 0 + β 1e (x) ); (3) decay exponential regression model ([Formula: see text]); (4) Tobit-censored, maximum-likelihood linear regression model (y* = [Formula: see text], ε ~ N(0, σ(2))), where x is follow-up time and y is threshold sensitivity. RESULTS: The average [± standard deviation (SD)] baseline VF mean deviation (MD) was -8.2 (±5.5) dB, the mean follow-up was 8.7 (±1.9) years, and the number of follow-up VFs was 14.7 (±4.4). The decay exponential model was the best-fitting (42.7 % of locations) and best-forecasting (65.5 % of locations) model. The decay exponential model was the best prediction model in all categories of severity. CONCLUSIONS: It is not clear that the ordinary least-squares linear regression model is always the favored model for fitting and forecasting VF data in patients with glaucoma. The pointwise decay exponential regression (PER) model was the best-fitting and best-predicting model across a wide range of glaucoma severity and can be readily understood by clinicians.
Authors: E B Werner; K I Bishop; J Koelle; G R Douglas; R P LeBlanc; R P Mills; B Schwartz; W R Whalen; J T Wilensky Journal: Arch Ophthalmol Date: 1988-05
Authors: A C Viswanathan; D P Crabb; A I McNaught; M C Westcott; D Kamal; D F Garway-Heath; F W Fitzke; R A Hitchings Journal: Br J Ophthalmol Date: 2003-06 Impact factor: 4.638