Haogang Zhu1, David P Crabb2, Tuan Ho3, David F Garway-Heath3. 1. School of Health Sciences City University London, London, United Kingdom 2National Institute for Health Research Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital National Health Service Foundation Trust and UCL Institute of Ophthalmol. 2. School of Health Sciences City University London, London, United Kingdom. 3. National Institute for Health Research Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital National Health Service Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom.
Abstract
PURPOSE: To validate a method for visual field (VF) progression analysis, called ANSWERS (Analysis with Non-Stationary Weibull Error Regression and Spatial Enhancement), which takes into account increasing measurement variability as glaucoma progresses and spatial correlation among test locations. METHODS: ANSWERS outputs both a global index of progression and a pointwise estimate of rate of change at each VF location. ANSWERS was compared with linear regression of mean deviation (MD) and permutation of pointwise linear regression (PoPLR). Visual field series of up to 2 years from the United Kingdom Glaucoma Treatment Study were used. This consists of 9104 Swedish Interactive Thresholding Algorithm Standard 24-2 VFs. ANSWERS and PoPLR rate of change were used to predict the VF at the next visit using subseries that were within 7, 13, 18, or 22 months from the baseline. The comparison was carried out on the statistical sensitivity, specificity, and accuracy of predicting future VF. RESULTS: Across all subseries, statistical sensitivity of ANSWERS in detecting VF deterioration was significantly better than the linear regression of MD and PoPLR, especially in short time series. Prediction accuracy of ANSWERS was better than PoPLR at all series lengths, and the improvement was particularly marked in shorter series. Seventy-five percent of VF series were better predicted by ANSWERS compared with PoPLR. The average prediction error of ANSWERS was 15% lower than that of PoPLR. CONCLUSIONS: ANSWERS is more sensitive to detect VF progression and predicts future VF loss better than linear regression of MD and PoPLR, especially over short observation periods. (http://www.isrctn.com number, ISRCTN96423140.).
RCT Entities:
PURPOSE: To validate a method for visual field (VF) progression analysis, called ANSWERS (Analysis with Non-Stationary Weibull Error Regression and Spatial Enhancement), which takes into account increasing measurement variability as glaucoma progresses and spatial correlation among test locations. METHODS: ANSWERS outputs both a global index of progression and a pointwise estimate of rate of change at each VF location. ANSWERS was compared with linear regression of mean deviation (MD) and permutation of pointwise linear regression (PoPLR). Visual field series of up to 2 years from the United Kingdom Glaucoma Treatment Study were used. This consists of 9104 Swedish Interactive Thresholding Algorithm Standard 24-2 VFs. ANSWERS and PoPLR rate of change were used to predict the VF at the next visit using subseries that were within 7, 13, 18, or 22 months from the baseline. The comparison was carried out on the statistical sensitivity, specificity, and accuracy of predicting future VF. RESULTS: Across all subseries, statistical sensitivity of ANSWERS in detecting VF deterioration was significantly better than the linear regression of MD and PoPLR, especially in short time series. Prediction accuracy of ANSWERS was better than PoPLR at all series lengths, and the improvement was particularly marked in shorter series. Seventy-five percent of VF series were better predicted by ANSWERS compared with PoPLR. The average prediction error of ANSWERS was 15% lower than that of PoPLR. CONCLUSIONS: ANSWERS is more sensitive to detect VF progression and predicts future VF loss better than linear regression of MD and PoPLR, especially over short observation periods. (http://www.isrctn.com number, ISRCTN96423140.).