ArticleTranslational vision science & technology2021
Visual Field Progression in Glaucoma: Comparison Between PoPLR and ANSWERS.
Article in Translational vision science & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Functional generalized estimating equation model to detect glaucomatous visual field progression.International journal of ophthalmology · 2026Article
- Combined Probability Test for Sectoral Progression of the Circumpapillary Retinal Nerve Fiber Layer Thickness.Translational vision science & technology · 2025Article
- The Open Perimetry Initiative: A framework for cross-platform development for the new generation of portable perimeters.Journal of vision · 2022Article
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Authors and funding
4 authors.
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Abstract
Purpose: It has been suggested that the detection of visual field progression can be improved by modeling statistical properties of the data such as the increasing retest variability and the spatial correlation among visual field locations. We compared a method that models those properties, Analysis with Non-Stationary Weibull Error Regression and Spatial Enhancement (ANSWERS), against a simpler one that does not, Permutation of Pointwise Linear Regression (PoPLR). Methods: Visual field series from three independent longitudinal studies in patients with glaucoma were used to compare the positive rate of PoPLR and ANSWERS. To estimate the false-positive rate, the same visual field series were randomly re-ordered in time. The first dataset consisted of series of 7 visual fields from 101 eyes, the second consisted of series of 9 visual fields from 150 eyes, and the third consisted of series of more than 9 visual fields (17.5 on average) from 139 eyes. Results: For a statistical significance of 0.05, the false-positive rates for ANSWERS were about 3 times greater than expected at 15%, 17%, and 16%, respectively, whereas for PoPLR they were 7%, 3%, and 6%. After equating the specificities at 0.05 for both models, positive rates for ANSWERS were 16%, 25%, and 38%, whereas for PoPLR they were 12%, 33%, and 49%, or about 5% greater on average (95% confidence interval = -1% to 11%). Conclusions: Despite being simpler and less computationally demanding, PoPLR was at least as sensitive to deterioration as ANSWERS once the specificities were equated. Translational Relevance: Close control of false-positive rates is key when visual fields of patients are analyzed for change in both clinical practice and clinical trials.
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