Properties of Perimetric Threshold Estimates from Full Threshold, ZEST, and SITA-like Strategies, as Determined by Computer Simulation

To investigate the accuracy and precision of threshold estimates returned by two Bayesian perimetric strategies, staircase-QUEST or SQ (a Swedish interactive threshold algorithm [SITA]-like strategy) and ZEST (zippy estimation by sequential testing), and to compare these measures with those of the f...

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Published inInvestigative ophthalmology & visual science Vol. 44; no. 11; pp. 4787 - 4795
Main Authors Turpin, Andrew, McKendrick, Allison M, Johnson, Chris A, Vingrys, Algis J
Format Journal Article
LanguageEnglish
Published Rockville, MD ARVO 01.11.2003
Association for Research in Vision and Ophtalmology
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Abstract To investigate the accuracy and precision of threshold estimates returned by two Bayesian perimetric strategies, staircase-QUEST or SQ (a Swedish interactive threshold algorithm [SITA]-like strategy) and ZEST (zippy estimation by sequential testing), and to compare these measures with those of the full-threshold (FT) algorithm. A computerized visual field simulation model was developed to compare the performance (accuracy, precision, and number of presentations) of the three algorithms. SQ implemented aspects of the SITA algorithm that are in the public domain. The simulation was tested by using standard automated perimetry (SAP) visual field data from 265 normal subjects and 163 observers with glaucomatous visual field loss and by exploring the effect of response variability and response errors on algorithm performance. SQ was faster than FT or ZEST, with a comparable mean error when simulating field tests on patients. Point-wise analysis revealed similar error and standard deviation of error as a function of threshold for FT and SQ. If the initial estimate of threshold for either procedure was incorrect, the means and standard deviations of the error increased markedly. ZEST produced more accurate thresholds than did the other two strategies when the initial estimate was removed from the true threshold. When simulated patients made errors, the accuracy and precision of sensitivity estimates were poor when the initial estimate of threshold either overestimated or underestimated the true threshold. This was particularly so for FT and SQ. ZEST demonstrated more consistent error properties than the other two measures.
AbstractList To investigate the accuracy and precision of threshold estimates returned by two Bayesian perimetric strategies, staircase-QUEST or SQ (a Swedish interactive threshold algorithm [SITA]-like strategy) and ZEST (zippy estimation by sequential testing), and to compare these measures with those of the full-threshold (FT) algorithm.PURPOSETo investigate the accuracy and precision of threshold estimates returned by two Bayesian perimetric strategies, staircase-QUEST or SQ (a Swedish interactive threshold algorithm [SITA]-like strategy) and ZEST (zippy estimation by sequential testing), and to compare these measures with those of the full-threshold (FT) algorithm.A computerized visual field simulation model was developed to compare the performance (accuracy, precision, and number of presentations) of the three algorithms. SQ implemented aspects of the SITA algorithm that are in the public domain. The simulation was tested by using standard automated perimetry (SAP) visual field data from 265 normal subjects and 163 observers with glaucomatous visual field loss and by exploring the effect of response variability and response errors on algorithm performance.METHODSA computerized visual field simulation model was developed to compare the performance (accuracy, precision, and number of presentations) of the three algorithms. SQ implemented aspects of the SITA algorithm that are in the public domain. The simulation was tested by using standard automated perimetry (SAP) visual field data from 265 normal subjects and 163 observers with glaucomatous visual field loss and by exploring the effect of response variability and response errors on algorithm performance.SQ was faster than FT or ZEST, with a comparable mean error when simulating field tests on patients. Point-wise analysis revealed similar error and standard deviation of error as a function of threshold for FT and SQ. If the initial estimate of threshold for either procedure was incorrect, the means and standard deviations of the error increased markedly. ZEST produced more accurate thresholds than did the other two strategies when the initial estimate was removed from the true threshold.RESULTSSQ was faster than FT or ZEST, with a comparable mean error when simulating field tests on patients. Point-wise analysis revealed similar error and standard deviation of error as a function of threshold for FT and SQ. If the initial estimate of threshold for either procedure was incorrect, the means and standard deviations of the error increased markedly. ZEST produced more accurate thresholds than did the other two strategies when the initial estimate was removed from the true threshold.When simulated patients made errors, the accuracy and precision of sensitivity estimates were poor when the initial estimate of threshold either overestimated or underestimated the true threshold. This was particularly so for FT and SQ. ZEST demonstrated more consistent error properties than the other two measures.CONCLUSIONSWhen simulated patients made errors, the accuracy and precision of sensitivity estimates were poor when the initial estimate of threshold either overestimated or underestimated the true threshold. This was particularly so for FT and SQ. ZEST demonstrated more consistent error properties than the other two measures.
To investigate the accuracy and precision of threshold estimates returned by two Bayesian perimetric strategies, staircase-QUEST or SQ (a Swedish interactive threshold algorithm [SITA]-like strategy) and ZEST (zippy estimation by sequential testing), and to compare these measures with those of the full-threshold (FT) algorithm. A computerized visual field simulation model was developed to compare the performance (accuracy, precision, and number of presentations) of the three algorithms. SQ implemented aspects of the SITA algorithm that are in the public domain. The simulation was tested by using standard automated perimetry (SAP) visual field data from 265 normal subjects and 163 observers with glaucomatous visual field loss and by exploring the effect of response variability and response errors on algorithm performance. SQ was faster than FT or ZEST, with a comparable mean error when simulating field tests on patients. Point-wise analysis revealed similar error and standard deviation of error as a function of threshold for FT and SQ. If the initial estimate of threshold for either procedure was incorrect, the means and standard deviations of the error increased markedly. ZEST produced more accurate thresholds than did the other two strategies when the initial estimate was removed from the true threshold. When simulated patients made errors, the accuracy and precision of sensitivity estimates were poor when the initial estimate of threshold either overestimated or underestimated the true threshold. This was particularly so for FT and SQ. ZEST demonstrated more consistent error properties than the other two measures.
Author Turpin, Andrew
McKendrick, Allison M
Johnson, Chris A
Vingrys, Algis J
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SubjectTerms Algorithms
Bayes Theorem
Biological and medical sciences
Computer Simulation
Glaucoma - physiopathology
Humans
Investigative techniques of ocular function and vision
Investigative techniques, diagnostic techniques (general aspects)
Medical sciences
Middle Aged
Optic Nerve Diseases - physiopathology
Reproducibility of Results
Sensory Thresholds
Vision Disorders - physiopathology
Visual Field Tests
Visual Fields - physiology
Title Properties of Perimetric Threshold Estimates from Full Threshold, ZEST, and SITA-like Strategies, as Determined by Computer Simulation
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