Advantages of terminating Zippy Estimation by Sequential Testing (ZEST) with dynamic criteria for white-on-white perimetry

McKendrick, A and Turpin, A 2005, 'Advantages of terminating Zippy Estimation by Sequential Testing (ZEST) with dynamic criteria for white-on-white perimetry', Optometry and vision science, vol. 82, no. 11, pp. 981-987.


Document type: Journal Article
Collection: Journal Articles

Title Advantages of terminating Zippy Estimation by Sequential Testing (ZEST) with dynamic criteria for white-on-white perimetry
Author(s) McKendrick, A
Turpin, A
Year 2005
Journal name Optometry and vision science
Volume number 82
Issue number 11
Start page 981
End page 987
Total pages 7
Publisher American academy of optometry
Abstract Purpose. A number of automated perimeters use the Zippy Estimation by Sequential Testing (ZEST) algorithm, which is an adaptive Bayesian method, for determining sensitivity measures. There are two popular rules for deciding when to terminate Bayesian procedures: (1) after a fixed number of presentations; or (2) when the probability density function (pdf) over all thresholds modified by the procedure becomes sufficiently narrow (a dynamic termination criterion). It has recently been argued that fixed termination criteria perform equally as well as dynamic criteria when applied in a fashion typical of laboratory-based visual psychophysics. Perimetry, however, has specific requirements; the tests must be very short, there is a wide range of possible sensitivities, and erroneous responses from the patient must be tolerated. This study used computer simulation to compare fixed and dynamic termination criteria for the ZEST algorithm using conditions typical of white-on-white perimetry. Methods. Eight ZEST procedures were compared using the following termination criteria: fixed termination after 4, 5, 6, 7, and 8 presentations; dynamic termination when the standard deviation of the pdf was 1 dB, 1.5 dB, and 2 dB. Four patient error models were used: ideal, typical false-positive, typical false-negative, and unreliable patients. We also ran a version of ZEST that set the likelihood function exactly equal to the patient's frequency of seeing curve.
Subject Clinical Sciences not elsewhere classified
Copyright notice Copyright © 2005 American Academy of Optometry.
ISSN 1040-5488
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