B. Chauhan
Detection of glaucomatous visual field progression often takes a considerable length
of time, requiring frequent and regular testing over several years. More precise
analytical tools would be needed to shorten the duration of clinical trials evaluating
treatment effect on disease progression. Robust and meaningful estimates of variability
and test-retest variability are required, and clarity is needed on what constitutes
meaningful change. Either event-based or trend-based approaches to change analysis
can be adopted to derive information on degree or rate of change. Compared with
the event-based approach, trend based approaches are more appropriate for providing
information on the rate of progression, which is important for clinical decisions.
Trend-based analysis represents a linear regression analysis of data over time,
with a comparison made with the patient’s own data. This approach has certain advantages,
particularly when many examinations are available as the visual field measurements
over time can be used to determine the rate and magnitude of change over time.
The
tools that have been developed for longitudinal analysis include the Glaucoma Progression
Analysis (GPA), the Topographical Change Analysis (TCA) on the HRT and linear regression
techniques. Event analysis may identify progressive test locations with as few as
three test results, but it is dependent on the degree of change exceeding test-retest
variability, which can be high for damaged locations. In perimetry, population-based
estimates are used as surrogates for individual-specific estimates of variability.
To maintain reasonable specificity, most investigators have found it necessary
to have glaucoma change probability points outside normal limits to be confirmed
on two or more tests. While longitudinal analysis is a more appropriate approach
than cross-sectional analysis, it has rarely been used in recent glaucoma trials.
There are many quantitative assessments that can be done when seeking to measure
or monitor structural change. Growing experience with modern imaging techniques
will help identify which parameters best represent clinically meaningful information
in the measurement of change. The detection of change will aid the clinician not
only in assessment of progression of existing damage but also establishing the diagnosis
of glaucoma. How much change is meaningful in glaucomaand how do we select criteria
for change? Change criteria must reflect what relevant false positive rate can
be tolerated. Methods for estimating false-positive rates include using the rate
of change in a group of healthy patients, the rate of improvement in the same eye
or use of permutation analysis for determining rate of change in the optic disc
or visual field.
Because modern imaging devices allow measurements of the optic
disc surface, depth and shape parameters such as cup volume, maximum cup death,
and cup shape measure can also be determined. Localized analysis of the height measurements
of the optic disc surface and peripapillary retina can be conducted using a probability-based
analysis called topographic change analysis (Chauhan et al., 2000). With this technique,
clusters of pixels where change has occurred can be analyzed over time using a variety
of methods. Statistic image mapping, a proven quantitative technique widely used
in neuroimaging, has been shown to have better diagnostic precision in detecting
change in series of HRT images when compared with current quantitative techniques
(Patterson et al., 2005). A pixel-by-pixel analysis of topographic height over time
yields a statistic image that is generated by using permutation testing, derives significance limits for change wholly from the patient’s own data, and removes
the need for reference data sets. These are examples of potential new statistical
techniques for quantitative assessment of structural progression that could ultimately
help shorten trial duration. It should be borne in mind that current clinical indicators
of visual function and measures of optic disc structure provide largely independent
measures of progression in OAG (Artes and Chauhan, 2005).
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