How Many Subjects are Needed for a Visual Field Normative Database? A Comparison of Ground Truth and Bootstrapped Statistics
AuthorPhu, J; Bui, BV; Kalloniatis, M; Khuu, SK
Source TitleTranslational Vision Science and Technology
PublisherASSOC RESEARCH VISION OPHTHALMOLOGY INC
AffiliationOptometry and Vision Sciences
Anatomy and Neuroscience
Document TypeJournal Article
CitationsPhu, J., Bui, B. V., Kalloniatis, M. & Khuu, S. K. (2018). How Many Subjects are Needed for a Visual Field Normative Database? A Comparison of Ground Truth and Bootstrapped Statistics. TRANSLATIONAL VISION SCIENCE & TECHNOLOGY, 7 (2), https://doi.org/10.1167/tvst.7.2.1.
Access StatusOpen Access
Purpose: The number of subjects needed to establish the normative limits for visual field (VF) testing is not known. Using bootstrap resampling, we determined whether the ground truth mean, distribution limits, and standard deviation (SD) could be approximated using different set size (x) levels, in order to provide guidance for the number of healthy subjects required to obtain robust VF normative data. Methods: We analyzed the 500 Humphrey Field Analyzer (HFA) SITA-Standard results of 116 healthy subjects and 100 HFA full threshold results of 100 psychophysically experienced healthy subjects. These VFs were resampled (bootstrapped) to determine mean sensitivity, distribution limits (5th and 95th percentiles), and SD for different 'x' and numbers of resamples. We also used the VF results of 122 glaucoma patients to determine the performance of ground truth and bootstrapped results in identifying and quantifying VF defects. Results: An x of 150 (for SITA-Standard) and 60 (for full threshold) produced bootstrapped descriptive statistics that were no longer different to the original distribution limits and SD. Removing outliers produced similar results. Differences between original and bootstrapped limits in detecting glaucomatous defects were minimized at x = 250. Conclusions: Ground truth statistics of VF sensitivities could be approximated using set sizes that are significantly smaller than the original cohort. Outlier removal facilitates the use of Gaussian statistics and does not significantly affect the distribution limits. Translational Relevance: We provide guidance for choosing the cohort size for different levels of error when performing normative comparisons with glaucoma patients.
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