Comparing the Rate of Retinal Nerve Fiber Layer and Visual Field Loss as Outcomes in Glaucoma Trials

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PURPOSE: This study aimed to compare the statistical power of structural and visual field (VF) outcomes for randomized clinical trials (RCTs) in glaucoma. DESIGN: Analysis of retrospectively collected data. PARTICIPANTS: Eighty-two patients with glaucoma were recruited to a test-retest study, during which up to 10 24-2 Swedish Interactive Thresholding Algorithm (SITA) Standard VF and circumpapillary retinal nerve fiber layer (cpRNFL) Spectralis OCT scans were collected in separate sessions over 3 months. METHODS: Eyes with at least 3 sessions with a reliable VF (false-positives < 15%) and cpRNFL scan (quality index ≥ 25 dB) were selected (127 eyes, 68 patients) to model the test-retest variability and the structural floor effect. These estimates were combined with a published realistic structure-function progression model from the United Kingdom Glaucoma Treatment Study to simulate longitudinal RCTs (30% neuroprotective effect). Simulations only included data from eyes with early to moderate VF loss (mean deviation [MD], ≥-10 dB, 107 eyes, 65 patients). Simulations were repeated 5000 times to estimate sample size requirements to detect a significant difference (P < 0.05) in the rate of change of MD and average cpRNFL thickness, estimated with a linear mixed-effects model. We also tested the power of a significant outcome with either metric (P < 0.025). A supplementary analysis was performed including eyes with early VF loss only (MD ≥ -6 dB). MAIN OUTCOME MEASURES: Sample size at 80% power for the linear rate of MD, cpRNFL, and their combination. RESULTS: At 80% power, the required sample size (patients [95% confidence interval]) was 38% smaller for the MD rate (292 [300-283]) than the cpRNFL rate (470 [481-459]). The sample size for the combined outcome was only marginally smaller than the MD alone (275 [283-268]). The supplementary analysis on eyes with early VF loss showed similar results. CONCLUSIONS: Using realistic modeling of structure-function progression and test-retest data, MD progression showed higher statistical power cpRFNL as an outcome measure for clinical trials. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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