Improving the Power of Glaucoma Neuroprotection Trials Using Existing Visual Field Data

Am J Ophthalmol. 2021 Sep:229:127-136. doi: 10.1016/j.ajo.2021.04.008. Epub 2021 Apr 24.

Abstract

Purpose: Selecting reliable visual field (VF) test takers could improve the power of randomized clinical trials in glaucoma. We test this hypothesis via simulations using a large real world data set.

Design: Methodology analysis: assessment of how improving reliability affects sample size estimates.

Methods: A variability index (VI) estimating intertest variability was calculated for each subject using the residuals of the regression of the mean deviation over time for the first 6 tests in a series of at least 10 examinations for 2,804 patients. Using data from the rest of the series, we simulate VFs at regular intervals for 2 years. To simulate the neuroprotective effect (NE), we reduced the observed progression rate by 20%, 30%, or 50%. The main outcome measure was the sample size to detect a significant difference (P < .05) at 80% power.

Results: In the first experiment, we simulated a trial including one eye per subject, either selecting randomly from the database or prioritizing patients with low VI. We could not reach 80% power for the low NE with the available patients, but the sample size was reduced by 38% and 49% for the 30% and 50% NE, respectively. In the second experiment, we simulated 2 eyes per subject, one of which was the control eye. The sample size (smaller overall) was reduced by 26% and 38% for the 30% and 50% NE by prioritizing patients with low VI.

Conclusions: Selecting patients with low intertest variability can significantly improve the power and reduce the sample size needed in a trial.

Keywords: Clinical trial; Glaucoma; Neuroprotection; Perimetry; Visual field.

MeSH terms

  • Disease Progression
  • Glaucoma* / drug therapy
  • Humans
  • Intraocular Pressure
  • Neuroprotection
  • Randomized Controlled Trials as Topic
  • Reproducibility of Results
  • Vision Disorders / prevention & control
  • Visual Field Tests
  • Visual Fields*