5 Key Takeaways

• 1

  Statistical methods in ophthalmic research vary in accuracy based on how they account for correlations between a patient's two eyes.

• 2

  Analyzing eye measurements as independent observations leads to inflated Type-I error rates and inaccurate results.

• 3

  Mixed effects models and generalized estimating equations are preferred for eye-level predictors due to their statistical power.

• 4

  Averaging measurements is effective for subject-level predictors, while single-eye analysis shows lower power.

• 5

  Researchers emphasize careful model selection to avoid misleading results in studies involving correlated ocular data.