Objective:

To evaluate the impact of different statistical methods on the accuracy of ophthalmic research by accounting for correlations between a patient's two eyes.

Key Findings:

• Treating measurements from both eyes as independent leads to inflated Type-I error rates.
• Mixed effects models and averaging measurements maintained appropriate Type-I error rates.
• Model performance varies based on whether predictors are measured per subject or per eye.

Interpretation:

Selecting appropriate statistical models that account for interocular correlation is crucial to avoid misleading results in ophthalmic research.

Limitations:

• The study is based on simulated data, which may not fully capture real-world complexities.

Conclusion:

Mixed effects models and generalized estimating equations are preferred for eye-level predictors, while averaging may suffice for subject-level predictors. Avoid treating both eyes as independent observations.