Using data from both eyes of participants: Evaluating the power and Type‐I error rates of common approaches to ocular data analysis via a simulation study

Mixed effects models and GEEs out-perform other approaches when the predictor of interest is bivariate and correlated, assuming correlations are similar for the predictor and outcome. For univariate predictors, averaging the outcome across eyes within each subject performs similarly to mixed effects modeling. Treating correlated measurements as independent (such as when using data from both eyes without averaging or factored into a model) inflates Type-I error rates and yields inappropriately high power, especially as correlation increases; this modeling approach leads to inference errors and should be avoided.