Purpose: The eye is often the unit of measurement for outcomes, and frequently also for covariates, in vision research, and measurements in the two eyes of the same person are often strongly but far from perfectly correlated. Advances have occurred in the development and accessibility of analytic approaches to evaluate determinants of eye-specific outcomes including information from both eyes of some subjects.
Methods: We illustrate available regression approaches to analyze correlated outcomes from both eyes in datasets with both eye- and subject-specific exposures and potential confounding variables. We consider cross-sectional and longitudinal study designs, and discrete, continuous, and time-to-event outcomes.
Results: Across a range of study designs and measurement scales for the outcome variable, we show the under-estimation of P-values and widths of confidence intervals that occurs when the correlation between paired eyes in a person is ignored, and the reduced precision that occurs in separate analyses of right or left eyes, or in analyses of persons rather than eyes. By comparison, regression models with the eye as the unit of analysis and appropriate consideration of the correlation between paired outcomes generally offer maximal use of available data, enhanced interpretability of covariate-outcome associations, and efficient use of information from subjects who contribute only one eye to analyses.
Conclusions: For many studies in vision research, the now widely available regression models that appropriately treat the eye as the unit of analysis offer the best analytic approach.