Tutorial on Biostatistics: Longitudinal Analysis of Correlated Continuous Eye Data.

Purpose: To describe and demonstrate methods for analyzing longitudinal correlated eye data with a continuous outcome measure.
Methods: We described fixed effects, mixed effects and generalized estimating equations (GEE) models, applied them to data from the Complications of Age-Related Macular Degeneration Prevention Trial (CAPT) and the Age-Related Eye Disease Study (AREDS). In CAPT (N = 1052), we assessed the effect of eye-specific laser treatment on change in visual acuity (VA). In the AREDS study, we evaluated effects of systemic supplement treatment among 1463 participants with AMD category 3.
Results: In CAPT, the inter-eye correlations (0.33 to 0.53) and longitudinal correlations (0.31 to 0.88) varied. There was a small treatment effect on VA change (approximately one letter) at 24 months for all three models ( p = .009 to 0.02). Model fit was better with the mixed effects model than the fixed effects model ( p < .001). In AREDS, there was no significant treatment effect in all models ( p > .55). Current smokers had a significantly greater VA decline than non-current smokers in the fixed effects model ( p = .04) and the mixed effects model with random intercept ( p = .0003), but marginally significant in the mixed effects model with random intercept and slope ( p = .08), and GEE models ( p = .054 to 0.07). The model fit was better with the fixed effects model than the mixed effects model ( p < .0001).
Conclusion: Longitudinal models using the eye as the unit of analysis can be implemented using available statistical software to account for both inter-eye and longitudinal correlations. Goodness-of-fit statistics may guide the selection of the most appropriate model.