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.

Automated summary

The study aimed to analyze longitudinal correlated eye data with different models and found treatment effects in CAPT study but not in AREDS study. There were varying effects on vision decline for smokers in different models used in the analysis.