Tutorial on Biostatistics: Statistical Analysis for Correlated Binary Eye Data. (2nd January 2018)

• Record Type: Journal Article
• Authors: Ying, Gui-shuang; Maguire, Maureen G.; Glynn, Robert; Rosner, Bernard
• Is Part Of: Ophthalmic epidemiology. Volume 25:Number 1(2018)
• Page Start: 1
• Page End: 12
• Publication Date: 2018-01-02
• Subjects: Correlated binary data -- generalized estimating equations -- generalized linear mixed effects model -- inter-eye correlation -- marginal model; Blindness -- Prevention -- Periodicals; Eye -- Diseases -- Epidemiology -- Periodicals; Ophthalmology -- Periodicals; 614.5997
• DOI: 10.1080/09286586.2017.1320413
• Languages: English
• ISSNs: 0928-6586

Abstract

ABSTRACT: Purpose : To describe and demonstrate methods for analyzing correlated binary eye data. Methods : We describe non-model based (McNemar's test, Cochran-Mantel-Haenszel test) and model-based methods (generalized linear mixed effects model, marginal model) for analyses involving both eyes. These methods were applied to: (1) CAPT (Complications of Age-related Macular Degeneration Prevention Trial) where one eye was treated and the other observed (paired design); (2) ETROP (Early Treatment for Retinopathy of Prematurity) where bilaterally affected infants had one eye treated conventionally and the other treated early and unilaterally affected infants had treatment assigned randomly; and (3) AREDS (Age-Related Eye Disease Study) where treatment was systemic and outcome was eye-specific (both eyes in the same treatment group). Results : In the CAPT ( n = 80), treatment group (30% vision loss in treated vs. 44% in observed eyes) was not statistically significant ( p = 0.07) when inter-eye correlation was ignored, but was significant ( p = 0.01) with McNemar's test and the marginal model. Using standard logistic regression for unfavorable vision in ETROP, standard errors and p -values were larger for person-level covariates and were smaller for ocular covariates than using models accounting for inter-eye correlation. For risk factors of geographic atrophy in AREDS, two-eye analyses accounting for inter-eye correlation yielded more power than one-eye analyses and provided larger standard errors and p -values than invalid two-eye analyses ignoring inter-eye correlation. Conclusion : Ignoring inter-eye correlation can lead to larger p -values for paired designs and smaller p -values when both eyes are in the same group. Marginal models or mixed effects models using the eye as the unit of analysis provide valid inference.