[Skip to Content]
Access to paid content on this site is currently suspended due to excessive activity being detected from your IP address 54.159.158.180. Please contact the publisher to request reinstatement.
[Skip to Content Landing]
Article
March 1992

Accounting for the Correlation Between Fellow Eyes in Regression Analysis

Author Affiliations

From the Department of Medicine, Brigham and Women's Hospital and the Channing Laboratory, Harvard Medical School (Drs Glynn and Rosner) and the Department of Biostatistics, Harvard School of Public Health (Dr Rosner), Boston, Mass.

Arch Ophthalmol. 1992;110(3):381-387. doi:10.1001/archopht.1992.01080150079033
Abstract

• Regression techniques that appropriately use all available eyes have infrequently been applied in the ophthalmologic literature, despite advances both in the development of statistical models and in the availability of computer software to fit these models. We considered the general linear model and polychotomous logistic regression approaches of Rosner and the estimating equation approach of Liang and Zeger, applied to both linear and logistic regression. Methods were illustrated with the use of two real data sets: (1) impairment of visual acuity in patients with retinitis pigmentosa and (2) overall visual field impairment in elderly patients evaluated for glaucoma. We discuss the interpretation of coefficients from these models and the advantages of these approaches compared with alternative approaches, such as treating individuals rather than eyes as the unit of analysis, separate regression analyses of right and left eyes, or utilization of ordinary regression techniques without accounting for the correlation between fellow eyes. Specific advantages include enhanced statistical power, more interpretable regression coefficients, greater precision of estimation, and less sensitivity to missing data for some eyes. We concluded that these models should be used more frequently in ophthalmologic research, and we provide guidelines for choosing between alternative models.

×