COMPARISON OF ALTERNATIVE REGRESSION-MODELS FOR PAIRED BINARY DATA

We used simulated data, derived from real ophthalmologic examples, to evaluate the performance of alternative logistic regression approaches for paired binary data. Approaches considered were: standard logistic regression (ignoring the correlation between fellow eyes, treating in dividuals classified on the basis of their more impaired eye as the un it of analysis, or considering only right eyes), marginal logistic reg ression models fitted by the maximum likelihood approach of Lipsitz, L aird and Harrington or the estimating equation approach of Liang and Z eger; and conditional logistic regression models fitted by the maximum likelihood approach of Rosner or the estimating equation approach of Connolly and Liang. Taylor series approximations were used to compare conditional and marginal parameter estimates. Consideration of type I and II error rates found application of standard logistic regression t o be inferior to methods that treated the eye as the unit of analysis and accounted for the correlation between fellow eyes. Among these lat ter approaches, none was uniformly superior to the others across the r ange of conditions considered.