Methods are presented for performing multiple regression analyses and multiple logistic regression analyses on ophthalmologic data with normally and binomially distributed outcome variables, while accounting for the intraclass correlation between eyes. These methods are extended to more general nested data structures where a variable number of subunits are available for each primary unit of analysis, as in familial data. These methods can also be applied to other types of paired data, as in matched studies with a variable matching ratio, where one has a continuous outcome variable and wishes to control for other confounding variables while maintaining the matching. Examples are given of these methods with a group of over 400 patients with retinitis pigmentosa, in which spherical refractive error and visual acuity are related to genetic type after the effects of age, sex and the presence of cataract, have been controlled.