Summary Case–control studies augmented by the values of responses and covariates from family members allow investigators to study the association between the response and genetics and environment by relating differences in the response directly to within-family differences in covariates. However, existing approaches for case–control family data parameterize covariate effects in terms of the marginal probability of response, the same effects that one estimates from standard case–control studies. This article focuses on the estimation of family-specific covariate effects and develops efficient methods to fit family-specific models such as binary mixed-effects models. We also extend the approach to cover any setting where one has a fully specified model for the vector of responses in a family. We illustrate our approach using data from a case–control family study of brain cancer and consider the use of weighted and conditional likelihood methods as alternatives.