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Regression approaches to estimation of relative risk : application to multiple sclerosis studies Fu, Shing

Abstract

Using a log link for binary response in generalized linear mixed-effects models (GLMM) allows direct estimation of the relative risk. If a random intercept is the only random effect in the conditional mean structure, the marginal mean has the same form. The fixed effects, representing the log relative risks, have the same interpretation in both the mixed-effects model and the marginal model. This leads to two approaches to estimate the relative risks, 1) maximum likelihood for the mixed-effects models and 2) the generalized estimating equations (GEE) approach for the marginal models. In our study, we apply such log-linear models to assess the effects of neutralizing antibodies on interferon beta-1b in relapsing-remitting multiple sclerosis. The results obtained by the two approaches are compared. The relative efficiency of the GEE approach and the robustness of the GLMM approach to some forms of misspecification of the model for the random effects are studied by simulations.

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