Generalized estimating equations are the main alternative to mixed models for grouped and longitudinal data. Instead of modeling group effects explicitly, a GEE estimates the marginal relationship between predictors and outcome across the whole population, treats the within-group correlation as a nuisance described by a working structure (exchangeable, autoregressive), and protects the standard errors with a robust sandwich estimator that stays honest even when that structure is wrong.
The choice between GEE and a mixed model is a choice of question, not of quality: a GEE answers “what is the average effect across the population?”, while a mixed model’s coefficients answer “what is the effect within a group?”. For nonlinear links such as the logit the two answers genuinely differ, with the marginal effect attenuated relative to the conditional one.