Generalized linear mixed-effects models combine linear regression and generalized linear models to analyze longitudinal data. They include random effects that vary between subjects, creating within-subject dependence in repeated measurements. This approach is useful for studying how different factors affect outcomes over time, like tracking changes in health or behavior.