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We describe generalized linear latent and mixed models (GLLAMMs) and illustrate their potential in epidemiology. GLLAMMs include many types of multilevel random effect, factor and structural equation models. A wide range of response types are accommodated including continuous, dichotomous, ordinal and nominal responses as well as counts and survival times. Multivariate responses can furthermore be of mixed types. The utility of GLLAMMs is illustrated in three applications involving repeated measurements, measurement error and multilevel data.

Some applications of generalized linear latent and mixed models in epidemiology: Repeated measures, measurement error and multilevel modeling