The goal of this paper is to highlight the use and interpretation of
statistical techniques that account for correlation in epidemiological data. A conceptual
statistical background is provided, and the main types of regression models for correlated
data are highlighted. These models include marginal models, random effect models and
transitional regression models. For each model type an example with data from the
veterinary literature is provided. The examples are specifically used to highlight
estimation procedures for parameters, and the interpretation of the estimated parameters.
This paper emphasizes that statistical techniques and software to fit them are more widely
available now, but that parameters have different interpretations in different model types.
Consequently, we stress the importance of focusing on choosing the most appropriate model
for the specific purpose of the analysis.