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Focus of Presentation Multivariable regression models are widely used in epidemiological data analysis. Traditional teaching often focusses on technical aspects with insufficient attention paid to the purposes for which regression methods are used. Findings We have addressed these issues in a new short course that provides an introduction to regression analysis in the context of learning about causal inference, beginning from the standpoint that the majority of research questions in epidemiology are causal in nature. This approach leads naturally to using regression models in two different ways, firstly for direct estimation of a causal effect, under an assumption of constancy of the effect across strata of confounders, and secondly for prediction of outcomes, as a necessary step in the estimation of causal effects via g-computation. Conclusions/Implications Approaching the teaching of regression methods within a causal inference framework helps to dispel confusion created by traditional statistical approaches that imply the existence of “true models” and encourage the building of models in a way that is unclear about the purpose for which they will be used, for example seeking to identify “risk factors” in an exploratory manner. Key messages The teaching and practice of regression methods in epidemiology can be enhanced by emphasising the key differences between three distinct analytic purposes: description, prediction, causal. Regression models may play a role in all three but the way in which models are developed and interpreted differs between them.

1023The uses and abuses of regression models: a new approach to teaching regression analysis