effect-measure-modification

To test for effect measure modification, you add interaction terms to your regression model and evaluate whether the association between exposure and outcome varies across levels of a third variable.

Interaction analysis in epidemiology detects whether a third variable modifies the association between an exposure and an outcome, requiring regression model construction to assess and report these interaction effects.

When interaction is present, you compute and report stratum-specific estimates by analyzing the interaction effects within your regression model to show how the association varies across different levels.

Yes, you can analyze whether the association between smoking and lung cancer differs by age groups by running the interaction analysis script on your dataset with age as the third variable.

You need a dataset containing your exposure, outcome, and the third variable, such as a CSV file, to construct regression models and estimate the moderation effects accurately.

Limitations of interaction terms include the need for specific regression model construction and estimation techniques to accurately detect modification, requiring sufficient data within each stratum to report reliable estimates.