This workshop will provide a broad introduction to the topic of interaction. We discuss interaction on both additive and multiplicative scales, and we discuss their relation to statistical models (e.g. linear, log-linear and logistic models). We describe procedures for interaction when logistic models are fit to data but when additive and not just multiplicative measures of interaction are desired. We discuss issues of confounding for interaction analyses and how whether control has been made for only one or both of two exposures affects interpretation. We further discuss conditions under which interaction gives evidence of synergism within the sufficient cause framework, when interaction is robust to unmeasured confounding, methods attributing effects to interaction, case-only estimators of interaction, and power and sample size calculations for interaction. Illustrations will be given from environmental, genetic, and infectious disease epidemiology. Software code will be provided.
