Handling interactions in Stata, especially with continuous predictors

In an era in which doctors and patients aspire to personalized medicine, detecting and modeling interactions between covariates or between covariates and treatment is becoming increasingly important. In observational studies, for example, in epidemiology, interactions are known as effect modifiers; their presence can substantially change the understanding of how a risk factor impacts the outcome. However, modeling interactions in an appropriate and interpretable way is not straightforward. In our talk, we consider three main topics. The first topic is the nuts and bolts of factor variables and interactions in Stata. We outline how Stata's parameterizations of interactions between factor variables work in regression models. The second topic is modeling interactions in observational studies that involve at least one continuous covariate, an area that practitioners apparently find difficult. We introduce a new Stata program, mfpigen, for detecting and modeling such interactions using fractional polynomials, adjusting for confounders if necessary. The third topic is modeling interactions between treatment and continuous covariates in randomized controlled trials. We outline a Stata program, mfpi, designed for this purpose. Key themes of our talk are the vital role played by graphical displays of interactions and the importance of applying simple plausibility checks.