Background: When the treatment effect on the outcome of interest is influenced by a baseline/demographic factor, investigators say that an interaction is present. In randomized clinical trials (RCTs), this type of analysis is typically referred to as subgroup analysis. Although interaction (or subgroup) analyses are usually stated as a secondary study objective, it is not uncommon that these results lead to changes in treatment protocols or even modify public health policies. Nonetheless, recent reviews have indicated that their proper assessment, interpretation and reporting remain challenging.
Results: Therefore, this article provides an overview of these challenges, to help investigators find the best strategy for application of interaction analyses on binary outcomes in RCTs. Specifically, we discuss the key points of formal interaction testing, including the estimation of both additive and multiplicative interaction effects. We also provide recommendations that, if adhered to, could increase the clarity and the completeness of reports of RCTs.
Conclusion: Altogether, this article provides a brief non-statistical guide for clinical investigators on how to perform, interpret and report interaction (subgroup) analyses in RCTs.
Keywords: effect modification; heterogeneity; interaction; randomized clinical trial; stratification; subgroup analysis; trial.
© 2019 The Authors. European Journal of Clinical Investigation published by John Wiley & Sons Ltd on behalf of Stichting European Society for Clinical Investigation Journal Foundation.