Effect Modifiers and Statistical Tests for Interaction in Randomized Trials

Statistical analyses of randomized controlled trials (RCTs) yield a causally valid estimate of the overall treatment effect, which is the contrast between the outcomes in two randomized treatment groups commonly accompanied by a confidence interval. In addition, the trial investigators may want to examine whether the observed treatment effect varies across patient subgroups (also called 'heterogeneity of treatment effects'), i.e. whether the treatment effect is modified by the value of a variable assessed at baseline. The statistical approach for this evaluation of potential effect modifiers is a test for statistical interaction to evaluate whether the treatment effect varies across levels of the effect modifier. In this article, we provide a concise and nontechnical explanation of the use of simple statistical tests for interaction to identify effect modifiers in RCTs. We explain how to calculate the test of interaction by hand, applied to a dataset with simulated data on 1,000 imaginary participants for illustration. (C) 2021 The Author(s). Published by Elsevier Inc.

Automated summary

This article explains how to use simple statistical tests for interaction to identify effect modifiers in randomized controlled trials (RCTs). Researchers typically examine whether the observed treatment effect varies across different patient subgroups to determine if the treatment effect is influenced by baseline variables.