TY - JOUR
T1 - Effect Modifiers and Statistical Tests for Interaction in Randomized Trials
AU - Christensen, R.
AU - Bours, M.J.L.
AU - Nielsen, S.M.
PY - 2021/6/1
Y1 - 2021/6/1
N2 - 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.
AB - 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.
U2 - 10.1016/j.jclinepi.2021.03.009
DO - 10.1016/j.jclinepi.2021.03.009
M3 - Article
C2 - 34016442
SN - 0895-4356
VL - 134
SP - 174
EP - 177
JO - Journal of Clinical Epidemiology
JF - Journal of Clinical Epidemiology
ER -