Premium
Understanding of interaction (subgroup) analysis in clinical trials
Author(s) -
Brankovic Milos,
Kardys Isabella,
Steyerberg Ewout W.,
Lemeshow Stanley,
Markovic Maja,
Rizopoulos Dimitris,
Boersma Eric
Publication year - 2019
Publication title -
european journal of clinical investigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.164
H-Index - 107
eISSN - 1365-2362
pISSN - 0014-2972
DOI - 10.1111/eci.13145
Subject(s) - subgroup analysis , randomized controlled trial , clarity , clinical trial , medicine , psychology , meta analysis , family medicine , pathology , biochemistry , chemistry
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.