Milos Brankovic1,2, Isabella Kardys1, Ewout W Steyerberg3, Stanley Lemeshow4, Maja Markovic5, Dimitris Rizopoulos6, Eric Boersma1. 1. Clinical Epidemiology Unit, Department of Cardiology, Erasmus Medical Center, Rotterdam, The Netherlands. 2. School of Medicine, University of Belgrade, Belgrade, Serbia. 3. Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands. 4. Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, Ohio. 5. Department of Child and Adolescent Psychiatry, Erasmus Medical Center, Rotterdam, The Netherlands. 6. Department of Biostatistics, Erasmus Medical Center, Rotterdam, The Netherlands.
Abstract
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.
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.
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