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Effect modification, interaction and mediation: an overview of theoretical insights for clinical investigators

Authors Corraini P, Olsen M, Pedersen L, Dekkers OM, Vandenbroucke JP

Received 8 December 2016

Accepted for publication 28 April 2017

Published 8 June 2017 Volume 2017:9 Pages 331—338

DOI https://doi.org/10.2147/CLEP.S129728

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Akshita Wason

Peer reviewer comments 3

Editor who approved publication: Professor Irene Petersen


Priscila Corraini,1 Morten Olsen,1 Lars Pedersen,1 Olaf M Dekkers,1,2 Jan P Vandenbroucke1–3

1Department of Clinical Epidemiology, Institute of Clinical Medicine, Aarhus University Hospital, Aarhus, Denmark; 2Leiden University Medical Center, Leiden, the Netherlands; 3Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK

Abstract: We revisited the three interrelated epidemiological concepts of effect modification, interaction and mediation for clinical investigators and examined their applicability when using research databases. The standard methods that are available to assess interaction, effect modification and mediation are explained and exemplified. For each concept, we first give a simple “best-case” example from a randomized controlled trial, followed by a structurally similar example from an observational study using research databases. Our explanation of the examples is based on recent theoretical developments and insights in the context of large health care databases. Terminology is sometimes ambiguous for what constitutes effect modification and interaction. The strong assumptions underlying the assessment of interaction, and particularly mediation, require clinicians and epidemiologists to take extra care when conducting observational studies in the context of health care databases. These strong assumptions may limit the applicability of interaction and mediation assessments, at least until the biases and limitations of these assessments when using large research databases are clarified.

Keywords: methods, epidemiology, effect modifiers, stratified analyses, health care administrative claims

Corrigendum for this paper has been published

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