The Use of Traditional and Causal Estimators for Mediation Models with a Binary Outcome and Exposure-Mediator Interaction. Issue 3 (4th May 2021)

• Record Type: Journal Article
• Authors: Rijnhart, Judith J.M.; Valente, Matthew J.; MacKinnon, David P.; Twisk, Jos W.R.; Heymans, Martijn W.
• Is Part Of: Structural equation modeling. Volume 28:Issue 3(2021)
• Page Start: 345
• Page End: 355
• Publication Date: 2021-05-04
• Subjects: Mediation analysis -- potential outcomes -- interaction -- binary outcome; Multivariate analysis -- Periodicals; Social sciences -- Statistical methods -- Periodicals; 519.535
• DOI: 10.1080/10705511.2020.1811709
• Languages: English
• ISSNs: 1070-5511

Abstract

ABSTRACT: An important recent development in mediation analysis is the use of causal mediation analysis. Causal mediation analysis decomposes the total exposure effect into causal direct and indirect effects in the presence of exposure-mediator interaction. However, in practice, traditional mediation analysis is still most widely used. The aim of this paper is to demonstrate the similarities and differences between the causal and traditional estimators for mediation models with a continuous mediator, a binary outcome, and exposure-mediator interaction. A real-life data example, analytical comparisons, and a simulation study were used to demonstrate the similarities and differences between the traditional and causal estimators. The causal and traditional estimators provide similar indirect effect estimates, but different direct and total effect estimates. Traditional mediation analysis may only be used when conditional direct effect estimates are of interest. Causal mediation analysis is the generally preferred method as its casual effect estimates help unravel causal mechanisms.