Sensitivity Plots for Confounder Bias in the Single Mediator Model. (October 2013)

• Record Type: Journal Article
• Authors: Cox, Matthew G.; Kisbu-Sakarya, Yasemin; Miočević, Milica; MacKinnon, David P.
• Is Part Of: Evaluation review. Volume 37:Number 5(2013)
• Page Start: 405
• Page End: 431
• Publication Date: 2013-10
• Subjects: mediation -- indirect effects -- causal inference -- confounder bias -- sensitivity analysis; Evaluation research (Social action programs) -- Periodicals; 361.0072
• DOI: 10.1177/0193841X14524576
• Languages: English
• ISSNs: 0193-841X

Abstract

Background: Causal inference continues to be a critical aspect of evaluation research. Recent research in causal inference for statistical mediation has focused on addressing the sequential ignorability assumption; specifically, that there is no confounding between the mediator and the outcome variable. Objectives: This article compares and contrasts three different methods for assessing sensitivity to confounding and describes the graphical depiction of these methods. Design: Two types of data were used to fully examine the plots for sensitivity analysis. The first type was generated data from a single mediator model with a confounder influencing both the mediator and the outcome variable. The second was data from an actual intervention study. With both types of data, situations are examined where confounding has a large effect and a small effect. Subjects: The nonsimulated data were from a large intervention study to decrease intentions to use steroids among high school football players. We demonstrate one situation where confounding is likely and another situation where confounding is unlikely. Conclusions: We discuss how these methods could be implemented in future mediation studies as well as the limitations and future directions for these methods.