Sensitivity Plots for Confounder Bias in the Single Mediator Model

被引:56
作者
Cox, Matthew G. [1 ]
Kisbu-Sakarya, Yasemin [2 ]
Miocevic, Milica [3 ]
MacKinnon, David P. [3 ]
机构
[1] Univ Texas MD Anderson Canc Ctr, Dept Behav Sci, Houston, TX 77230 USA
[2] Northwestern Univ, Inst Policy Res, Evanston, IL USA
[3] Arizona State Univ, Dept Psychol, Tempe, AZ 85287 USA
关键词
mediation; indirect effects; causal inference; confounder bias; sensitivity analysis; CAUSAL; IDENTIFICATION; INFERENCE;
D O I
10.1177/0193841X14524576
中图分类号
C [社会科学总论];
学科分类号
03 ; 0303 ;
摘要
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.
引用
收藏
页码:405 / 431
页数:27
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