How do unobserved confounding mediators and measurement error impact estimated mediation effects and corresponding...

Area of Science:

• Psychology
• Statistics
• Causal Inference

Background:

• Empirical studies frequently involve complex causal mechanisms with multiple mediators.
• Simple mediation models are common but may omit relevant mediators, leading to confounding.
• Measurement error in observed variables can also bias effect estimates.

Purpose of the Study:

• To investigate the impact of omitting unobserved mediators and measurement error on mediation analysis.
• To develop a method for assessing the robustness of mediation inferences under these conditions.
• To provide a practical tool for researchers to evaluate potential biases.

Main Methods:

• Exploration of bias introduced by unobserved confounding mediators and measurement error.
• Development of a correlation-based sensitivity analysis, extending Frank's impact threshold for a confounding variable (ITCV).
• Creation of an R package, ConMed, for practical application.

Main Results:

• Omission of unobserved mediators and measurement error can significantly bias direct and indirect effects.
• The proposed sensitivity analysis quantifies the potential impact of omitted variables and measurement error.
• The ConMed package facilitates the assessment of mediation inference robustness.

Conclusions:

• Mediation analyses are vulnerable to unobserved confounding and measurement error.
• Sensitivity analysis is crucial for robust causal inference in mediation studies.
• The ConMed package offers a valuable resource for researchers to address these threats.