Sensitivity plots for confounder bias in the single mediator model

Area of Science:

• Statistics
• Causal Inference
• Evaluation Research

Background:

• Causal inference is crucial for evaluation research.
• Statistical mediation analysis often assumes sequential ignorability, meaning no confounding between mediator and outcome.
• Addressing confounding is key to robust mediation findings.

Purpose of the Study:

• To compare and contrast three methods for assessing sensitivity to confounding in mediation analysis.
• To illustrate these methods using graphical depictions.
• To enhance the understanding of confounding effects in causal inference.

Main Methods:

• Utilized both generated data from a single mediator model and real-world data from an intervention study.
• Examined scenarios with both large and small confounding effects.
• Applied methods to data from an intervention study on steroid use intentions among high school athletes.

Main Results:

• Demonstrated the application of sensitivity analysis methods to different datasets.
• Illustrated how confounding can impact mediation analysis results under varying conditions.
• Provided graphical representations to aid in the interpretation of confounding effects.

Conclusions:

• Discussed the practical implementation of these sensitivity analysis methods in future mediation studies.
• Highlighted the limitations of the current methods.
• Suggested future research directions for improving causal inference in mediation analysis.