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Sensitivity Analysis for Unmeasured Confounding in Causal Mediation Analysis With Survival Outcome.
Yi Guo1, Dan Chen1, Xinming Xu2
1Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China.
Statistics in Medicine
|April 16, 2026
Summary
This study introduces a new sensitivity analysis for mediation analysis in survival outcomes, addressing unmeasured confounding. The method enhances the reliability of mediation results in observational studies.
Area of Science:
- Epidemiology
- Biostatistics
- Health Research Methods
Background:
- Mediation analysis validity hinges on the no unmeasured confounding assumption.
- Existing sensitivity analyses for survival outcomes have limitations, including the rare outcome assumption and ignoring exposure-unmeasured confounding relationships.
Purpose of the Study:
- To develop a robust sensitivity analysis for mediation analysis in observational studies with survival outcomes.
- To assess robustness against both mediator-outcome and exposure-related unmeasured confounding.
Main Methods:
- Developed a novel sensitivity analysis approach by simulating an unmeasured confounder.
- Constructed the confounder's conditional distribution using sensitivity parameters (regression coefficients).
- Evaluated sensitivity by comparing mediation results before and after adjustment for the simulated confounder; developed a 3D visualization tool.
Main Results:
- The proposed method effectively assesses sensitivity to unmeasured confounding in mediation analysis.
- Validated methodology using simulated datasets.
- Applied to China Health and Nutrition Survey (CHNS) data to examine obesity, hypertension, and stroke mediation.
Conclusions:
- The new sensitivity analysis method improves the reliability of mediation findings in observational studies.
- The developed R package 'medsenssurv' facilitates practical implementation.
- The approach is applicable to various observational studies, including the obesity-stroke-hypertension example.
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