Sensitivity Analysis for Unmeasured Confounding in Causal Mediation Analysis With Survival Outcome

The validity of mediation analysis critically depends on the assumption of no unmeasured confounding, which is typically evaluated through sensitivity analysis. However, existing sensitivity analysis methods for mediation analysis with survival outcomes often rely on the rare outcome assumption and neglect the relationship between exposure and unmeasured confounding. To address this challenge, we developed a sensitivity analysis approach to assess the robustness of mediation results. Specifically, it can assess sensitivity to both mediator-outcome confounding and confounding involving the exposure in observational studies. Our method innovatively generates a simulated unmeasured confounder from its conditional distribution, constructed through sensitivity parameters such as regression coefficients linking the outcome, mediator, and exposure to the unmeasured confounder. Then the sensitivity of mediation analysis can be evaluated by comparing the results before and after adjusting for the simulated unmeasured confounder. A three-dimensional visualization tool was developed to visualize the sensitivity of mediation analysis results. We validated our methodology on simulated datasets and further applied it to a real dataset from the China Health and Nutrition Survey (CHNS) to investigate the relationship between obesity and stroke mediated by hypertension. An R package "medsenssurv" has also been developed to facilitate implementation of our proposed method ( https://github.com/Guo-yi-y/medsenssurv).