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Published on: October 23, 2020
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Sensitivity analysis for unmeasured confounding in estimating the difference in restricted mean survival time
Seungjae Lee1,2, Ji Hoon Park2, Woojoo Lee1,3
1Institute of Health and Environment, Seoul National University, Seoul, South Korea.
Statistical Methods in Medical Research
|October 7, 2024
Summary
This study introduces a new sensitivity analysis to assess how unmeasured confounding affects estimates of restricted mean survival time (RMST) differences. The method provides a reliable range for RMST estimates, crucial for accurate survival analysis.
Area of Science:
- Biostatistics
- Survival Analysis
- Epidemiology
Background:
- Restricted mean survival time (RMST) difference is a key metric in survival analysis, often replacing the hazard ratio.
- Existing methods for RMST estimation adjust for measured confounders but often overlook unmeasured confounding.
- Assessing the impact of unmeasured confounding is critical for robust observational study findings.
Purpose of the Study:
- To develop a novel sensitivity analysis for RMST difference estimates, specifically addressing unmeasured confounding.
- To provide a practical method for quantifying the potential bias introduced by unmeasured confounders in RMST analyses.
- To enhance the reliability of RMST as an outcome measure in observational research.
Main Methods:
- Formulated sensitivity analysis as an optimization problem to determine the range of RMST differences under unmeasured confounding.
- Developed an efficient approach to calculate the sensitivity range and assess uncertainty using percentile bootstrap confidence intervals.
- Derived analytic results for key survival analysis scenarios.
Main Results:
- The proposed sensitivity analysis method effectively quantifies the impact of unmeasured confounding on RMST difference estimates.
- Simulation studies demonstrated the method's good performance across various statistical settings.
- The approach allows for efficient calculation of sensitivity ranges and uncertainty assessment.
Conclusions:
- The developed sensitivity analysis provides a valuable tool for evaluating the robustness of RMST difference estimates in the presence of unmeasured confounding.
- This method enhances the credibility of RMST as an alternative to hazard ratios in observational studies.
- The approach was successfully illustrated using data from the German Breast Cancer Study Group.
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