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Published on: October 23, 2020
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Sensitivity analysis for missing outcomes in time-to-event data with covariate adjustment.
Yue Zhao1, Benjamin R Saville2, Haibo Zhou3
1a Merck Research Laboratories , North Wales , Pennsylvania , USA.
Journal of Biopharmaceutical Statistics
|January 31, 2015
Summary
This study introduces covariate-adjusted sensitivity analyses for missing time-to-event data using multiple imputation (MI). The methods help assess the impact of missing outcomes in clinical trials.
Area of Science:
- Biostatistics
- Clinical Trials
- Survival Analysis
Background:
- Missing time-to-event outcomes pose challenges in clinical trial analysis.
- Sensitivity analyses are crucial for assessing the robustness of results to missing data assumptions.
Purpose of the Study:
- To propose and evaluate covariate-adjusted sensitivity analyses for time-to-event outcomes with missing data.
- To compare different multiple imputation (MI) methods within a sensitivity analysis framework.
Main Methods:
- Developed covariate-adjusted sensitivity analysis using multiple imputation (MI) for missing failure times.
- Compared multivariable Cox proportional hazards (PH) models and nonparametric analysis of covariance.
- Utilized Kaplan-Meier MI and covariate-adjusted/unadjusted PH MI methods.
Main Results:
- Demonstrated the application of proposed sensitivity analysis in a clinical trial example.
- Illustrated incorporation of covariance analysis methods into sensitivity analysis.
- Discussed assumptions, statistical issues, and features of various MI techniques.
Conclusions:
- The proposed covariate-adjusted sensitivity analysis provides a robust approach for handling missing time-to-event outcomes.
- Different MI strategies offer flexibility in assessing the impact of missing data under various assumptions.
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