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Event-specific win ratios for inference with terminal and non-terminal events
Song Yang1, James Troendle1, Daewoo Pak2
1Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland, USA.
Statistics in Medicine
|November 24, 2021
Summary
This study introduces win ratios for analyzing semi-competing risks data, offering censoring-free estimates of treatment effects. These methods provide reliable confidence intervals and hypothesis tests for clinical trials.
Area of Science:
- Biostatistics
- Clinical Trial Analysis
- Survival Analysis
Background:
- Semi-competing risks data present challenges in clinical trials due to non-terminal and terminal events.
- Traditional methods may be biased by censoring, especially when censoring distributions differ between treatment groups.
Purpose of the Study:
- To derive and validate statistical methods for analyzing semi-competing risks data using event-specific win ratios.
- To develop censoring-free confidence intervals and hypothesis testing procedures for treatment effect evaluation.
Main Methods:
- Derivation of asymptotic distributions for event-specific win ratios under proportional hazards assumptions.
- Development of confidence intervals and testing procedures based on bivariate normal distributions.
- Simulation studies and real-world data analysis to assess performance and identify optimal transformations.
Main Results:
- Win ratios converge to hazard ratios under proportional hazards, providing censoring-free estimates.
- Proposed transformations of win ratios ensure good control of type I error rates and competitive power.
- Confidence intervals demonstrate good coverage probabilities.
Conclusions:
- The developed win ratio methods offer a robust and censoring-free approach for analyzing semi-competing risks in clinical trials.
- New tests for proportional hazards assumptions and equal hazard ratios are introduced.
- The methods are effectively illustrated using data from a heart failure clinical trial.
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