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Published on: October 23, 2020
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While-alive regression analysis of composite survival endpoints
Xi Fang1,2, Hajime Uno3, Fan Li1,2
1Department of Biostatistics, Yale School of Public Health, 60 College St, New Haven, CT 06510, United States.
Biostatistics (Oxford, England)
|December 24, 2025
Summary
This study introduces a new regression framework for analyzing composite survival outcomes, particularly when a terminal event is present. The method enhances statistical power by modeling time-varying associations in while-alive measures for clinical trial data.
Area of Science:
- Biostatistics
- Clinical Trials
- Survival Analysis
Background:
- Composite endpoints are common in clinical trials to increase event rates and statistical power.
- The while-alive cumulative frequency measure is a valuable tool for composite survival outcomes with terminal events, linking average event rates to survival time.
- Existing non-parametric methods for two-sample comparisons are limited, with a lack of regression approaches for time-varying effects in while-alive measures.
Purpose of the Study:
- To develop a novel regression framework for exposure-weighted while-alive measures in composite survival outcomes, specifically addressing terminal component events.
- To model time-varying associations between covariates and the generalized while-alive loss rate for all component events.
- To provide a method applicable to both independent and clustered data.
Main Methods:
- Developed a regression framework utilizing splines to model time-varying associations.
- Derived asymptotic properties of the regression estimator for both independent and cluster-correlated data.
- Validated the method through simulations and application to data from two randomized clinical trials.
Main Results:
- The proposed regression framework effectively models time-varying associations in while-alive measures for composite survival outcomes with terminal events.
- The method demonstrates robust performance in both independent and clustered data settings, as confirmed by simulations.
- The regression approach was successfully applied to analyze data from two clinical trials, showing its practical utility.
Conclusions:
- The developed regression framework provides a powerful tool for analyzing composite survival outcomes with terminal events using while-alive measures.
- This approach addresses the gap in regression methods for time-varying effects in such outcomes, enhancing statistical analysis in clinical trials.
- The implementation in the WAreg R package facilitates the application of these advanced statistical methods by researchers.
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