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Causal Inference for First Non-Fatal Events With the Competing Risk of Death: A Principal Stratification Approach
1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Statistics in Medicine
|November 18, 2025
Summary
This study introduces a new statistical model to accurately measure a treatment's direct effect on preventing nonfatal events, even when death is a competing risk. The proportional principal stratum hazards model provides a more precise understanding of treatment efficacy in clinical trials.
Area of Science:
- Biostatistics
- Clinical Trials Methodology
- Survival Analysis
Background:
- Active treatments can directly or indirectly affect nonfatal events in clinical trials with competing risks.
- Standard Cox models may misestimate direct treatment effects on nonfatal events due to the competing risk of death.
Purpose of the Study:
- To develop a statistical framework to isolate and estimate the direct effect of an active treatment on the underlying nonfatal event process.
- Introduce the proportional principal stratum hazards model for accurate estimation in the presence of competing risks.
Main Methods:
- Utilized the principal stratification framework to define principal stratum hazards.
- Introduced the proportional principal stratum hazards model to estimate the principal stratum hazard ratio.
- Employed a shared frailty model for probabilistic identification of principal stratum membership.
Main Results:
- The proposed model estimates the principal stratum hazard ratio, reflecting the direct treatment effect on the nonfatal event process.
- This ratio simplifies to the standard hazard ratio when death is not a competing risk.
- Simulation studies confirmed the reliability of the developed estimators.
Conclusions:
- The proportional principal stratum hazards model offers a robust method for assessing the direct impact of treatments on morbidity in the presence of mortality.
- This approach enhances the interpretation of treatment effects in complex clinical trial settings, as demonstrated in the Carvedilol trial.
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