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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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Practical Recommendations on Quantifying and Interpreting Treatment Effects in the Presence of Terminal Competing
Zachary R McCaw1, Brian Lee Claggett2, Lu Tian3
1Google, Mountain View, California.
JAMA Cardiology
|December 1, 2021
Summary
Handling competing risks in clinical trials is crucial. This study reviews methods for analyzing time-to-event data when terminal events like death occur, offering guidance for researchers.
Area of Science:
- Biostatistics
- Clinical Trials
- Survival Analysis
Background:
- Time-to-event endpoints are key in comparative trials.
- Terminal events (death, discontinuation) can obscure outcomes.
- Dissimilar terminal event profiles complicate analysis.
Purpose of the Study:
- To review and compare methods for handling competing risks in clinical trials.
- To provide guidance on selecting appropriate methods for specific scenarios.
- To address methods for both desirable and undesirable outcomes.
Main Methods:
- Review of common competing risk methods: censoring terminal events, cumulative incidence functions, and event-free survival.
- Analysis of advantages and disadvantages of each approach.
- Evaluation of interpretability and applicability in practice.
Main Results:
- Censoring terminal events can lead to uninterpretable results and inappropriate summary measures (cause-specific hazard ratio).
- Cumulative incidence functions are suitable for desirable outcomes but may have interpretation issues for undesirable ones.
- Event-free survival offers a clinically interpretable method that naturally accounts for differing terminal event rates.
Conclusions:
- Understanding the nuances of competing risk methods is essential for accurate clinical trial analysis.
- The choice of method depends on the specific outcome (desirable/undesirable) and study context.
- Event-free survival is a robust method for undesirable outcomes with dissimilar terminal event profiles.
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