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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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Competing risks analysis of time-to-event data for cardiovascular surgeons
Steven J Staffa1, David Zurakowski1
1Department of Surgery, Boston Children's Hospital, Harvard Medical School, Boston, Mass; Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Harvard Medical School, Boston, Mass.
The Journal of Thoracic and Cardiovascular Surgery
|December 24, 2019
Summary
Competing risks analysis is crucial for accurate time-to-event data in cardiovascular surgery. This practical guide helps surgeons and biostatisticians avoid invalid conclusions from traditional survival analysis when outside events occur.
Area of Science:
- Cardiovascular Surgery
- Biostatistics
- Survival Analysis
Background:
- Traditional time-to-event analysis assumes noninformative censoring, which is often violated in clinical practice.
- The presence of competing risks, where an event other than the one of interest can occur, can lead to biased and invalid conclusions.
- Thoracic and cardiovascular surgeons frequently encounter scenarios where competing risks are present in their patient data.
Purpose of the Study:
- To provide a practical, step-by-step strategy for implementing competing risks analysis in time-to-event data analysis.
- To guide thoracic and cardiovascular surgeons in collaborating effectively with biostatisticians for accurate analysis.
- To highlight the limitations of traditional survival analysis in the presence of competing risks.
Main Methods:
- A five-step approach is outlined: (1) determine the need for competing risks analysis, (2) perform nonparametric analysis, (3) conduct model-based analysis, (4) interpret results, and (5) compare with traditional methods.
- The approach is demonstrated using a hypothetical cardiovascular surgery case involving mortality after the Fontan operation.
- Nonparametric, semiparametric (Fine-Gray model), and parametric methods are applied to analyze time-to-event data.
Main Results:
- Traditional Cox regression identified prematurity as a significant risk factor for mortality after stage 3 (HR, 1.26; P=.009).
- However, competing risks analysis using the Fine-Gray model, which accounted for mortality during stage 2, found prematurity not to be a significant predictor (HR, 1.07; P=.467).
- This illustrates how competing risks analysis can alter conclusions regarding risk factors.
Conclusions:
- Competing risks analysis provides a more accurate assessment of time-to-event data when competing events are present.
- The proposed step-by-step strategy makes competing risks analysis more accessible for cardiac surgeons and biostatisticians.
- Accurate interpretation of time-to-event data through competing risks analysis is essential for reliable clinical conclusions.
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