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Updated: Mar 27, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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An Introduction to Competing Risks in Epidemiology.

Henrik Toft Sørensen1,2, Erzsébet Horváth-Puhó1, Janet L Peacock1,2

  • 1Department of Clinical Epidemiology, Center for Population Medicine, Aarhus University Hospital and Aarhus University, Aarhus, Denmark.

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|March 25, 2026
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Summary
This summary is machine-generated.

Competing risks, where one event impacts another, require careful analysis to avoid biased estimates. This review guides researchers in selecting appropriate methods like the Aalen-Johansen estimator or cause-specific hazard models for accurate competing risk data interpretation.

Keywords:
Aalen-Johansen estimatorcause-specific hazard modelcompeting risksepidemiologyfine-gray subdistribution hazard model

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Clinical Research

Background:

  • Competing risks occur when multiple mutually exclusive events are possible, influencing each other's probability.
  • Ignoring competing risks in research can lead to significantly biased statistical estimates.
  • Accurate analysis of competing risks is crucial for valid epidemiological and clinical studies.

Purpose of the Study:

  • To outline and compare key methods for analyzing competing risk data.
  • To clarify the assumptions, interpretations, and relevance of different analytical approaches.
  • To guide researchers in selecting appropriate strategies for analyzing competing risk data.

Main Methods:

  • Aalen-Johansen estimator for non-parametric estimation of cumulative incidence.
  • Cause-specific hazard models for etiologic research, treating competing events as censored.
  • Fine-Gray subdistribution hazard models for clinically interpretable absolute risk estimation.

Main Results:

  • The Aalen-Johansen estimator is an alternative to the Kaplan-Meier estimator when competing events are present.
  • Cause-specific hazard models estimate instantaneous risk for specific events.
  • The Fine-Gray model directly models cumulative incidence, providing a measure of absolute risk.

Conclusions:

  • Selecting the right competing risk analysis method is essential for avoiding bias and ensuring meaningful interpretation.
  • Methods like Aalen-Johansen, cause-specific hazard, and Fine-Gray models offer different insights depending on the research question.
  • Understanding the nuances of each method, including composite endpoints, is vital for robust clinical and epidemiological research.