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Related Experiment Video

Updated: May 25, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

The use and interpretation of competing risks regression models.

James J Dignam¹, Qiang Zhang, Masha Kocherginsky

¹Department of Health Studies, The University of Chicago, Chicago, Illinois 60637, USA. jdignam@health.bsd.uchicago.edu

Clinical Cancer Research : an Official Journal of the American Association for Cancer Research

|January 28, 2012

Summary

Competing risks regression models are crucial in cancer studies. Choosing the right model is essential, as different approaches can yield substantially different covariate effects on failure events.

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Area of Science:

Biostatistics
Clinical Trials
Cancer Research

Background:

Clinical cancer studies frequently involve competing risks, where patients face multiple potential failure events.
Understanding covariate effects in the presence of competing risks is vital for accurate prognostic and predictive modeling.

Purpose of the Study:

To discuss the application and interpretation of commonly used competing risks regression models.
To evaluate how different modeling approaches influence the assessment of covariate effects on cause-specific failures.

Main Methods:

Utilized simulation studies to compare covariate effects on cause-specific hazards versus cumulative incidence.
Applied competing risks regression models to analyze data from a Radiation Therapy Oncology Group (RTOG) prostate cancer trial.

Main Results:

Simulation results demonstrated that model choice significantly impacts estimated covariate effects, depending on relationships with both primary and competing failure types.
A covariate affecting a competing event's hazard can appear significantly associated with a primary event's cumulative incidence, even without direct hazard influence.
Analysis of the RTOG prostate cancer data revealed age and tumor grade effects varied based on the chosen endpoint and regression model.

Conclusions:

The selection of an appropriate competing risks regression model is contingent upon the specific research question.
Careful consideration of the model's formulation is necessary to correctly interpret covariate effects in the context of competing risks.