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Related Experiment Video

Updated: Jun 1, 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

Understanding competing risks: a simulation point of view.

Arthur Allignol¹, Martin Schumacher, Christoph Wanner

¹Freiburg Center for Data Analysis and Modeling, University of Freiburg, Germany. arthur.allignol@fdm.uni-freiburg.de

BMC Medical Research Methodology

|June 7, 2011

Summary

This summary is machine-generated.

Competing risks analysis requires examining all hazards, not just one. This simulation approach clarifies event interpretation and aids study planning for complex time-to-event data.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

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Last Updated: Jun 1, 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

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

Biostatistics
Epidemiology
Clinical Research

Background:

Competing risks methodology enables event-specific analysis of composite time-to-event endpoints.
A critical aspect of competing risks is the presence of multiple hazards, one for each competing risk.
This multiplicity of hazards is often inadequately addressed in applied research.

Purpose of the Study:

To advocate for a simulation-based perspective for understanding competing risks.
To illustrate the interpretation of competing risks using empirical simulations.
To provide a method that bypasses identifiability and plausibility issues associated with latent failure time approaches.

Main Methods:

Conceptualizing hazards as momentary event forces that jointly determine event time and type.
Utilizing 'empirical simulations' with data from a cardiovascular events in diabetes study.
Applying a simulation algorithm to real-world data for study planning.

Main Results:

Empirical simulations demonstrated the proof of concept for the simulation approach.
Manipulating baseline hazards and treatment effects highlighted scenarios needing careful interpretation.
The simulation viewpoint was shown to aid in interpreting complex competing risks scenarios.

Conclusions:

Emphasize that the number of hazards equals the number of competing risks.
Advocate for the analysis of all hazards, including baseline hazard estimation.
Stress the importance of incorporating these considerations into study planning.