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Updated: Jun 14, 2026
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
An R function to non-parametric and piecewise analysis of competing risks survival data
Thomas Filleron1, Agnès Laplanche, Jean-Marie Boher
1Institut Claudius Regaud, 20-24 rue du Pont Saint Pierre, 31052 Toulouse, France. filleron.thomas@claudiusregaud.fr
Computer Methods and Programs in Biomedicine
|March 27, 2010
Summary
This study introduces a new R function for non-parametric analysis of competing risks data, simplifying the estimation and comparison of cumulative incidence functions for researchers in oncology and other fields.
Area of Science:
- Biostatistics
- Survival Analysis
- Medical Informatics
Background:
- Competing risks are common in survival data analysis.
- Estimating cumulative incidence functions requires specific analytical methods.
- Existing R packages offer some tools but can be limited.
Purpose of the Study:
- To introduce a novel R add-on function for the cmprsk package.
- To provide a unified program for non-parametric analysis of competing risk data.
- To facilitate the estimation and comparison of cumulative incidence functions.
Main Methods:
- Development of an R add-on function for the cmprsk package.
- Implementation of non-parametric analysis for competing risk data.
- Integration of three commonly used analytical methods into a single function.
Main Results:
- The proposed function offers a streamlined approach to analyzing competing risks.
- Illustrative examples in oncology demonstrate the function's utility.
- Comparison with existing cmprsk and survival package estimates is provided.
Conclusions:
- The new R function enhances the analysis of competing risks data.
- It offers a practical tool for researchers, particularly in oncology.
- The function simplifies complex statistical analyses in survival data.
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