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Related Experiment Video

Updated: Jul 14, 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

Competing risk analysis using R: an easy guide for clinicians.

L Scrucca¹, A Santucci, F Aversa

¹Statistics Section, Department of Economy Finance and Statistics, University of Perugia, Perugia, Italy.

Bone Marrow Transplantation

|June 15, 2007

Summary

This study introduces an R add-on package for advanced competing risk analysis, essential for medical research. It guides clinicians on using this tool for analyzing patient data, particularly after haematopoietic stem cell transplantation.

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Area of Science:

Biostatistics
Medical Informatics
Hematology

Background:

Quantitative analysis is increasingly vital in medical research, necessitating collaboration between statisticians and physicians.
Accessible statistical software enables clinicians to perform analyses, but often lacks advanced capabilities like competing risk analysis.
Competing risk analysis is crucial for understanding outcomes in complex medical scenarios.

Purpose of the Study:

To recommend and provide instructions for an R add-on package enabling in-depth competing risk analysis.
To demonstrate the utility of this package using a real-world dataset of acute leukaemia patients undergoing haematopoietic stem cell transplantation.

Main Methods:

Development and recommendation of a specialized R statistical software add-on package.
Detailed instructions for downloading and installing the R package from the internet.
Application of the package to analyze a sample dataset.

Main Results:

Successful implementation of the R add-on package for competing risk analysis.
Demonstration of the package's effectiveness in analyzing patient data from haematopoietic stem cell transplantation.
Facilitation of more robust statistical analysis for clinicians.

Conclusions:

The recommended R add-on package significantly enhances the capability for competing risk analysis in medical research.
This tool empowers clinicians to conduct sophisticated analyses, improving the interpretation of patient outcomes.
Adoption of such tools is key to advancing quantitative medical research and patient care.