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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
The mstate package for estimation and prediction in non- and semi-parametric multi-state and competing risks models
Liesbeth C de Wreede1, Marta Fiocco, Hein Putter
1Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands. l.c.de_wreede@lumc.nl
Computer Methods and Programs in Biomedicine
|March 16, 2010
Summary
This study introduces the R package "mstate" for multi-state model analysis in survival analysis. It offers user-friendly tools for data preparation, flexible estimation, and dynamic predictions, enhancing biomedical applications.
Area of Science:
- Biostatistics
- Survival Analysis
- Computational Biology
Background:
- Multi-state models are valuable in survival analysis but underutilized due to limited software.
- Existing software lacks flexibility and user-friendliness for complex biomedical applications.
Purpose of the Study:
- Introduce the R package 'mstate' to address the software gap for multi-state model analysis.
- Provide tools for data preparation, estimation, and prediction in non- and semi-parametric multi-state models.
Main Methods:
- Developed the 'mstate' package in R for comprehensive multi-state model analysis.
- Implemented functions for data preparation, Cox regression with various covariate effects, and patient-specific transition intensity estimation.
- Included methods for dynamic prediction probabilities and standard error calculations (Greenwood and Aalen-type).
Main Results:
- The 'mstate' package facilitates all stages of multi-state model analysis, including competing risks.
- New asymptotic results for cumulative hazard functions and recursive formulas for standard error calculations are presented.
- Demonstrated the package's utility with a liver cirrhosis survival data analysis.
Conclusions:
- The 'mstate' R package significantly enhances the accessibility and application of multi-state models in biomedical research.
- Offers a flexible, user-friendly solution for complex survival analyses, including dynamic predictions and competing risks.
- The package and its underlying mathematical theory provide a robust framework for advanced survival analysis.
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