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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
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A method for determining groups in cumulative incidence curves in competing risk data
Marta Sestelo1,2, Luís Meira-Machado3, Nora M Villanueva2
1CITMAga, 15782, Santiago de Compostela, Spain.
Biometrical Journal. Biometrische Zeitschrift
|May 22, 2024
Summary
This study introduces a new method for comparing cumulative incidence functions in competing risks analysis. The procedure tests curve equality, groups dissimilar curves, and determines group composition and number, showing good performance in simulations.
Area of Science:
- Biostatistics
- Survival Analysis
- Epidemiology
Background:
- Cumulative incidence functions (CIFs) are standard for estimating event probabilities with competing risks.
- Comparing CIFs is crucial but lacks established methodologies.
- Existing literature on comparing competing risk curves is limited.
Purpose of the Study:
- To develop a novel statistical procedure for comparing cumulative incidence functions under competing risks.
- To enable testing for equality of CIFs and grouping dissimilar curves.
- To automatically determine the number and composition of identified groups.
Main Methods:
- Proposed a new procedure for the analysis of competing risk data.
- Developed methods for testing CIF equality and for grouping curves.
- Included automatic selection for the number of groups and their composition.
Main Results:
- Simulation studies demonstrated the proposed method's good numerical stability for finite sample sizes.
- The procedure effectively tests CIF equality and facilitates grouping.
- The method successfully determines group composition and quantity.
Conclusions:
- The developed procedure offers a robust approach to comparing cumulative incidence functions in the presence of competing risks.
- This method addresses a significant gap in the literature for analyzing and grouping competing risk data.
- The technique is validated through simulations and illustrated with real-world data analysis.
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