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An R-Based Landscape Validation of a Competing Risk Model
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A review on statistical and machine learning competing risks methods.

Karla Monterrubio-Gómez1, Nathan Constantine-Cooke1,2, Catalina A Vallejos1,3

  • 1MRC Human Genetics Unit, University of Edinburgh, Edinburgh, UK.

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|February 13, 2024
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Summary

This study offers a guide to modern competing risks (CR) survival analysis methods, bridging statistical and machine learning techniques. It aims to increase the use of advanced CR survival models in practice by providing clear explanations and software examples.

Keywords:
competing risksrisk predictionsurvival analysistime-to-event data

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Area of Science:

  • Biostatistics
  • Machine Learning
  • Survival Analysis

Background:

  • Competing risks (CR) survival data modeling has seen advancements in both statistical and machine learning fields.
  • State-of-the-art methods offer improved predictive performance, high-dimensional data handling, and missing value imputation.
  • Widespread adoption of these modern CR survival approaches in applied research remains limited.

Purpose of the Study:

  • To facilitate the adoption of advanced competing risks survival methods in applied research.
  • To provide a unified compendium of CR survival techniques with consistent notation and interpretation.
  • To highlight available software tools and demonstrate their application using reproducible R vignettes.

Main Methods:

  • Compilation and synthesis of existing statistical and machine learning methods for competing risks survival analysis.
  • Development of a unified notation and interpretation framework for diverse CR survival approaches.
  • Illustrative examples using R vignettes to demonstrate software implementation and reproducibility.

Main Results:

  • A comprehensive overview of modern competing risks survival methods is presented.
  • The article provides practical guidance on software implementation and reproducible analysis.
  • Key considerations for benchmark studies, including performance metrics and reproducibility, are discussed.

Conclusions:

  • This work aims to bridge the gap between advanced competing risks survival methodologies and their practical application.
  • By offering a unified compendium and practical demonstrations, the study encourages wider use of sophisticated CR survival models.
  • Emphasis on performance metrics and reproducibility is crucial for reliable benchmarking in competing risks analysis.