Robust variable selection methods with Cox model-a selective practical benchmark study

Yunwei Zhang; Samuel Muller

doi:10.1093/bib/bbae508

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Robust variable selection methods with Cox model-a selective practical benchmark study

Journal article

Open access

Peer reviewed

Robust variable selection methods with Cox model-a selective practical benchmark study

Yunwei Zhang and Samuel Muller

Briefings in bioinformatics, Vol.25(6), bbae508

2024

DOI: https://doi.org/10.1093/bib/bbae508

PMID: 39400113

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Abstract

Benchmarking

Humans

Proportional Hazards Models

With the advancement of biological and medical techniques, we can now obtain large amounts of high-dimensional omics data with censored survival information. This presents challenges in method development across various domains, particularly in variable selection. Given the inherently skewed distribution of the survival time outcome variable, robust variable selection methods offer potential solutions. Recently, there has been a focus on extending robust variable selection methods from linear regression models to survival models. However, despite these developments, robust methods are currently rarely used in practical applications, possibly due to a limited appreciation of their overall good performance. To address this gap, we conduct a selective review comparing the variable selection performance of twelve robust and non-robust penalised Cox models. Our study reveals the intricate relationship among covariates, survival outcomes, and modeling approaches, demonstrating how subtle variations can significantly impact the performance of methods considered. Based on our empirical research, we recommend the use of robust Cox models for variable selection in practice based on their superior performance in presence of outliers while maintaining good efficiency and accuracy when there are no outliers. This study provides valuable insights for method development and application, contributing to a better understanding of the relationship between correlated covariates and censored outcomes.

Details

Title: Robust variable selection methods with Cox model-a selective practical benchmark study
Authors/Creators: Yunwei Zhang - School of Mathematics and Statistics, The University of Sydney, F07 Eastern Ave, Camperdown NSW 2050, Australia
Samuel Muller - The University of Sydney
Publication Details: Briefings in bioinformatics, Vol.25(6), bbae508
Publisher: Oxford University Press
Number of pages: 9
Grant note: 230101908 / Australian Research Council Discovery Project
Identifiers: 991005707256107891
Murdoch Affiliation: School of Mathematics, Statistics, Chemistry and Physics
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration
Citation topics: 9 Mathematics; 9.92 Statistical Methods; 9.92.220 Robust Estimation
Web Of Science research areas: Biochemical Research Methods; Mathematical & Computational Biology
ESI research areas: Computer Science