Time-to-event prediction for grouped variables using Exclusive Lasso

• URL: http://arxiv.org/abs/2504.01520v1
• Date: Wed, 02 Apr 2025 09:07:05 GMT
• Title: Time-to-event prediction for grouped variables using Exclusive Lasso
• Authors: Dayasri Ravi, Andreas Groll,
• Abstract summary: We propose utilizing Exclusive Lasso regularization in place of standard Lasso penalization.<n>We apply our methodology to a real-life cancer dataset, demonstrating enhanced survival prediction performance compared to the conventional Cox regression model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of high-dimensional genomic data and clinical data into time-to-event prediction models has gained significant attention due to the growing availability of these datasets. Traditionally, a Cox regression model is employed, concatenating various covariate types linearly. Given that much of the data may be redundant or irrelevant, feature selection through penalization is often desirable. A notable characteristic of these datasets is their organization into blocks of distinct data types, such as methylation and clinical predictors, which requires selecting a subset of covariates from each group due to high intra-group correlations. For this reason, we propose utilizing Exclusive Lasso regularization in place of standard Lasso penalization. We apply our methodology to a real-life cancer dataset, demonstrating enhanced survival prediction performance compared to the conventional Cox regression model.

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