Evaluating key predictors of breast cancer through survival: a comparison of AFT frailty models with LASSO, ridge, and elastic net regularization

Evaluating key predictors of breast cancer through survival: a comparison of AFT frailty models with LASSO, ridge, and elastic net regularization

Abstract Background Frailty models are extensively utilized in survival analysis to address unobserved heterogeneity among individuals. However, selecting the most robust model for survival prediction, especially in the context of high-dimensional data, continues to pose a challenge. This study eval...

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Bibliographic Details
Main Authors: Senyefia Bosson-Amedenu, Emmanuel Ayitey, Francis Ayiah-Mensah, Luyton Asare
Format: Article
Language:English
Published: BMC 2025-04-01
Series:BMC Cancer
Subjects:
Survival analysis
Accelerated Failure Time (AFT) frailty model
Extreme value distribution
Regularization (LASSO
Ridge
Elastic Net)
Online Access:https://doi.org/10.1186/s12885-025-14040-z
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https://doi.org/10.1186/s12885-025-14040-z

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