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New Cox model method enhances variable selection for survival analysis

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new method for variable selection in survival analysis, building upon Cox's proportional hazards model. This approach utilizes a square-root transformation of the partial likelihood to make the selection of the regularization parameter independent of the unknown baseline hazard and censoring mechanism. The proposed criterion combines aspects of information criteria like BIC and penalized regression methods such as the lasso, aiming to improve performance on both simulated and real-world data, particularly in support recovery applications. AI

IMPACT Introduces an improved statistical method for survival analysis, potentially impacting fields that rely on time-to-event data modeling.

RANK_REASON The cluster contains a new academic paper detailing a novel statistical method. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Cox model method enhances variable selection for survival analysis

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The cluster contains a new academic paper detailing a novel statistical method. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Maxime van Cutsem, Sylvain Sardy ·

    Survival of the fittest Cox model: Pivotal variable selection for time-to-event data

    arXiv:2510.19374v2 Announce Type: replace Abstract: We revisit Cox's proportional hazards model to improve variable selection in survival analysis. A square-root transformation of the partial likelihood renders the selection of the regularization parameter pivotal, free of the un…

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