Subgroup Discovery with the Cox Model
Published:Dec 25, 2025 05:00
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Analysis
This arXiv paper introduces a novel approach to subgroup discovery within the context of survival analysis using the Cox model. The authors identify limitations in existing quality functions for this specific problem and propose two new metrics: Expected Prediction Entropy (EPE) and Conditional Rank Statistics (CRS). The paper provides theoretical justification for these metrics and presents eight algorithms, with a primary algorithm leveraging both EPE and CRS. Empirical evaluations on synthetic and real-world datasets validate the theoretical findings, demonstrating the effectiveness of the proposed methods. The research contributes to the field by addressing a gap in subgroup discovery techniques tailored for survival analysis.
Key Takeaways
- •Introduces EPE and CRS as novel metrics for evaluating survival models.
- •Presents eight algorithms for Cox subgroup discovery.
- •Provides theoretical correctness results for the main algorithm.
Reference
“We study the problem of subgroup discovery for survival analysis, where the goal is to find an interpretable subset of the data on which a Cox model is highly accurate.”