Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods
Jean-Eudes Dazard, Michael Choe, Michael LeBlanc, J. Sunil Rao

TL;DR
This paper presents a novel recursive peeling framework for survival bump hunting that effectively identifies extreme risk subgroups in censored data, with robust validation techniques and practical clinical applications.
Contribution
It introduces a survival bump hunting method using recursive peeling with tailored cross-validation and objective functions, addressing local extrema detection in survival analysis.
Findings
Effective identification of high and low survival risk subgroups
Demonstrated importance of replicated cross-validation in survival models
Applied framework to clinical data revealing distinct patient risk groups
Abstract
We introduce a framework to build a survival/risk bump hunting model with a censored time-to-event response. Our Survival Bump Hunting (SBH) method is based on a recursive peeling procedure that uses a specific survival peeling criterion derived from non/semi-parametric statistics such as the hazards-ratio, the log-rank test or the Nelson-Aalen estimator. To optimize the tuning parameter of the model and validate it, we introduce an objective function based on survival or prediction-error statistics, such as the log-rank test and the concordance error rate. We also describe two alternative cross-validation techniques adapted to the joint task of decision-rule making by recursive peeling and survival estimation. Numerical analyses show the importance of replicated cross-validation and the differences between criteria and techniques in both low and high-dimensional settings. Although…
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Taxonomy
TopicsStatistical Methods and Inference · Statistical Methods and Bayesian Inference · Advanced Causal Inference Techniques
