Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods

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 several non-parametric survival models exist, none addresses the problem of directly identifying local extrema. We show how SBH efficiently estimates extreme survival/risk subgroups unlike other models. This provides an insight into the behavior of commonly used models and suggests alternatives to be adopted in practice. Finally, our SBH framework was applied to a clinical dataset. In it, we identified subsets of patients characterized by clinical and demographic covariates with a distinct extreme survival outcome, for which tailored medical interventions could be made. An R package `PRIMsrc` is available on CRAN and GitHub.