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We introduce a framework to build a survival/risk bump hunting model with acensored time-to-event response. Our Survival Bump Hunting (SBH) method isbased on a recursive peeling procedure that uses a specific survival peelingcriterion derived from non/semi-parametric statistics such as thehazards-ratio, the log-rank test or the Nelson-Aalen estimator. To optimize thetuning parameter of the model and validate it, we introduce an objectivefunction based on survival or prediction-error statistics, such as the log-ranktest and the concordance error rate. We also describe two alternativecross-validation techniques adapted to the joint task of decision-rule makingby recursive peeling and survival estimation. Numerical analyses show theimportance of replicated cross-validation and the differences between criteriaand techniques in both low and high-dimensional settings. Although severalnon-parametric survival models exist, none addresses the problem of directlyidentifying local extrema. We show how SBH efficiently estimates extremesurvival/risk subgroups unlike other models. This provides an insight into thebehavior of commonly used models and suggests alternatives to be adopted inpractice. Finally, our SBH framework was applied to a clinical dataset. In it,we identified subsets of patients characterized by clinical and demographiccovariates with a distinct extreme survival outcome, for which tailored medicalinterventions could be made. An R package `PRIMsrc` is available on CRAN andGitHub.

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