Interpretable Mean Residual Life Framework for Survival Rule Induction from Right-Censored Data: Methodology with Biostatistical Applications

Survival-rule induction is commonly guided by log-rank separation, whereas some prognostic questions target conditional future lifetime. We propose a directional finite-horizon mean residual life (MRL) criterion for survival-rule induction under right censoring, combining normalized subgroup support with a survival-weighted restricted-MRL discrepancy. The framework includes favorable and adverse objectives, training-only horizon selection and tuning, overlapping-rule prediction, and finite-candidate plug-in consistency. Across nine simulation scenarios (200 replications each), MRL recovered the true subgroup partition more accurately under delayed benefit, crossing hazards, delayed benefit with 60% censoring, and small-sample crossing; log-rank was stronger under proportional hazards, early-only effects, rare subgroups, and the adverse stress test. In repeated nested analyses, Cox proportional hazards achieved the lowest mean integrated Brier score (IBS) in WHAS100 (0.1797) and malignant melanoma (0.1300); controlled log-rank also yielded lower IBS than MRL (0.1926 vs. 0.2038 and 0.1358 vs. 0.1404, respectively). MRL nevertheless identified different conditional-lifetime structures. These results position MRL-guided rules as an estimand-specific complement to hazard-oriented methods rather than a universally superior predictor.