GRAFT: Decoupling Ranking and Calibration for Survival Analysis

• URL: http://arxiv.org/abs/2602.07884v1
• Date: Sun, 08 Feb 2026 09:32:24 GMT
• Title: GRAFT: Decoupling Ranking and Calibration for Survival Analysis
• Authors: Mohammad Ashhad, Robert Hoehndorf, Ricardo Henao,
• Abstract summary: We propose GRAFT, a novel AFT model that decouples prognostics ranking from calibration.<n>GRAFT's hybrid architecture combines a linear AFT model with a non-linear residual neural network, and it also integrates gates for automatic, end-to-end feature selection.<n>In public benchmarks, GRAFT outperforms baselines in discrimination and calibration, while remaining robust and sparse in high-noise settings.
• Score: 12.400774220062303
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Survival analysis is complicated by censored data, high-dimensional features, and non-linear interactions. Classical models are interpretable but restrictive, while deep learning models are flexible but often non-interpretable and sensitive to noise. We propose GRAFT (Gated Residual Accelerated Failure Time), a novel AFT model that decouples prognostic ranking from calibration. GRAFT's hybrid architecture combines a linear AFT model with a non-linear residual neural network, and it also integrates stochastic gates for automatic, end-to-end feature selection. The model is trained by directly optimizing a differentiable, C-index-aligned ranking loss using stochastic conditional imputation from local Kaplan-Meier estimators. In public benchmarks, GRAFT outperforms baselines in discrimination and calibration, while remaining robust and sparse in high-noise settings.

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