The C-index illusion: discrimination without calibration in published survival models

arXiv:2607.19526 · cs.LG · Submitted 2026-07-21 · Read on arXiv

cs.LG

Submitted: 2026-07-21

Updated: 2026-09-11

Comments: 34 pages, 6 figures, 18 tables

Code: https://github.com/rafa-rodriguess/c_index_illusion_pub

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Calibration — 56× in this paper · explained in 7 episodes
• Ablation — 20× in this paper · explained in 1 episode
• Copula — 12× in this paper · explained in 2 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Calibration Failure — 4× in this paper · explained in 2 episodes
• AUC — 3× in this paper · explained in 2 episodes
• Generative AI — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Fidelity — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Commentary — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Expected Calibration Error — 1× in this paper
• Failure Modes — 1× in this paper · explained in 3 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• Competing Risks: Impact on Risk Estimation and Algorithmic Fairness
• Overcoming Dependent Censoring in the Evaluation of Survival Models
• Position: Stop Chasing the C-index when Evaluating Survival Analysis Models
• Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks