# Position: Stop Chasing the C-index when Evaluating Survival Analysis Models

arXiv: https://arxiv.org/abs/2506.02075 · HTML: https://mlsanity.com/paper/2506.02075/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 147 references that are in the map.
- First posted on arXiv 2 Jun 2025.

## Builds on (most cited first, 30 of 40)

- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [MOTOR: A Time-To-Event Foundation Model For Structured Medical Records](https://mlsanity.com/paper/2301.03150/) (2023)
- [Effective Ways to Build and Evaluate Individual Survival Distributions](https://mlsanity.com/paper/1811.11347/) (2018)
- [Multimodal Prototyping for cancer survival prediction](https://mlsanity.com/paper/2407.00224/) (2024)
- [Survival Regression with Proper Scoring Rules and Monotonic Neural Networks](https://mlsanity.com/paper/2103.14755/) (2021)
- [SurvivalGAN: Generating Time-to-Event Data for Survival Analysis](https://mlsanity.com/paper/2302.12749/) (2023)
- [An Effective Meaningful Way to Evaluate Survival Models](https://mlsanity.com/paper/2306.01196/) (2023)
- [Fairness in Survival Analysis with Distributionally Robust Optimization](https://mlsanity.com/paper/2409.10538/) (2024)
- [Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis](https://mlsanity.com/paper/2404.02394/) (2024)
- [Copula-Based Deep Survival Models for Dependent Censoring](https://mlsanity.com/paper/2306.11912/) (2023)
- [Conformalized Survival Distributions: A Generic Post-Process to Increase Calibration](https://mlsanity.com/paper/2405.07374/) (2024)
- [Orthogonal Survival Learners for Estimating Heterogeneous Treatment Effects from Time-to-Event Data](https://mlsanity.com/paper/2505.13072/) (2025)
- [Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee](https://mlsanity.com/paper/2206.10477/) (2022)
- [Survival Mixture Density Networks](https://mlsanity.com/paper/2208.10759/) (2022)
- [Inverse-Weighted Survival Games](https://mlsanity.com/paper/2111.08175/) (2021)
- [Federated Survival Forests](https://mlsanity.com/paper/2302.02807/) (2023)
- [Deep Copula-Based Survival Analysis for Dependent Censoring with Identifiability Guarantees](https://mlsanity.com/paper/2312.15566/) (2023)
- [Interpretable machine learning for time-to-event prediction in medicine and healthcare](https://mlsanity.com/paper/2303.09817/) (2023)
- [Dynamical Survival Analysis with Controlled Latent States](https://mlsanity.com/paper/2401.17077/) (2024)
- [HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks](https://mlsanity.com/paper/2410.15180/) (2024)
- [Neural Frailty Machine: Beyond proportional hazard assumption in neural survival regressions](https://mlsanity.com/paper/2303.10358/) (2023)
- [Interpretable Survival Analysis for Heart Failure Risk Prediction](https://mlsanity.com/paper/2310.15472/) (2023)
- [Survival Models: Proper Scoring Rule and Stochastic Optimization with Competing Risks](https://mlsanity.com/paper/2410.16765/) (2024)
- [Proximal Survival Analysis to Handle Dependent Right Censoring](https://mlsanity.com/paper/2208.07014/) (2022)
- [Understanding the Impact of Competing Events on Heterogeneous Treatment Effect Estimation from Time-to-Event Data](https://mlsanity.com/paper/2302.12718/) (2023)
- [An evidential time-to-event prediction model based on Gaussian random fuzzy numbers](https://mlsanity.com/paper/2406.13487/) (2024)
- [Toward Conditional Distribution Calibration in Survival Prediction](https://mlsanity.com/paper/2410.20579/) (2024)
- [NeuralSurv: Deep Survival Analysis with Bayesian Uncertainty Quantification](https://mlsanity.com/paper/2505.11054/) (2025)
- [Transformer-based Time-to-Event Prediction for Chronic Kidney Disease Deterioration](https://mlsanity.com/paper/2306.05779/) (2023)

## Built on by (most cited first, 7 of 7)

- [SurvKAN: A Fully Parametric Survival Model Based on Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2602.02179/) (2026)
- [Good Rankings, Wrong Probabilities: A Calibration Audit of Multimodal Cancer Survival Models](https://mlsanity.com/paper/2604.04239/) (2026)
- [Deep Kernel Aalen-Johansen Estimator: An Interpretable and Flexible Neural Net Framework for Competing Risks](https://mlsanity.com/paper/2512.08063/) (2025)
- [A Unified Survival Benchmark for Temporal Dropout Risk Prediction in Learning Analytics](https://mlsanity.com/paper/2604.08870/) (2026)
- [The C-index illusion: discrimination without calibration in published survival models](https://mlsanity.com/paper/2607.19526/) (2026)
- [A reproducible and extensible framework for benchmarking competing risks survival models](https://mlsanity.com/paper/2608.00271/) (2026)
- [Paired Recipient-based Evaluation of Survival Prediction for Deceased Donor Kidney Transplants](https://mlsanity.com/paper/2608.03017/) (2026)

## Abstract (arXiv)

The current state of evaluation in survival analysis is plagued by the persistent use of evaluation metrics in ways that are misaligned with the stated modeling objective. In addition, many such evaluations are based on censoring assumptions that are left implicit or unjustified. This means that the reported performance can be misleading and may fail to answer the scientific or modeling question the evaluation was intended to address. In this position paper, we critically examine evaluation practices in survival analysis and highlight how censoring makes evaluation fundamentally different from standard regression or classification. We place particular focus on concordance-based measures, such as the C-index, which we show are heavily overused in the literature. To help identify appropriate metrics, we propose a set of key desiderata and introduce a double-helix ladder, in which valid evaluation requires alignment between metric and modeling assumptions. Through controlled experiments, we show that violations of this alignment can lead to misleading model comparisons. We conclude by providing practical guidance on how to evaluate a survival model.