ArticleStatistics in medicine2023
Pitfalls of the concordance index for survival outcomes.
Article in Statistics in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
55 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Predicting short- to medium-term care home admission risk in older adults: a systematic review of externally validated models.Age and ageing · 2024Pooled it
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- Bellmunt Risk Score as a Prognostic Tool in Metastatic Castration-Resistant Prostate Cancer Survival.JAMA network open · 2026Trial
- Circulating Tumor DNA Assessment for Treatment Monitoring Adds Value to PSA in Metastatic Castration-Resistant Prostate Cancer.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Trial
- Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient-Specific Survival Probabilities in Clinical Trial Data.Biometrical journal. Biometrische Zeitschrift · 2026Article
- Deep learning survival analysis with time-varying covariates: extending PyCox to assess COVID-19 antiviral treatments and long-COVID associations.JAMIA open · 2026Article
- CleanSurvival: automated data preprocessing for time-to-event models using reinforcement learning.BMC medical informatics and decision making · 2026Article
- Development of aAnnals of nuclear medicine · 2026Article
- Article
- Article
- Deep-Learning Algorithm Diagnostic Support for Usual Interstitial Pneumonia Pattern Recognition in Fibrotic Interstitial Lung Disease.Respirology (Carlton, Vic.) · 2026Article
- Review
- Beyond classical models: LLM-driven survival analysis for breast cancer prognosis using European cancer registry data.BMC medical informatics and decision making · 2026Article
- Can clinicians predict individual patient outcomes in neuroendocrine tumors treated with [177Lu]Lu-DOTATATE?The oncologist · 2026Article
- The survival double descent: generalization dynamics of deep neural networks in time-to-event analysis.BMC medical research methodology · 2026Article
- Multi-modal AI for comprehensive breast cancer prognostication.Nature communications · 2026Article
- Risk Prediction in Cardio-Oncology: Conceptual and Methodological Considerations: JACC: CardioOncology State-of-the-Art Review.JACC. CardioOncology · 2026Review
- Five tenets for advancing evidence-based precision medicine.Nature medicine · 2026Review
- Long-term performance of the Gleason score in predicting metastatic and fatal prostate cancer.American journal of epidemiology · 2026Article
- Bellmunt risk score enables survival prediction in men with metastatic castration resistant prostate cancer (mCRPC) undergoing PSMA-targeted radioligand therapy (LUMEN).European journal of nuclear medicine and molecular imaging · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Prognostic models are useful tools for assessing a patient's risk of experiencing adverse health events. In practice, these models must be validated before implementation to ensure that they are clinically useful. The concordance index (C-Index) is a popular statistic that is used for model validation, and it is often applied to models with binary or survival outcome variables. In this paper, we summarize existing criticism of the C-Index and show that many limitations are accentuated when applied to survival outcomes, and to continuous outcomes more generally. We present several examples that show the challenges in achieving high concordance with survival outcomes, and we argue that the C-Index is often not clinically meaningful in this setting. We derive a relationship between the concordance probability and the coefficient of determination under an ordinary least squares model with normally distributed predictors, which highlights the limitations of the C-Index for continuous outcomes. Finally, we recommend existing alternatives that more closely align with common uses of survival models.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.