ArticleProceedings of machine learning research2023
An Effective Meaningful Way to Evaluate Survival Models.
Article in Proceedings of machine learning research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Transfer Learning and Machine Learning for Training Five-Year Survival Prognostic Models in Early Breast Cancer: Development and Validation Study.Journal of medical Internet research · 2026Article
- Survival Prediction in Patients With Bladder Cancer Undergoing Radical Cystectomy Using a Machine Learning Algorithm: Retrospective Single-Center Study.JMIR perioperative medicine · 2026Article
- Modelling dependent censoring in time-to-event data using boosting copula regression.Lifetime data analysis · 2025Article
- Weighted Brier Score - an Overall Summary Measure for Risk Prediction Models with Clinical Utility Consideration.Statistics in biosciences · 2025Article
- A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis.PloS one · 2025Article
- On the estimation of inverse-probability-of-censoring weights for the evaluation of survival prediction error.PloS one · 2025Article
- The Role of Digital Technology in Preventing Anemia Among Pregnant Women: A Scoping Review.International journal of telemedicine and applications · 2025Review
- Survival models and longitudinal medical events for hospital readmission forecasting.BMC health services research · 2024Article
- Article
- Accurate personalized survival prediction for amyotrophic lateral sclerosis patients.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
One straightforward metric to evaluate a survival prediction model is based on the Mean Absolute Error (MAE) - the average of the absolute difference between the time predicted by the model and the true event time, over all subjects. Unfortunately, this is challenging because, in practice, the test set includes (right) censored individuals, meaning we do not know when a censored individual actually experienced the event. In this paper, we explore various metrics to estimate MAE for survival datasets that include (many) censored individuals. Moreover, we introduce a novel and effective approach for generating realistic semi-synthetic survival datasets to facilitate the evaluation of metrics. Our findings, based on the analysis of the semi-synthetic datasets, reveal that our proposed metric (MAE using pseudo-observations) is able to rank models accurately based on their performance, and often closely matches the true MAE - in particular, is better than several alternative methods.
Identifiers
40895293PMC12396822What 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.