ArticlePloS one2025
On the estimation of inverse-probability-of-censoring weights for the evaluation of survival prediction error.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- In-hospital development of new-onset frailty and discharge outcomes among patients with sepsis: a Korean multicenter registry study.Journal of intensive care · 2026Article
- Early adherence predicts treatment outcomes and mortality in people living with HIV and multidrug-resistant tuberculosis.Annals of epidemiology · 2026Article
- Improving risk assessment in forensic mental health: Temporal validation and clinical refinement of the FoVOx risk tool.European psychiatry : the journal of the Association of European Psychiatrists · 2026Article
- Modelling dependent censoring in time-to-event data using boosting copula regression.Lifetime data analysis · 2025Article
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Authors and funding
4 authors.
Funding
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Abstract
Inverse probability weighting (IPW) is a popular method for making inferences regarding unobserved or unobservable data of a target population based on observed data. This paper considers IPW applied to right-censored time-to-event data. We investigate the behavior of the inverse-probability-of-censoring weighted (IPCW) Brier score, which is frequently used to assess the predictive performance of time-to-event models. A key requirement of the IPCW Brier score is the estimation of the censoring distribution, which is needed to compute the weights. The established paradigm of splitting a dataset into a training and a test set for model fitting and evaluation raises the question which of these datasets to use in order to fit the censoring model. There seems to be considerable disagreement between authors with regards to this issue, and no standard has been established so far. To shed light on this important question, we conducted a comprehensive experimental study exploring various data scenarios and estimation schemes. We found that it is generally of little importance which dataset is used to model the censoring distribution. However, in some circumstances, such as in the case of a covariate-dependent censoring process, a small sample size, or when dealing with noisy data, it may be advisable to use the test set instead of the training set to model the censoring distribution. A detailed set of practical recommendations concludes our paper.
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