arXiv:2502.19460v4 Announce Type: replace-cross Abstract: Dependent censoring occurs when the event time and censoring time are not conditionally independent given the observed covariates. This complicates survival model evaluation because widely used metrics, such as the Brier score, typically handle right-censoring using inverse probability of censoring weighting (IPCW). Unfortunately, IPCW is valid only when the estimated censoring distribution is independent of the event time. We propose a dependent Brier score based on an Archimedean copula and the Copula-Graphic estimator, and establish
This research addresses a known statistical challenge in survival model evaluation that has become more prominent with the increased application of AI in fields like healthcare and risk assessment.
Improved evaluation metrics for survival models are crucial for robust and reliable AI applications where time-to-event predictions are critical, impacting areas from medical prognoses to financial risk management.
The proposed 'dependent Brier score' provides a more accurate way to assess survival model performance when data censoring is not independent, leading to more trustworthy model selections and deployments.
- · AI researchers
- · Healthcare sector
- · Insurance companies
- · Risk assessment platforms
- · Developers using naive survival model evaluation
More accurate survival models can be developed and validated, particularly in complex real-world scenarios where censored data is common.
Enhanced reliability of AI in critical applications like personalized medicine or predictive maintenance leads to better decision-making and outcomes.
Increased trust in AI's ability to handle intricate statistical challenges could accelerate its adoption in highly regulated and risk-averse industries.
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Read at arXiv cs.LG