Toward Conditional Distribution Calibration in Survival Prediction
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916660054589440 |
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| author | Qi, Shi-ang Yu, Yakun Greiner, Russell |
| author_facet | Qi, Shi-ang Yu, Yakun Greiner, Russell |
| contents | Survival prediction often involves estimating the time-to-event distribution from censored datasets. Previous approaches have focused on enhancing discrimination and marginal calibration. In this paper, we highlight the significance of conditional calibration for real-world applications -- especially its role in individual decision-making. We propose a method based on conformal prediction that uses the model's predicted individual survival probability at that instance's observed time. This method effectively improves the model's marginal and conditional calibration, without compromising discrimination. We provide asymptotic theoretical guarantees for both marginal and conditional calibration and test it extensively across 15 diverse real-world datasets, demonstrating the method's practical effectiveness and versatility in various settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20579 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Toward Conditional Distribution Calibration in Survival Prediction Qi, Shi-ang Yu, Yakun Greiner, Russell Machine Learning Artificial Intelligence Survival prediction often involves estimating the time-to-event distribution from censored datasets. Previous approaches have focused on enhancing discrimination and marginal calibration. In this paper, we highlight the significance of conditional calibration for real-world applications -- especially its role in individual decision-making. We propose a method based on conformal prediction that uses the model's predicted individual survival probability at that instance's observed time. This method effectively improves the model's marginal and conditional calibration, without compromising discrimination. We provide asymptotic theoretical guarantees for both marginal and conditional calibration and test it extensively across 15 diverse real-world datasets, demonstrating the method's practical effectiveness and versatility in various settings. |
| title | Toward Conditional Distribution Calibration in Survival Prediction |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.20579 |