A new discrimination measure for assessing predictive performance of non-linear survival models
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866909571263496192 |
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| author | Faruk, Alfensi Palczewski, Jan Aivaliotis, Georgios |
| author_facet | Faruk, Alfensi Palczewski, Jan Aivaliotis, Georgios |
| contents | Non-linear survival models are flexible models in which the proportional hazard assumption is not required. This poses difficulties in their evaluation. We introduce a new discrimination measure, time-dependent Uno's C-index, to assess the discrimination performance of non-linear survival models. This is an unbiased version of Antolini's time-dependent concordance. We prove convergence of both measures employing Nolan and Pollard's results on U-statistics. We explore the relationship between these measures and, in particular, the bias of Antolini's concordance in the presence of censoring using simulated data. We demonstrate the value of time-dependent Uno's C-index for the evaluation of models trained on censored real data and for model tuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_05630 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A new discrimination measure for assessing predictive performance of non-linear survival models Faruk, Alfensi Palczewski, Jan Aivaliotis, Georgios Methodology 62N03 Non-linear survival models are flexible models in which the proportional hazard assumption is not required. This poses difficulties in their evaluation. We introduce a new discrimination measure, time-dependent Uno's C-index, to assess the discrimination performance of non-linear survival models. This is an unbiased version of Antolini's time-dependent concordance. We prove convergence of both measures employing Nolan and Pollard's results on U-statistics. We explore the relationship between these measures and, in particular, the bias of Antolini's concordance in the presence of censoring using simulated data. We demonstrate the value of time-dependent Uno's C-index for the evaluation of models trained on censored real data and for model tuning. |
| title | A new discrimination measure for assessing predictive performance of non-linear survival models |
| topic | Methodology 62N03 |
| url | https://arxiv.org/abs/2504.05630 |