Time-Dependent Pseudo $\boldsymbol{R^2}$ for Assessing Predictive Performance in Competing Risks Data
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911066766704640 |
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| author | Zhuang, Zian Su, Wen Kawaguchi, Eric Li, Gang |
| author_facet | Zhuang, Zian Su, Wen Kawaguchi, Eric Li, Gang |
| contents | Evaluating and validating the performance of prediction models is a fundamental task in statistics, machine learning, and their diverse applications. However, developing robust performance metrics for competing risks time-to-event data poses unique challenges. We first highlight how certain conventional predictive performance metrics, such as the C-index, Brier score, and time-dependent AUC, can yield undesirable results when comparing predictive performance between different prediction models. To address this research gap, we introduce a novel time-dependent pseudo $R^2$ measure to evaluate the predictive performance of a predictive cumulative incidence function over a restricted time domain under right-censored competing risks time-to-event data. Specifically, we first propose a population-level time-dependent pseudo $R^2$ measures for the competing risk event of interest and then define their corresponding sample versions based on right-censored competing risks time-to-event data. We investigate the asymptotic properties of the proposed measure and demonstrate its advantages over conventional metrics through comprehensive simulation studies and real data applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_15040 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Time-Dependent Pseudo $\boldsymbol{R^2}$ for Assessing Predictive Performance in Competing Risks Data Zhuang, Zian Su, Wen Kawaguchi, Eric Li, Gang Methodology Applications Evaluating and validating the performance of prediction models is a fundamental task in statistics, machine learning, and their diverse applications. However, developing robust performance metrics for competing risks time-to-event data poses unique challenges. We first highlight how certain conventional predictive performance metrics, such as the C-index, Brier score, and time-dependent AUC, can yield undesirable results when comparing predictive performance between different prediction models. To address this research gap, we introduce a novel time-dependent pseudo $R^2$ measure to evaluate the predictive performance of a predictive cumulative incidence function over a restricted time domain under right-censored competing risks time-to-event data. Specifically, we first propose a population-level time-dependent pseudo $R^2$ measures for the competing risk event of interest and then define their corresponding sample versions based on right-censored competing risks time-to-event data. We investigate the asymptotic properties of the proposed measure and demonstrate its advantages over conventional metrics through comprehensive simulation studies and real data applications. |
| title | Time-Dependent Pseudo $\boldsymbol{R^2}$ for Assessing Predictive Performance in Competing Risks Data |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2507.15040 |