提出分层阿基米德拷贝模型，精准捕捉多重竞争风险间的依赖关系。
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks
- 用分层阿基米德拷贝结构建模竞争风险与删失的复杂依赖
- 在合成与真实数据上均优于现有最先进方法的生存预测性能
- 适合处理存在相互影响的医疗风险预测问题
生存分析中，受试者常面临多重竞争风险（如癌症患者同时患心脏病），这些疾病可能共同影响预后和删失情况。传统方法通常假设竞争风险独立，无法建模其相互依赖性。本文提出HACSurv，一种基于分层阿基米德拷贝的生存分析方法，从具有竞争风险的数据中学习分层拷贝结构与特定原因的生存函数。该方法利用灵活的分层阿基米德拷贝结构，有效表征不同风险与删失之间的依赖关系。通过捕捉风险间及风险与删失间的依赖，显著提升生存预测精度，并揭示风险交互机制。在合成数据集上的实验表明，该方法能准确识别复杂依赖结构并精确预测生存分布，而对比方法则存在明显偏差。在多个真实世界数据集上的实验进一步验证，相比现有最先进方法，HACSurv在生存预测上表现更优。
原文摘要 · Abstract (English)
In survival analysis, subjects often face competing risks; for example, individuals with cancer may also suffer from heart disease or other illnesses, which can jointly influence the prognosis of risks and censoring. Traditional survival analysis methods often treat competing risks as independent and fail to accommodate the dependencies between different conditions. In this paper, we introduce HACSurv, a survival analysis method that learns Hierarchical Archimedean Copulas structures and cause-specific survival functions from data with competing risks. HACSurv employs a flexible dependency structure using hierarchical Archimedean copulas to represent the relationships between competing risks and censoring. By capturing the dependencies between risks and censoring, HACSurv improves the accuracy of survival predictions and offers insights into risk interactions. Experiments on synthetic dataset demonstrate that our method can accurately identify the complex dependency structure and precisely predict survival distributions, whereas the compared methods exhibit significant deviations between their predictions and the true distributions. Experiments on multiple real-world datasets also demonstrate that our method achieves better survival prediction compared to previous state-of-the-art methods.
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