发表机构
University of North Carolina at Chapel Hill; The Hong Kong Polytechnic University（北卡罗来纳大学教堂山分校; 香港理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对生存分析中现有分位数回归方法的交叉问题，提出CNQ框架，结合Kolmogorov-Arnold与Transformer骨干，在模拟和临床数据上表现更优，可恢复生存分布上变化的协变量效应。
AI 中文摘要
在生存分析中，协变量对事件风险的作用方式往往在早期和晚期失效时间之间存在差异，但基于风险和均值的摘要会将这种变化压缩为单个数值。基于分位数的建模则在原始时间尺度上描述完整的条件分布，不过现有的删失数据方法要么不够灵活，要么会产生逻辑不一致的交叉分位数曲线。我们针对右删失数据提出了删失非交叉分位数（Censored Non-crossing Quantile, CNQ）框架，该框架可联合估计多个条件生存分位数，且通过构造保证有效排序，其灵活性由Kolmogorov-Arnold和Transformer骨干提供，我们还建立了在所有拟合分位数水平上联合成立的有限样本超额风险界。在27个模拟设置和6个队列中，当条件分布不对称时，该框架比基于分位数、风险和树的对比方法取得更低的pinball损失，且在所有6个队列上的区间覆盖率更接近名义值。在两项临床案例研究（METABRIC，乳腺癌；FLCHAIN，人群死亡率）中，它恢复了在生存分布上变化的协变量效应，而这种效应会被单一风险比隐藏，并产生一致的个体化分位数里程碑。代码：this https URL
英文摘要
In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based summaries collapse this variation into a single number. Quantile-based modeling instead describes the full conditional distribution on the original time scale, but existing censored-data methods are either inflexible or produce logically inconsistent crossing quantile curves. We propose a Censored Non-crossing Quantile (CNQ) framework for right-censored data that jointly estimates several conditional survival quantiles and guarantees valid ordering by construction, with flexibility supplied by Kolmogorov-Arnold and Transformer backbones, and we establish a finite-sample excess-risk bound holding jointly across all fitted quantile levels. Across 27 simulation settings and six cohorts the framework attains lower pinball loss than quantile-, hazard- and tree-based competitors whenever the conditional distribution is asymmetric, with interval coverage closer to nominal on all six. In two clinical case studies (METABRIC, breast cancer; FLCHAIN, population mortality) it recovers covariate effects that vary across the survival distribution and would be hidden by a single hazard ratio, and yields coherent individualized quantile milestones. Code: https://github.com/BIG-S2/deepcnq
Comments50 pages, 15 figures, 17 tables. Main text and supplementary material combined into a single document. Submitted to the Annals of Applied Statistics
