提出非交叉分位数回归框架，精准建模生存时间分布差异。
Non-Crossing Deep Quantile Regression for Distributional Survival Prediction
- 构建联合估计多个分位数的非交叉框架，保证顺序合理
- 在27种模拟和6个真实队列中分位数损失更低，覆盖率达标
- 适合需要个体化生存预测的临床研究，可揭示时变协变量效应
生存分析中，协变量对早期与晚期事件风险的影响常不同，但传统基于危险率或均值的总结会掩盖这种差异。分位数建模能描述原始时间尺度上的完整条件分布，但现有处理右删失数据的方法或过于僵硬，或导致分位数曲线交叉。本文提出针对右删失数据的非交叉分位数（CNQ）框架，联合估计多个条件生存分位数，并通过构造保证其有效排序；模型灵活性由Kolmogorov-Arnold和Transformer主干提供，并建立了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种模拟设置及6个队列中，当条件分布不对称时，该框架的分位数损失低于分位数、危险率和树基竞争方法，且六个队列的区间覆盖率均接近名义水平。在两个临床案例研究（METABRIC乳腺癌；FLCHAIN人群死亡率）中，该方法恢复了随生存分布变化的协变量效应，这些效应在单一危险比下会被隐藏，并生成一致的个体化分位数里程碑。代码：https://github.com/BIG-S2/deepcnq
原文摘要 · Abstract (English)
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
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