保序分布回归的生存版本
Survival Isotonic Distributional Regression
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中文总结 AI 辅助
本研究提出S-IDR，将IDR扩展至右删失场景，无调参且适配多类型协变量，在基准套件及肝移植MELD评分验证案例中表现良好，配套多语言包可用。
中文摘要 AI 辅助
我们提出了生存保序分布回归（Survival-IDR，简称S-IDR），这是一种在序约束下的条件生存分布非参数估计量，将保序分布回归（Isotonic Distributional Regression，简称IDR；Henzi等人，2021）扩展到右删失结局的场景。S-IDR无调参参数，可适配连续型、离散型及偏序型协变量。我们首先研究IDR的直接Kaplan-Meier适配版本：它在极小极大速率下是一致的，但仅当条件结局呈风险率序时成立。我们将该限制追溯至Kaplan-Meier估计量在非独立同分布样本上无法满足柯西中值性质，并基于该诊断结果构建了S-IDR。S-IDR估计量仅在条件结局呈随机占优时即可实现一致，当条件累积分布函数（CDF）的光滑度已知时可达到极小极大速率，且支持已知的跨阈值PAVA加速。我们进一步将S-IDR嵌入分布单索引框架并在基准套件上测试，同时将其应用于验证肝移植等待名单管理所用MELD评分的案例研究。配套的R、Python及Rust包可在该https URL获取。
英文摘要
We introduce Survival-IDR (S-IDR), a nonparametric estimator of conditional survival distributions under order restrictions, extending Isotonic Distributional Regression (IDR; Henzi et al., 2021) to right-censored outcomes. S-IDR has no tuning parameters and accommodates continuous, discrete, and partially ordered covariates. We first study the direct Kaplan-Meier adaptation of IDR: it is uniformly consistent at the minimax rate, but only when the conditional outcomes are hazard-rate ordered. We trace this restriction to the Kaplan-Meier estimator's failure to satisfy the Cauchy mean value property on non-i.i.d. samples, and use the diagnosis to construct S-IDR. The S-IDR estimator is uniformly consistent under only stochastic dominance of the conditional outcomes, attains the minimax rate when the smoothness of the conditional CDFs is known, and admits a known cross-threshold PAVA acceleration. We further embed S-IDR in a distributional single-index framework on a benchmark suite, and apply it in a case study that validates the MELD score used for liver-transplant wait list management. Accompanying R, Python and Rust packages are available at https://github.com/AlexanderHenzi/isodistrreg.
