lifelines-hc：用于Python中稀疏非比例风险偏离的高临界值检验
lifelines-hc: Higher Criticism testing for sparse non-proportional hazard departures in Python
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中文总结 AI 辅助
本文提出lifelines-hc包，实现HCHG检验，用于检测生存分析中稀疏非比例风险偏离，在四个临床数据集上显著优于对数秩检验，且不依赖预设时间模式。
中文摘要 AI 辅助
对数秩检验是两样本生存比较的标准工具，对比例风险备择假设具有良好功效，但面对稀疏风险偏离时会失去功效，这种偏离是指风险差异集中在少数时间区间内，且这些区间在随访中的位置事先未知（Kipnis, Galili 和 Yakhini, Biometrika 2026）。加权对数秩检验——Gehan-Wilcoxon、Tarone-Ware、Peto-Prentice、Fleming-Harrington——各自施加了预先指定的时间重点。组合程序如MaxCombo和Yang-Prentice短期/长期风险比模型放宽了这一选择，但仍仅扫描少量全局时间形状的字典，对发生在这些形状之外的稀疏偏离不敏感。我们引入了lifelines-hc，这是一个扩展lifelines生存库的Python包，包含HCHG检验：对每个区间超几何p值应用高临界值检验。HCHG适用于右删失两样本数据，能在不承诺任何时间模式的情况下检测未知位置的稀疏风险偏离。在四个临床案例研究——CheckMate 057 PFS（n=582）、COMET-1 OS（n=1028）、AZURE DFS（n=3359）以及哥本哈根肝病研究组肝硬化试验（n=446，完全公开的个体患者数据）——中，HCHG在每个数据集上都达到p <= 0.014，而对数秩检验在所有数据集中均不显著（p >= 0.26）。MaxCombo和Yang-Prentice自适应对数秩检验检测到CheckMate 057中的延迟获益交叉（p < 0.001），但在其余三个数据集中不显著（p >= 0.24），这些数据集中的偏离是稀疏或多窗口的，而非平滑的短期/长期风险比模式。lifelines-hc在MIT许可下免费提供，可通过此https URL和pip install lifelines-hc获取。
英文摘要
The log-rank test is the standard tool for two-sample survival comparison and has good power against proportional-hazards alternatives, but it loses power against sparse hazard departures, in which the hazard difference is concentrated in a small number of time intervals whose locations along the follow-up are unknown a priori (Kipnis, Galili and Yakhini, Biometrika 2026). Weighted log-rank tests -- Gehan-Wilcoxon, Tarone-Ware, Peto-Prentice, Fleming-Harrington -- each impose a pre-specified temporal emphasis. Combination procedures such as MaxCombo and the Yang-Prentice short-term/long-term hazard-ratio model relax that choice, but still scan only a small dictionary of global temporal shapes and remain insensitive to sparse departures occurring outside them. We introduce lifelines-hc, a Python package extending the lifelines survival library with the HCHG test: Higher Criticism applied to per-interval hypergeometric p-values. HCHG applies to right-censored two-sample data and detects sparse hazard departures at unknown locations without committing to any temporal pattern. Across four clinical case studies -- CheckMate 057 PFS (n=582), COMET-1 OS (n=1028), AZURE DFS (n=3359), and the Copenhagen Study Group for Liver Diseases cirrhosis trial (n=446, fully public individual patient data) -- HCHG achieves p <= 0.014 in every dataset, while the log-rank test is non-significant throughout (p >= 0.26). MaxCombo and the Yang-Prentice adaptive log-rank test detect the delayed-benefit crossing in CheckMate 057 (p < 0.001) but are non-significant on the remaining three (p >= 0.24), where the departure is sparse or multi-window rather than a smooth short-term/long-term hazard-ratio pattern. lifelines-hc is freely available under the MIT license at https://github.com/alonkipnis/lifelines-hc and via pip install lifelines-hc.
发表机构
- School of Computer Science, Reichman University（雷希曼大学计算机学院）
- Department of Computer Science, Technion – Israel Institute of Technology（以色列理工学院计算机科学系）
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