部分线性加速失效时间模型的去偏机器学习方法
Debiased Machine Learning for Partially Linear Accelerated Failure Time Models
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中文总结 AI 辅助
针对右删失下部分线性AFT模型，本文构建首个基于秩的去偏机器学习框架，通过正交化U统计量等技术实现有效推断，模拟与All of Us数据应用验证了其性能与实用性。
中文摘要 AI 辅助
Cox模型仍是生存分析的默认方法，但比例风险假设常被违反，风险比难以解释。加速失效时间（AFT）模型提供了直观的时间尺度替代方案，然而在保留对目标暴露的有效推断的同时进行灵活协变量调整仍具挑战性。对于右删失下的部分线性AFT模型，基于秩的去偏机器学习（DML）框架尚未建立：基于秩的成对矩不满足Neyman正交性，标准交叉拟合无法直接应用于U统计量。我们结合正交化的基于秩的U统计量、删失校正的影响函数和块成对交叉拟合，开发了首个此类框架，在灵活的扰动估计下实现有效推断。模拟研究及对All of Us电子健康记录数据的应用验证了其有限样本性能和实用价值。
英文摘要
The Cox model remains the default for survival analysis, but the proportional hazards assumption is often violated and hazard ratios can be difficult to interpret. Accelerated failure time (AFT) models provide an intuitive time-scale alternative, yet flexible covariate adjustment while preserving valid inference on a target exposure remains challenging. For the partially linear AFT model under right censoring, a rank-based debiased machine learning (DML) framework remains undeveloped: the rank-based pairwise moment is not Neyman orthogonal and standard cross-fitting does not directly apply to U-statistics. We develop the first such framework by combining an orthogonalized rank-based U-statistic, a censoring-corrected influence function, and block-pairwise cross-fitting, yielding valid inference under flexible nuisance estimation. Simulations and an application to All of Us electronic health record data demonstrate finite-sample performance and practical utility.
