发表机构
Takeda Pharmaceutical Company, Ltd.（武田药品工业株式会社）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对随机试验中的删失数据，提出直接学习条件RMST治疗获益排名的方法，通过正交伪结局和成对排名损失优化排序，提升治疗获益识别。
AI 中文摘要
在精准医学中，治疗决策往往更依赖于识别哪些患者最可能从治疗中获益，而非精确估计每位患者的获益。然而，现有方法估计患者特异性治疗效果，并将患者排名作为次要步骤。我们提出了一种直接排名方法，用于随机试验中右删失结局的条件RMST治疗获益。我们首先构建一个经删失调整的正交RMST伪结局，其条件期望等于真实的条件RMST差异。然后，我们成对比较患者，并优化一个直接针对治疗获益排序的平滑排名损失。该成对准则整合了对事件和删失分布估计的正交校正。我们证明，所提出损失的总体最小化器诱导的排序与真实条件RMST治疗获益相同，且所得估计方程对干扰生存和删失模型是Neyman正交的。在具有非线性治疗效应异质性和中度至重度删失的模拟研究中，与插值RMST估计和同一RMST伪结局的回归相比，所提出方法改善了秩相关和治疗获益富集。应用于一项随机乳腺癌试验，证明了对具有更大治疗获益患者的识别有所改善。总之，所提出的框架结合了正交生存学习和成对排名，可能在治疗优先排序和患者选择为主要目标的精准医学场景中特别有用。
英文摘要
In precision medicine, treatment decisions often depend more on identifying which patients are most likely to benefit from treatment than on accurately estimating benefit for every patient. However, existing methods estimate patient-specific treatment effects and derive patient rankings as a secondary step. We propose a direct ranking approach for conditional RMST treatment benefit in randomized trials with right-censored outcomes. We first construct a censoring-adjusted orthogonal RMST pseudo-outcome whose conditional expectation equals the true conditional RMST difference. Then, we compare patients pairwise and optimize a smooth ranking loss that directly targets treatment-benefit ordering. The pairwise criterion incorporates orthogonal corrections for estimation of the event and censoring distributions. We show that the population minimizer of the proposed loss induces the same ordering as the true conditional RMST treatment benefit and that the resulting estimating equation is Neyman-orthogonal to nuisance survival and censoring models. In simulation studies with nonlinear treatment-effect heterogeneity and moderate to heavy censoring, the proposed approach improved rank correlation and treatment-benefit enrichment relative to both plug-in RMST estimation and regression of the same RMST pseudo-outcome. Application to a randomized breast cancer trial demonstrated improved identification of patients with greater treatment benefit. In conclusion, the proposed framework combines orthogonal survival learning with pairwise ranking and may be particularly useful in precision medicine settings where treatment prioritization and patient selection are the primary goals.
