发表机构
The University of Tokyo; National Institute of Infectious Diseases, Japan Institute for Health Security（东京大学; 日本国立感染症研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对临床救援中首次跨越后无救援结局不可观测的问题，提出将均值界定简化为对停止救援条件效应的限制，通过有限凸规划获得锐利界并构建覆盖整个识别集的置信区间，实验表明该方法在较大样本下更有效。
AI 中文摘要
临床方案可能要求在患者状况跨越预设阈值时使用救援药物。在首次跨越之后，继续不救援情况下的结局便无法观测，这阻碍了在没有外推假设的情况下进行点识别。在本文中，我们在保留原始无救援干预的同时，针对这一假设估计量开发了锐利的部分识别与推断方法。我们证明，对该均值的界定恰好简化为对首次跨越历史中停止救援的条件均值效应的限制。基于这一表示，对预设跨越层平均值的限制产生了三项结果。(i) 一个有限凸规划给出了无需对跨越后轨迹建模的锐利界，该界由保持观测分布的全数据定律实现。(ii) 置信区间结合了抽样不确定性和数值误差，以覆盖整个锐利集合，包括没有观测到跨越的层。(iii) 在局部可行性条件下，即使存在零概率层，抽样和数值误差也能控制端点精度，而有效覆盖则不需要此类条件。我们的实验表明，层平均限制在较大样本中可产生更窄的置信区间，并且在救援罕见时，考虑空层中的不确定性可维持有效的覆盖。
英文摘要
Clinical protocols may require rescue medication when a patient's condition crosses a prespecified threshold. The outcome under continued non-rescue is then unobserved after first crossing, preventing point identification without extrapolation assumptions. In this paper, we develop sharp partial identification and inference for this hypothetical estimand while retaining the original no-rescue intervention. We show that bounding this mean reduces exactly to restricting conditional mean effects of withholding rescue at first-crossing histories. Building on this representation, restrictions on prespecified crossing-stratum averages yield three results. (i) A finite convex program gives sharp bounds without modeling post-crossing trajectories, attained by full-data laws that preserve the observed distribution. (ii) Confidence intervals incorporate sampling uncertainty and numerical error to cover the entire sharp set, including strata with no observed crossings. (iii) Under local feasibility conditions, sampling and numerical errors control endpoint accuracy even with zero-probability strata, whereas valid coverage requires no such conditions. Our experiments show that the stratum-average restrictions can yield narrower confidence intervals in larger samples, and that accounting for uncertainty in empty strata maintains valid coverage when rescue is rare.
