AI 中文总结
针对随机临床试验生存结局处理效应推断的不足，提出两种非参数置信区间，模拟验证其有效性并结合实例说明应用，提供相关代码与应用工具。
AI 中文摘要
对数秩检验和Kaplan--Meier曲线是随机临床试验中分析事件发生时间数据的标准工具，但二者均未提供处理效应大小的汇总。实践者通常通过报告Cox比例风险模型的风险比或加速失效时间（AFT）模型的加速因子来填补这一空白，但这两种方法都需要超出对数秩检验或Kaplan--Meier估计量所需的假设。我们提出两种用于标量效应大小汇总的非参数置信区间：加性偏移c和乘性因子ρ，通过在处理效应恒定的尖锐原假设下对对数秩检验求逆得到。基于Li和Small（2023）的随机化推断框架，这两种区间仅在随机化分布下有效，无需对事件时间分布做任何假设。我们通过模拟评估所提出的乘性区间，发现其在一系列删失率和样本量下均能保持名义覆盖率，包括在错误设定参数化AFT模型的数据生成过程中，且与正确设定下参数化AFT推断相比仅产生适度的效率损失。我们使用囊性纤维化的rhDNase随机试验数据说明该方法，并提供R代码和Shiny应用以便实施。
英文摘要
The log-rank test and Kaplan--Meier plot are standard tools for analyzing time-to-event data in randomized clinical trials, yet neither provides a summary of the magnitude of the treatment effect. Practitioners typically fill this gap by reporting a hazard ratio from a Cox proportional-hazards model or an acceleration factor from an accelerated failure time (AFT) model, but both require assumptions beyond those needed for the log-rank test or Kaplan--Meier estimator. We propose two nonparametric confidence intervals for scalar effect-size summaries, an additive shift c and a multiplicative factor $ρ$, obtained by inverting the log-rank test under sharp null hypotheses of constant treatment effects. Building on the randomization-inference framework of Li and Small (2023), both intervals are valid under the randomization distribution alone, requiring no assumptions for the event-time distribution. We evaluate the proposed multiplicative interval via simulation, finding that it maintains nominal coverage across a range of censoring rates and sample sizes, including under data-generating processes that misspecify a parametric AFT model, while incurring only a modest efficiency loss compared to parametric AFT inference under correct specification. We illustrate the approach using data from a randomized trial of rhDNase for cystic fibrosis and provide R code and a Shiny application for ease of implementation.
