Estimand-based Inference in Presence of Long-Term Survivors
存在长期存活者时基于目标量的推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要针对临床治疗效果比较，将结果拆为治愈率与易感者生存分布，并构造相对治疗效应目标量，对应因果推断和分布效应。
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存在长期存活者时基于目标量的推断
- 研究问题
- 当生存曲线交叉、延迟分离或尾部趋于平坦，使风险比难以描述治疗效果时，如何分别比较长期存活比例与易感人群的生存表现？
- 方法与思路
- 作者采用混合治愈框架，分别估计治愈率与易感者生存函数，并提出受 Kendall's tau 启发、比较易感者生存时间次序的图形目标量。
- 材料陈述的贡献
- 摘要称易感者生存估计保留 Kaplan–Meier 的多项性质，并给出大样本推断、模拟评估和基于数字化临床试验数据的应用。
- 阅读关注点
- 应核对充分随访的判定、治愈率与易感生存分布的识别、删失处理，以及比较治疗后易感亚组时目标量的解释和数字化误差。
- 阅读边界
- 这是 arXiv 预印本，同行评审及接收状态未核实；摘要未列完整删失及随访条件，易感亚组比较的因果解释和刊本变化均无法判断。
原文摘要
In this article, we develop nonparametric inference methods for comparing survival data across two samples, which are beneficial for clinical trials of novel cancer therapies where long-term survival is a critical outcome. These therapies, including immunotherapies or other advanced treatments, aim to establish durable effects. They often exhibit distinct survival patterns such as crossing or delayed separation and potentially leveling-off at the tails of survival curves, clearly violating the proportional hazards assumption and rendering the hazard ratio inappropriate for measuring treatment effects. The proposed methodology utilizes the mixture cure framework to separately analyze the cure rates of long-term survivors and the survival functions of susceptible individuals. We evaluate a nonparametric estimator for the susceptible survival function in the one-sample setting. Under sufficient follow-up, it is expressed as a location-scale-shift variant of the Kaplan-Meier (KM) estimator. It retains several desirable features of the KM estimator, including inverse-probability-censoring weighting, product-limit estimation, self-consistency, and nonparametric efficiency. In scenarios of insufficient follow-up, it can easily be adapted by incorporating a suitable cure rate estimator. In the two-sample setting, besides using the difference in cure rates to measure the long-term effect, we propose a graphical estimand to compare the relative treatment effects on susceptible subgroups. This process, inspired by Kendall's tau, compares the order of survival times among susceptible individuals. The proposed methods' large-sample properties are derived for further inference, and the finite-sample properties are examined through extensive simulation studies. The proposed methodology is applied to analyze the digitized data from the CheckMate 067 immunotherapy clinical trial.