Beyond Fixed Restriction Time: Adaptive Restricted Mean Survival Time Methods in Clinical Trials
超越固定截断时间：临床试验中的自适应限制平均生存时间方法AI 解读 ↓
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超越固定截断时间：临床试验中的自适应限制平均生存时间方法
- 研究问题
- 限制平均生存时间分析如何自适应选择截断时间，兼顾迟发治疗收益与估计精度，并在选择时间之后仍保持有效的统计推断？
- 方法与思路
- 作者在连续时间范围内优化效应与精度的平衡准则，以凸包估计和惩罚方法处理原假设下的非正则性，并另提离散网格选择程序。
- 材料陈述的贡献
- 摘要称其理论处理了时间选择的额外变异，离散网格程序可获 oracle 表现；模拟及胰腺癌试验支持功效改善，并提供 AdaRMST 包。
- 阅读关注点
- 应核对优化准则、删失与随访条件、原假设下的非正则处理，以及离散选择无渐近代价的条件和模拟中一类错误率的控制。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核实；摘要未给出理论条件、数值结果或临床收益大小，不能将功效优势推广至所有场景。
原文摘要
Restricted mean survival time (RMST) offers a compelling nonparametric alternative to hazard ratios for right-censored time-to-event data, particularly when the proportional hazards assumption is violated. By capturing the total event-free time over a specified horizon, RMST provides an intuitive and clinically meaningful measure of absolute treatment benefit. Nonetheless, selecting the restriction time $L$ poses challenges: choosing a small $L$ can overlook late-emerging benefits, whereas a large $L$, often underestimated in its impact, may inflate variance and undermine power. We propose a novel data-driven, adaptive procedure that identifies the optimal restriction time $L^*$ from a continuous range by maximizing a criterion balancing effect size and estimation precision. Consequently, our procedure is particularly useful when the pattern of the treatment effect is unknown at the design stage. We provide a rigorous theoretical foundation that accounts for variability introduced by adaptively choosing $L^*$. To address nonregular estimation under the null, we develop two complementary strategies: a convex-hull-based estimator, and a penalized approach that further enhances power. Additionally, when restriction time candidates are defined on a discrete grid, we propose a procedure that surprisingly incurs no asymptotic penalty for selection, thus achieving oracle performance. Extensive simulations across realistic survival scenarios demonstrate that our method outperforms traditional RMST analyses and the log-rank test, achieving superior power while maintaining nominal Type I error rates. In a phase III pancreatic cancer trial with transient treatment effects, our procedure uncovers clinically meaningful benefits that standard methods overlook. Our methods are implemented in the R package AdaRMST.