RECaST-Surv：不等随机化试验中生存终点的校准借用方法

中文摘要 AI 辅助

具有有限同期对照信息的随机试验——包括但不限于不等随机化设置——可以提供伦理和实践上的优势，尤其是在儿科和罕见病领域，但由于可用于直接比较的对照患者较少，这些试验往往会损失检验效能。从外部对照借用信息可以提高效率，但当外部人群与试验人群不够可比时，也可能使I类错误率膨胀。我们提出RECaST-Surv，一个用于时间-事件结局的贝叶斯迁移学习框架，将RECaST方法扩展到生存设置。该方法从外部对照数据学习一个结构化生存模型，并通过柯西随机效应将其校准到当前试验的同期对照臂。为了改善频率学操作特性，我们进一步开发了基于自助法的程序来校准检验规则以控制I类错误。RECaST-Surv可以容纳多个外部数据集，并且仅需要来自外部来源的汇总水平信息。模拟研究表明，该方法在具有挑战性的设置下保持接近名义水平的I类错误，同时相对于标准分析将检验效能提高约10%至12%。在一项肌萎缩侧索硬化试验模拟中，RECaST-Surv相对于不借用的随机对照试验分析将检验效能从82.8%提高到95.7%，同时保持了可接受的错误控制。

英文摘要

Randomized trials with limited concurrent control information---including but not limited to unequal-randomization settings---can offer ethical and practical advantages, especially in pediatric and rare diseases, but they often lose power because fewer control patients are available for direct comparison. Borrowing information from external controls may improve efficiency, but can also inflate the Type I error rate when the external and trial populations are not sufficiently comparable. We propose RECaST-Surv, a Bayesian transfer-learning framework for time-to-event outcomes that extends the RECaST method to survival settings. The method learns a structural survival model from external control data and calibrates it to the concurrent control arm of the current trial through a Cauchy random effect. To improve frequentist operating characteristics, we further develop a bootstrap-based procedure to calibrate the testing rule for Type I error control. RECaST-Surv can accommodate multiple external datasets and requires only summary-level information from external sources. Simulation studies show that the method maintains near-nominal Type I error across challenging settings while improving power by roughly 10%--12% over standard analyses. In an Amyotrophic Lateral Sclerosis trial emulation, RECaST-Surv increased power from 82.8% to 95.7% relative to the no-borrowing RCT analysis, while maintaining acceptable error control.

发表机构

• Stanford University（斯坦福大学）
• University Medical Center Utrecht（乌得勒支大学医学中心）
• University of Cambridge（剑桥大学）
• UCB Pharma（优时比制药）

机构由 AI 辅助整理，请以论文原文为准。