在异质性存在下使用混合先验自适应结合随机对照与外部对照数据

中文摘要 AI 辅助

当随机试验中的资源有限时，一种常见策略是用外部对照来扩充对照臂的数据。我们提出了一种混合组序贯贝叶斯设计，利用该方法比较时间至事件分布。该设计允许治疗间效应在不同患者亚组间存在差异，并自适应地合并具有相似风险函数的亚组。其目标是在不因试验与外部数据之间的系统性差异而损害比较性推断有效性的前提下，减少对照臂样本量。该模型通过使用自适应混合先验动态借用外部对照数据，该先验是非信息先验与由外部对照构建的信息先验的加权平均。借用量与随机对照和外部对照之间的一致性成正比。在每次中期分析时，该设计比较各亚组内的治疗，若得出劣效性或优效性结论，则停止该亚组的入组。模拟表明，所提出的设计在随机试验中纳入外部对照数据方面优于其他方法，并提高了效率。

英文摘要

When resources are limited in a randomized trial, a common strategy is to augment data from the control arm with external controls. We propose a hybrid group sequential Bayesian design that uses this approach to compare time-to-event distributions. The design allows between-treatment effects to differ between patient subgroups, and adaptively combines subgroups that have similar hazard functions. The aim is to reduce the control arm sample size without compromising the validity of comparative inferences due to systematic trial-versus-external data differences. The model borrows external control data dynamically by using a self-adapting mixture prior, which is a weighted average of a non-informative prior and an informative prior constructed from the external controls. The amount of borrowing is proportional to the agreement between the randomized and external controls. At each interim analysis, the design compares the treatments in each subgroup, and if inferiority or superiority is concluded stops accrual for that subgroup. Simulations show that the proposed design outperforms other methods for incorporating external control data in a randomized trial, and improves efficiency.

发表机构

• The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）
• The University of Texas MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）

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