在瘤学与治疗中止的RCT中评估因果关系对时间到事件结果的因果关系
Veronica Ballerini1, Björn Bornkamp2, Fabrizia Mealli3
1Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
Biometrical journal. Biometrische Zeitschrift
|November 13, 2025
概括
本研究介绍了一种主要的分层策略,用于分析临床试验数据,当患者因不良事件而提前停止治疗时. 这种方法有助于理解对生存结果的因果关系,即使在早期停止和审查的情况下也是如此.
科学领域:
- 临床试验方法论 临床试验方法论
- 生物统计学 生物统计学
- 因果推理因果推理
背景情况:
- 在临床试验中提前停止治疗会打破随机化,并使预期治疗效果的估计复杂化.
- 偶尔发生的事件,例如由于不良事件而停止治疗,需要根据ICH E9 (R1) 准则采取特定的处理策略.
研究的目的:
- 提出和实施一个主要的分层策略,以分析在持续时间治疗中断的情况下的生存结果.
- 将整体治疗意图效应分解为主要因果效应,基于潜在的患者停药行为.
主要方法:
- 一个主要的层级策略被用来处理时间到事件的中间变量和审查.
- 灵活的基于模型的贝叶斯方法被用于分析,提供可解释的结果.
- 该框架应用于来自瘤学试验的合成数据,并通过模拟研究进行验证.
主要成果:
- 拟议的策略有效地处理时间到事件的结果,包括治疗中止和审查.
- 共变量增强了假设的可信性,提高了推断精度,并描述了断药行为.
- 贝叶斯主要分层框架为复杂的临床试验场景提供了可解释的结果.
结论:
- 主要分层策略提供了一个强大的方法来分析随机对照试验中的因果关系,并停止治疗.
- 这种方法为患者子组提供了有价值的见解,这些细分组是由他们对治疗和停止治疗模式的反应来定义的.
- 这些发现可以为临床实践和未来临床试验方案的设计提供信息.
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