在瘤学试验中更换治疗的增强两阶段估计:利用外部数据提高精度
Harlan Campbell1,2, Nicholas Latimer3, Jeroen P Jansen1,4
1Precision AQ, HEOR - Evidence Synthesis and Decision Modeling, Vancouver, BC, Canada.
Statistical methods in medical research
|September 30, 2025
概括
增强两阶段估计 (ATSE) 将随机试验数据与外部数据相结合,以改善长期治疗效果估计. 这种新方法有望减少偏差,并在外部数据无误时提高精度.
科学领域:
- 生物统计学 生物统计学
- 临床试验方法论 临床试验方法论
- 瘤学研究研究
背景情况:
- 在瘤学随机对照试验 (RCT) 中的治疗转换使长期效果估计变得复杂.
- 现有的方法,如双阶段估计,可能会产生不准确的结果,因为切换率很高或样本规模有限.
- 可以使用使用现实数据的外部控制臂,但忽视非切换参与者的有价值的RCT数据.
研究的目的:
- 引入和评估一种新的方法,即增强的两阶段估计 (ATSE),以更精确地估计在瘤学RCT中治疗转换的长期治疗效果.
- 使用模拟研究,将ATSE的性能与传统的两阶段估计和外部控制臂方法进行比较.
主要方法:
- 通过创建一个"混合非切换臂"来开发增强的两阶段估计 (ATSE),将来自RCT非切换器和外部数据集的数据结合起来.
- 在特定假设下评估ATSE表现:独立于转换后进展生存和RCT和外部队列之间的可交换性,取决于共变量.
- 进行模拟研究,以比较ATSE与双阶段估计和外部控制臂方法.
主要成果:
- 增强的两阶段估计 (ATSE) 证明了与两阶段估计和外部控制臂相比,当外部数据无误时,减少偏差和提高精度的潜力.
- ATSE的表现取决于场景.
- 当外部数据受到未测量的混时,ATSE仍然容易产生偏差,尽管通常比外部控制臂方法更少.
结论:
- 增强的两阶段估计 (ATSE) 提供了一种有前途的方法,以提高治疗转换的瘤学RCT中长期治疗效果估计的准确性.
- 该方法的有效性取决于可用的未经证实的外部数据以及其基础假设的有效性.
- 需要进一步的研究来探索ATSE在不同临床试验环境中的稳定性和适用性.
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