对于异质随机对照试验中事件发生时间结果的子组分析方法
Valentine Perrin1, Nathan Noiry2, Nicolas Loiseau2
1Owkin Inc., New York, USA. valentine.perrin@owkin.com.
BMC medical research methodology
|February 26, 2026
概括
识别对治疗有反应的患者子组对于精准医学至关重要. 本研究评估了时间到事件数据的方法,建议用于异质性检测的相互作用测试和用于子组识别的CATE估计.
科学领域:
- 生物统计学 生物统计学
- 临床试验分析
- 精准医学是一门精准的医学.
背景情况:
- 随机对照试验 (RCT) 的非显著结果可能会掩盖从实验药物中受益的患者子组,阻碍药物开发.
- 确定异质治疗效应对于精准医学至关重要,但缺乏对时间到事件数据的系统评估.
- 现有的基准主要集中在二进制和连续终点上,这在理解生存数据的子组分析方面存在差距.
研究的目的:
- 系统地评估子组分析算法以获得时间到事件的结果.
- 为了解决关键问题:是否存在治疗异质性? 哪些生物标志物可以预测它? 谁是好的响应者?谁是好的响应者?
- 引入一种新的合成和半合成数据生成过程,用于控制的异质性场景探索.
主要方法:
- 对应用到时间到事件数据的各种子组分析算法的评估.
- 利用新的数据生成过程来模拟不同的异质性水平.
- 评估检测异质性的方法,识别预测生物标志物和识别响应子组.
主要成果:
- 基于相互作用测试的方法在检测微妙的异质性方面表现出卓越的统计能力.
- 基于Cox的多变量和相互作用测试方法擅长识别异质性预测变量.
- 估计条件平均治疗效应 (CATE) 的方法,像S学习者一样,有效地识别响应子组,Cox多变量在低到中等异质性中表现良好.
结论:
- 没有任何一种方法对所有异质性调查都是最佳的;方法的选择取决于具体的研究问题.
- 建议基于相互作用测试的方法用于异质性检测和生物标记物识别.
- 倡导采用两步方法:首先,通过可解释的方法确定异质性和共变量,然后使用复杂的方法来识别子组.
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