从单个随机对照试验中严格评估异质治疗效应的框架
Jane W Liang1,2, Lu Tian3, Manjula Kurella Tamura4,5
1Quantitative Sciences Unit, Section of Biostatistics, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA.
American journal of epidemiology
|November 20, 2025
概括
本研究引入了一个框架,通过开发效果模型来识别异构的治疗效应. 这些模型有助于通过预测个体患者从治疗中获得的益处来个性化医疗.
科学领域:
- 生物统计学 生物统计学
- 临床流行病学临床流行病学
- 翻译医学是一种翻译医学.
背景情况:
- 随机对照试验 (RCT) 估计了平均治疗效果,但忽视了个体患者的变化.
- 异质性治疗效应发生在治疗对不同患者子组有益,有害或没有影响时.
- 识别这些影响对于个性化医疗和优化治疗策略至关重要.
研究的目的:
- 为开发和评估效应模型提供一个一般框架.
- 通过基线共变量定义的子组内量化异质治疗效应.
- 根据预测的个人益处,实现针对性的治疗方案.
主要方法:
- 开发了"效果模型"以直接建模共变量和治疗分配之间的相互作用.
- 从患者排名的效果模型中得出的"效果分数".
- 解决了模型开发的挑战,例如过度装配.
- 通过使用现实世界数据集,以时间到事件结果和正确的审查来说明方法.
主要成果:
- 展示了一个严格的框架来描述异质治疗效应.
- 展示了效果模型和得分对于个性化治疗预测的实用性.
- 成功地将该方法应用于具有复杂时间到事件数据的数据集.
结论:
- 效果模型提供了一种可靠的方法来量化和理解异构的治疗效应.
- 这一框架有助于制定有针对性的治疗策略.
- 该方法适用于各种临床环境,包括那些经过审查的时间到事件数据.
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