将因果森林应用于随机对照试验数据以确定异质治疗效应:一个案例研究
Eleanor Van Vogt1, Anthony C Gordon1, Karla Diaz-Ordaz2
1Imperial College London, London, UK.
BMC medical research methodology
|February 22, 2025
概括
机器学习在败血性休克患者中发现了异质的治疗效应,血清水平表明了不同的结果. 因果森林提供了一种新的方法,可以为个性化治疗策略找到临床上可操作的子组.
科学领域:
- 关键护理医学 关键护理医学
- 生物统计学 生物统计学
- 机器学习 机器学习
背景情况:
- 随机对照试验 (RCT) 中的经典子组分析测试了对异质治疗效应 (HTEs) 的相互作用.
- 连续的共变量给使用传统方法确定临床可操作子组带来了挑战.
- 非参数因果机器学习提供了一个灵活的替代方案,可以同时在多个修饰器上分析HTEs.
研究的目的:
- 为了比较经典,数据适应性和因果机器学习方法来识别败血性休克患者的HTEs.
- 评估因果森林在发现基于连续共变量的临床可操作子组的有用性.
主要方法:
- 对VANISHRCT的二次分析,比较了压素和上腺素在败血症休克中的作用.
- 应用了经典 (邦费罗尼纠正的相互作用测试),数据适应 (层次拉索回归) 和非参数因果机器学习 (因果森林) 方法.
- 从因果森林中提取的模态根分裂,以定义数据衍生子组.
主要成果:
- 所有方法都确定了与血清度相关的高高温体.
- 层次拉索确定了与血清,,温度,血小板计数和缺血性心脏病的相互作用.
- 因果森林表明HTE (p=0.124),主要分为血清 (4.68 mmol/L),揭示了差异性生存概率.
结论:
- 因果森林与其他方法在通过血清来识别HTEs时保持一致.
- 经典和数据适应性方法可以识别HTE来源,但不能识别可操作的子组.
- 从因果森林中提取根分裂为数据衍生,临床相关子组识别提供了一种新的方法.
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