因果森林与治疗权重的逆概率对集群级混进行调整:基于美国医院电子健康记录数据的参数和等离子模拟研究
Mike Du1, Stephen Johnston2, Paul M Coplan2
1Pharmaco- and Device Epidemiology Group, Health Data Sciences, Botnar Research Centre, NDORMS, University of Oxford, Oxford, UK.
Pharmacoepidemiology and drug safety
|November 4, 2025
概括
因果森林 (CF) 显示出减少在植入式设备监控中外科医生强烈影响的偏见的希望. 当混杂性显著时,CF的表现优于治疗权重的反向概率 (IPTW).
科学领域:
- 医疗器械安全 医疗器械安全
- 观察性研究方法的方法.
- 因果推断的原因推断是因果推断.
背景情况:
- 由于快速的创新和新的法规,植入式设备的市场后监测至关重要.
- 患者和提供者因素的复杂混使对设备风险和益处的观察研究复杂化.
- 这项研究解决了治疗分配中的混偏见,特别是外科医生的影响.
研究的目的:
- 为了比较因果森林 (CF) 与治疗权重反向概率 (IPTW) 在减少混偏差方面的表现.
- 根据不同程度的外科医生对治疗分配的影响来评估这些方法.
- 评估模拟和真实世界患者数据中的偏差减少.
主要方法:
- 进行了两项蒙特卡洛模拟研究:使用嵌套数据进行参数模拟,并从现实世界的医院数据库中进行等离子模拟.
- 参数模拟涉及10,000个样本大小,患者与的比例不同 (10:1至500:1) 和多层次的混因素.
- 血模拟使用了来自美国PINC AT数据库的9,981名胰腺切除术患者 (2015-2019) 的数据.
主要成果:
- 因果森林 (CF) 在强烈的混下 (OR=2.5) 与治疗权重反向概率 (IPTW) 相比,显示出优越偏差降低.
- 在参数模拟中,CF的平均相对偏差为11.2%,而IPTW的平均偏差为19.9%.
- 在等离子模拟中观察到CF的类似优势,表明不同数据生成机制的稳定性.
结论:
- 因果森林 (CF) 是一种有前途的方法,用于在有强烈集群级混的观察性研究中估计治疗效应.
- 由于CF能够处理复杂的混,特别是外科医生层面的影响,因此它对营销后监测非常有价值.
- 建议进行进一步的研究,以完善CF的方法,并指导其在现实环境中的应用.
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