倾向性得分权重方法用于因果分组分析,具有时间到事件结果
Siyun Yang1, Ruiwen Zhou2, Fan Li3
1Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
Statistical methods in medical research
|August 10, 2023
概括
这项研究引入了因果子组生存分析的倾向性得分权重,这对于时间到事件的结果至关重要. 与物流模型重叠权衡在估计患者子组间因果效应方面表现优越.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 评估特定患者亚组的干预效应对于比较有效性研究至关重要.
- 现有的因果子组分析方法对时间到事件结果有局限性.
- 倾向性得分权重为因果子组生存分析提供了一个有希望的方法.
研究的目的:
- 调查因果分组生存分析的倾向性得分权重方法.
- 引入和估计两个新的因果估计:子组边际危险比率和子组受限平均因果效应.
- 为了比较不同倾向得分模型和权重方案的性能.
主要方法:
- 为子组边际危险比率和子组受限平均因果效应开发了倾向性得分权重估计器.
- 通过分析确定了子组共变量平衡与受限平均因果效应偏差之间的联系.
- 进行了广泛的模拟,比较了后勤回归,随机森林,LASSO和GBM倾向得分模型与反向概率权重和重叠权重.
主要成果:
- 由LASSO选择的包含子组-共变相互作用的后勤模型始终优于其他倾向得分模型.
- 叠加权重通常在共同变量平衡,偏差和方差方面超过了反向概率权重.
- 重叠权重的好处在小子组和重叠不多的情况中最为明显.
结论:
- 倾向性得分权重,特别是重叠权重和精心选择的倾向性得分模型,对于因果子组生存分析是有效的.
- 这些发现提供了可靠的方法来评估预先指定的患者子组的时间到事件结果.
- 应用于"比较管理选择:以PA为中心的子宫纤维瘤RESULTS"研究的方法,以评估肌切除术与子宫切除术对疾病复发的影响.
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