扩展bootstrap MARS与Lasso组进行异质治疗效果估计
Guanwenqing He1, Ke Wan2, Toshio Shimokawa3
1Department of Medical Data-Science, Graduate School of Medicine, Wakayama Medical University, Wakayama, Japan.
Statistics in medicine
|January 23, 2026
概括
这项研究引入了一种新的收缩因果引导MARS方法,用于从现实数据 (RWD) 估计异质治疗效应 (HTEs). 这种新的方法改进了现有的模型,为精准医学应用提供了更高的准确性.
科学领域:
- 医学数据科学 医学数据科学
- 生物统计学 生物统计学
- 计算生物学 计算生物学
背景情况:
- 来自患者调查和医疗记录的真实世界数据 (RWD) 在医学数据科学中至关重要.
- 从RWD估计异质治疗效应 (HTEs) 对于推进精准医学和为患者子组量身定制治疗至关重要.
- 现有的方法,如包装因果多变量自适应回归线 (BCM) 显示有效性,但有改进的空间.
研究的目的:
- 为了引入一种新的治疗效应模型,收缩因果引导MARS方法.
- 通过使用现实世界数据 (RWD) 增强对异质治疗效应 (HTEs) 的估计.
- 改进现有的因果推理模型在观察性研究中的性能.
主要方法:
- 拟议的方法使用转换结果引导抽样MARS进行基础函数估计.
- 模型优化和参数估计使用组最小绝对收缩和选择操作员 (LASSO) 方法进行.
- 这种方法建立在多变量自适应回归线 (MARS) 的框架之上.
主要成果:
- 模拟表明,与现有方法相比,收缩因果引导MARS方法实现了更好的平均平方误差和偏差.
- 这种新的方法在大多数模拟场景中显示出增强的性能.
- 使用ACTG 175数据集验证了实际适用性.
结论:
- 收缩因果引导MARS方法为观测研究中的HTE估计提供了一种精细的方法.
- 这种方法具有显著的潜力,可以通过使更准确,个性化的治疗决策来改善精准医学.
- 这些发现表明,在临床研究中分析真实世界的数据是有价值的新工具.
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