机器学习与逻辑回归用于倾向得分估计:与PARADIGM-HF随机试验进行基准测试的试验仿真
Kaicheng Wang1,2, Lindsey Rosman3, Haidong Lu4,5
1Yale Center for Analytical Sciences, Yale School of Public Health, New Haven, CT, USA. kaicheng_wang@med.unc.edu.
机器学习倾向分数并不能改善因果推理. 传统的后勤回归与仔细的混者选择产生了比ML方法更准确的结果,特别是那些自动化特征选择的方法.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 机器学习 (ML) 越来越多地用于倾向性得分估计,以增强因果推理.
- 数据驱动的ML方法对混因素选择和因果推理调整的有效性仍然不确定.
研究的目的:
- 以基于ML的倾向得分方法与因果推断的传统逻辑回归进行基准比较.
- 评估ML倾向得分模型中自动化特征选择的影响.
主要方法:
- 模拟了PARADIGM-HF试验的二次分析,使用美国退伍军人 (2016-2020) 的观察数据.
- 我们比较了三种倾向性评分方法:逻辑回归 (预先指定混因子),用预先指定混因子推广增强模型 (GBM) 和使用扩展共变量和自动特征选择的GBM.
主要成果:
- 后勤回归产生了与试验结果最接近的估计.
- 使用预先指定的混因子的GBM与后勤回归相比没有任何改善.
- 具有自动特征选择的GBM引入了实质性的偏差,增加了估计错误.
结论:
- 基于ML的倾向评分方法本身并不能改善因果估计.
- 在ML模型中的自动化特征选择可能会引入过度调整偏差.
- 仔细的混器规范和因果推理对于可靠的因果推理至关重要,超过了算法复杂性.
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