确定解决混偏见的最佳方法,在估计国家层面政策影响时
Beth Ann Griffin1, Megan S Schuler1, Elizabeth M Stone2
1From the RAND Corporation, Arlington, VA.
Epidemiology (Cambridge, Mass.)
|September 21, 2023
概括
这项研究评估了四种政策评估方法在不同的混条件下. 没有一种单一的方法始终优于其他方法,这凸显了卫生政策研究中需要多样化的分析工具包的需要.
科学领域:
- 卫生政策研究 卫生政策研究
- 计量经济学 计量经济学 计量经济学
- 因果推理因果推理
背景情况:
- 国家层面的政策评估对于理解健康结果至关重要.
- 在观察性研究中,混偏见是一个重大挑战.
- 在政策评估中,较新的分析方法的性能尚未得到充分理解.
研究的目的:
- 为了比较四种政策评估方法在不同混情景下的表现.
- 评估不同程度和类型的混如何影响方法性能.
主要方法:
- 通过分阶段政策采用的数据进行了模拟研究.
- 评估了四种方法:双向固定效应差异差异,自回归模型,增强合成控制和卡拉威-圣安娜差异差异.
- 混场景的规模,性质 (水平与趋势) 和线性各不相同.
主要成果:
- 偏差随着混大小,先前的结果趋势和所有方法的非线性关联而增加.
- 自动回归模型和增强合成控制通常显示较低的根平均平方误差.
- 卡拉威-圣安娜在先前趋势的非线性混方面表现出色;增强的合成控制显示了高覆盖率,表明大标准错误.
结论:
- 没有一个单一的政策评估方法在所有混的场景中始终优于其他方法.
- 研究人员应该考虑一系列的政策评估的方法选择.
- 该研究提供模拟和R包,以帮助选择方法.
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