没有积极性的可转移性:统计和模拟建模的综合
Paul N Zivich1,2, Jessie K Edwards2, Eric T Lofgren3
1From the Institute of Global Health and Infectious Diseases, University of North Carolina at Chapel Hill, Chapel Hill, NC.
因果推理的可转移性方法通常由于正面性违反而失败. 使用统计和模拟模型的新综合方法有效地解决了这些违规行为,改善了公共卫生决策.
科学领域:
- 在观察性和随机研究中,因果推断和可转移性.
- 统计建模和模拟用于解决现实世界的数据限制.
背景情况:
- 来自研究的估计通常需要将其概括为在样本中不完全代表的目标人群.
- 可转移性方法通常假定是积极的,这意味着所有目标人群的共同变量模式都存在于研究样本.
- 严格的资格标准在试验中可能会违反积极性假设,限制概括性.
研究的目的:
- 在对目标人群进行研究估计的概括时,解决因果推断中对积极性假设的违反问题.
- 提出和评估一种新的综合方法,将统计和模拟模型结合起来,作为人口或共变量限制的替代方案.
主要方法:
- 开发了一种综合方法,整合统计和模拟模型,以克服积极性违规.
- 在合成框架内提出的g计算和反向概率权重估计器.
- 使用模拟实验和现实世界的例子,将合成方法与传统的限制方法进行了比较.
主要成果:
- 拟议的综合方法准确地解决了模拟和说明性示例中的研究问题.
- 限制目标人群或共变量集的传统方法未能准确地解决激励研究问题的问题.
- 与限制方法相比,合成方法在处理积极性违规方面表现优越.
结论:
- 模型综合提供了一个可行的策略,因果推理当面对不完美的目标人群信息和积极性违规.
- 通过精心挑选的模拟模型将经验数据与外部知识相结合,对于成功的可运输性至关重要.
- 这种方法提高了基于研究结果的公共卫生决策的可靠性.
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