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检查顺序正面性的违规行为是否让你失望? 试试体育吧!

Arthur Chatton1, Michael Schomaker2,3, Miquel-Angel Luque-Fernandez4,5

  • 1From the Faculté de Pharmacie, Université de Montréal, Montréal, QC, Canada.

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顺序的积极性回归树 (sPoRT) 算法识别出违反因果推断的顺序积极性假设的子组. 这种方法有助于在观察性研究中了解违规行为并找到解决方案.

关键词:
因果推断数据支持动态处理方式纵向研究积极的态度回归树

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科学领域:

  • 流行病学
  • 生物统计学
  • 因果推理

背景情况:

  • 顺序的阳性对于因果推断至关重要,但难以验证.
  • 现有的方法依赖于可能错误的倾向得分模型.
  • 在观察性研究中,顺序正值的违反可能会导致结果偏差.

研究的目的:

  • 引入一个新的算法,即顺序正态回归树 (sPoRT),用于验证顺序正态假设.
  • 识别违反顺序积极性的子组,并了解这些违反的性质.
  • 为处理积极性问题提供实用解决方案和见解.

主要方法:

  • 开发了顺序正态回归树 (sPoRT) 算法.
  • sPoRT提供了静态/动态处理策略的版本,随着时间的推移分层/共享.
  • 使用现实世界HIV治疗时间研究证明sPoRT.

主要成果:

  • sPoRT算法易于使用,并为流行病学家提供可解释的结果.
  • 确定了表现出阳性违规性的特定子组.
  • 有一个R笔记本可用于实践实施sPoRT.

结论:

  • sPoRT有效地识别了违反顺序阳性的可解释子组.
  • 容易识别混因素的模式和趋势.
  • 为解决因果推理中的积极性违规提供实用建议.