调解分析与病例控制抽样:在二元调解器的存在下识别和估计
Marco Doretti1, Minna Genbäck2, Elena Stanghellini2,3
1Department of Statistics, Computer Science, and Applications, University of Florence, Florence, Italy.
Biometrical journal. Biometrische Zeitschrift
|January 29, 2024
概括
本研究使用分层病例对照 (SCC) 数据处理调解分析中的偏见. 它介绍了在物流模型中准确估计二元结果和调解者的因果调解效应的方法.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 因果推理因果推理
背景情况:
- 分层病例控制 (SCC) 设计在流行病学研究中很常见.
- 在SCC数据中使用二进制结果和调解器的调解分析带来了独特的统计挑战.
- 现有的方法可能无法充分考虑采样设计对调度参数估计的影响.
研究的目的:
- 在SCC数据中采样设计扭曲的情况下,推导和评估估计因果调解效应的方法.
- 将拟议的最大概率 (ML) 和M估计器的性能与现有的基于权重的方法进行比较.
- 提供适用于参数和非参数调解分析的一般策略.
主要方法:
- 在SCC数据中的二次变量对物流模型参数的抽样设计扭曲的推导.
- 开发最大概率 (ML) 和M估计器,用于联合结果-媒介参数向量.
- 模拟研究,以评估自然效应估计的准确性.
- 作为一个说明性的例子,重新分析了德国病例控制数据集.
主要成果:
- 提出的方法可以准确估计因果调解量,即使采用复杂的抽样设计.
- 模拟结果表明ML和M估计在特定场景中优于传统的权重方法.
- 重新分析确定了一种潜在的调解途径,用于在李斯特菌病发病时降低免疫能力.
结论:
- 开发的统计框架有效地纠正了在SCC中介分析中的采样设计偏差.
- ML和M估计为在二元结果/中介设置中估计自然效应提供了强大的替代方案.
- 这种方法提高了分层病例控制研究的因果推断的可靠性.
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