在修改Poisson回归和log-binomial回归之间做出选择:在异质风险比率下证明修改Poisson回归在异质风险比率下的优越性
Kota Sawada1, Yasuhiro Hagiwara2, Yutaka Matsuyama2
1Laboratory of Biostatistics, Department of Data Science, Center for Clinical Sciences, Japan Institute for Health Security, Tokyo, Japan.
American journal of epidemiology
|October 20, 2025
概括
修改Poisson回归提供更可靠的风险比率估计比日志-二项式回归,当暴露-结果协会在共变量之间有所不同. 这对于准确的流行病学研究至关重要,特别是对于复杂的数据.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 统计建模 统计建模
背景情况:
- 逻辑回归对于暴露-结果关联是常见的,但对于估计风险比率来说,更喜欢日志-二项式和修改的波桑回归.
- 之前的研究经常假设均的暴露-结果关联,这可能不反映现实世界的流行病学数据.
- 异质性对日志双项与修改的波桑回归的表现的影响仍然不清楚.
研究的目的:
- 在估计标准化风险比率时,比较日志双项和修改的波桑回归的性能.
- 研究暴露结果关联中异质性对这些回归方法的影响.
- 为在具有潜在异质性的流行病学研究中选择适当的回归模型提供指导.
主要方法:
- 对于日系双项和修改的波桑回归的估计程序的理论检查.
- 模拟研究,以评估不同程度的异质性和共变量-暴露关联下的性能.
- 在怀疑异质性的乳腺癌流行病学研究中应用方法.
主要成果:
- 修改Poisson回归在大多数场景中提供了实际有效的标准化风险比率估计.
- 逻辑-二项式回归只在同质或轻微异质关联下产生可解释的标准化风险比率.
- 修改Poisson回归的有效性只有当风险比异质性和共变量-暴露关联都很强时才会受到影响.
结论:
- 当风险比率存在异质性时,修改Poisson回归通常优先于日志-二项式回归.
- 修改Poisson回归在更广泛的流行病学背景下提供了标准化风险比率的更强大和实际有效的估计器.
- 这些发现支持使用修改后的Poisson回归来准确估计复杂的流行病学数据中的风险比率.
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