在比例的元分析中,选择通用线性混合模型的链接函数
Lianne K Siegel1, Milena Silva1, Lifeng Lin2
1Division of Biostatistics, University of Minnesota, Minneapolis, MN, 55455, USA.
Research methods in medicine & health sciences
|December 13, 2024
概括
与传统的两步转换相比,通用线性混合模型 (GLMM) 为比例的元分析提供了一种优越的方法. GLMMs直接模拟数据,考虑研究内部的差异,没有正常的近似或临时校正.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 医学研究 医学研究
背景情况:
- 对比例的元分析通常使用两个步骤的转换 (例如,logit,arcsine),假设正常.
- 这些方法可以通过近似来限制,并且需要对零计数进行校正.
研究的目的:
- 评估通用线性混合模型 (GLMMs) 在比例元分析中的实用性.
- 展示链接函数选择的重要性及其对偏差的影响.
主要方法:
- 直接将GLMM与双项数据相匹配,使用精确的概率.
- 使用Akaike信息标准 (AIC) 进行链接功能选择.
- 评估由链接函数错误规范引入的偏差.
主要成果:
- GLMMs直接模拟比例,考虑研究内部的差异,没有正常近似.
- 链接功能选择显著影响结果;AIC有助于选择.
- 链接函数的错误指定可能会引入偏差,这种偏差可能不会被三明治估计器完全缓解.
结论:
- GLMMs提供了一个强大的替代方案,用于进行比例的元分析的两步方法.
- 仔细选择链接函数对于准确的基于GLMM的元分析至关重要.
- 该研究强调了使用COVID-19发烧流行数据的实际应用.
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