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ZIBGLMM:用双零事件研究进行元分析的零膨胀双变量通用线性混合模型

Lu Li^1,2, Lifeng Lin³, Joseph C Cappelleri⁴

¹Center for Health Analytics and Synthesis of Evidence, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Research synthesis methods

|February 2, 2026

概括

一个新的零膨胀双变量通用线性混合模型 (ZIBGLMM) 准确地估计了元分析 (MA) 中的治疗效应. 这种方法优于零事件研究的现有方法,减少风险比率估计的偏差.

科学领域:

生物统计学生物统计学
流行病学流行病学
医学研究方法学医学研究方法学

背景情况:

超分析 (MA) 面临着双零事件研究 (DZS) 的挑战.
现有的方法,如连续性校正或研究遗漏,可以引入偏差.
标准的双变量通用线性混合模型 (BGLMM) 不考虑DZS的系统差异.

研究的目的:

引入一种新的零膨胀双变通用线性混合模型 (ZIBGLMM),以解决MA的DZS问题.
开发频率主义和贝叶斯式ZIBGLMM实现.
评估ZIBGLMM在估计风险比率方面的表现.

主要方法:

开发了一种具有零通货膨胀组件的双组件有限混合模型 (ZIBGLMM).
实现了ZIBGLMM的频率主义和贝叶斯版本.
通过模拟和案例研究,通过模拟和案例研究,比较ZIBGLMM与BGLMM和传统的两阶段MA,不包括DZS.

主要成果:

与BGLMM和传统MA相比,ZIBGLMM在估计真实效应大小方面表现出更好的表现.
拟议的模型在风险比率估计中产生了较少的偏差.
ZIBGLMM实现了与现有方法相比较的覆盖概率.

结论:

在元分析中,ZIBGLMM有效地处理双零事件研究.
该模型为估计治疗效应,特别是风险比率提供了一个不那么偏见的方法.
ZIBGLMM为涉及零事件数据的元分析提供了一个强大的统计框架.

关键词:

两变的通用线性混合模型.双零事件研究.一般化的线性混合模型.这是一个元分析.零通货膨胀没有通货膨胀

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