循证视角下的偏倚识别:基于Egger拓展模型的大数据元分析
Bias Identification from an Evidence-Based Perspective:A Big Data Meta-Analysis Procedure Based on Egger's Extension Model
周文杰 1林伟杰 2魏志鹏 3杨克虎3
作者信息
- 1. 中国人民大学信息资源管理学院,北京 100080;西北师范大学商学院,兰州 730070;兰州大学循证社会科学交叉创新实验室,兰州 730030
- 2. 北京交通大学经济管理学院,北京 100044
- 3. 兰州大学基础医学院循证医学中心,兰州 730030;兰州大学循证社会科学交叉创新实验室,兰州 730030
- 折叠
摘要
证据综合是实现学术研究发现向实践指南制定转化的桥梁,元分析作为证据整合与转化工具,是循证体系建设的核心.然而,由于偏倚的存在,元分析结果的可靠性难以保障.针对循证研究过程中普遍存在的选择偏移和结果报告偏倚,本研究旨在围绕Egger等发展的模型,通过元回归对其加以拓展,并通过数学分解的方法对选择偏移和结果报告偏倚加以有效识别,从而发展出一种用于识别偏倚的新方法.在建立了精确的偏倚识别拓展模型的基础上,本研究使用一组经验研究数据,验证了拓展模型的合理性与科学性.本研究提出的拓展模型有效提高了Egger检验的效率,有助于提升元分析质量、构建和完善科学化的循证社会科学基础理论体系.
Abstract
Research synthesis serves as a bridge between academic research findings and the development of practice guidelines.As a tool for evidence integration and translation,meta-analysis is at the core of evidence-based practice.How-ever,the reliability of meta-analysis results is often compromised by biases.Addressing the common issues of selection bi-as and outcome reporting bias in the process of evidence synthesis,this study aims to extend the model developed by Egg-er and others through meta-regression.It employs a mathematical decomposition method to effectively identify selection bias and outcome reporting bias,thus developing a new approach for bias identification.Building upon the establishment of an accurate bias identification extension model,this study further validates the rationality and scientific validity of the extended model using a set of empirical research data.The developed extension model significantly enhances the efficien-cy of Egger's test,contributes to improving the quality of meta-analysis,and aids in the construction and refinement of a scientific evidence-based social science theoretical framework.
关键词
偏倚识别/Egger拓展模型/元分析/证据综合Key words
bias identification/Egger extension model/meta-analysis/research synthesis引用本文复制引用
基金项目
国家社会科学基金重大项目(19ZDA142)
出版年
2024
NETL
NSTL
维普
万方数据