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| Bias Identification from an Evidence-Based Perspective: A Big Data Meta-Analysis Procedure Based on Egger's Extension Model |
| Zhou Wenjie1,2,5, Lin Weijie3, Wei Zhipeng4,5, Yang Kehu4,5 |
1.School of Information Resource Management, Renmin University of China, Beijing 100080
2.Business School of Northwest Normal University, Lanzhou 730070
3.School of Economics and Management, Beijing Jiaotong University, Beijing 100044
4.Evidence-Based Medical Center of School of Basic Medical Sciences of Lanzhou University, Lanzhou 730030
5.Cross-Innovation Laboratory of Evidence-Based Social Science of Lanzhou University, Lanzhou 730030 |
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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. However, the reliability of meta-analysis results is often compromised by biases. Addressing the common issues of selection bias and outcome reporting bias in the process of evidence synthesis, this study aims to extend the model developed by Egger 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 efficiency 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.
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Received: 10 July 2023
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