Bias Identification from an Evidence-Based Perspective:A Big Data Meta-Analysis Procedure Based on Egger's Extension Model
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.
NETL
NSTL
万方数据
维普
