The Synthesis of Regression Slopes in Meta-Analysis

TL;DR

This paper addresses the challenge of synthesizing regression slopes in meta-analysis, proposing a multivariate generalized least squares method to improve the accuracy of combining regression results from multiple studies.

Contribution

It introduces a novel multivariate generalized least squares approach for synthesizing regression slopes in meta-analysis, handling complexities of real data sets.

Findings

Proposes a new method for slope synthesis in meta-analysis.

Addresses complexities in combining regression models.

Provides a framework for more accurate meta-analytic slope estimates.

Abstract

Research on methods of meta-analysis (the synthesis of related study results) has dealt with many simple study indices, but less attention has been paid to the issue of summarizing regression slopes. In part this is because of the many complications that arise when real sets of regression models are accumulated. We outline the complexities involved in synthesizing slopes, describe existing methods of analysis and present a multivariate generalized least squares approach to the synthesis of regression slopes.