A graphical method for exploring heterogeneity in meta-analyses: application to a meta-analysis of 65 trials

Abstract

Heterogeneity can be a major component of meta-analyses and by virtue of that fact warrants investigation. Classic analysis methods, such as meta-regression, are used to explore the sources of heterogeneity. However, it may be difficult to apply such a method in complex cases or in the absence of an a priori hypothesis. This paper presents a graphical method to identify trials, groups of trials or groups of patients that are sources of heterogeneity. The contribution of these trials to the overall result can also be evaluated with this method. Each trial is represented by a dot on a 2D graph. The X-axis represents the contribution of the trial to the overall Cochran Q-test for heterogeneity. The Y-axis represents the influence of the trial, defined as the standardized squared difference between the treatment effects estimated with and without the trial. This approach has been applied to data from the Meta-Analysis of Chemotherapy in Head and Neck Cancer (MACH-NC) comprising 10,850 patients in 65 randomized trials. The graphical method allowed us to identify trials that contributed considerably to the overall heterogeneity and had a strong influence on the overall result. It also provided useful information for the interpretation of heterogeneity in this meta-analysis. The proposed graphical method identifies trials that account for most of the heterogeneity without having to explore all possible sources of heterogeneity by subgroup analyses. This method can also be applied to identify types of patients that explain heterogeneity in the treatment effect.