Metaplot: A Novel Stata Graph for Assessing Heterogeneity at a Glance

Abstract

Background: Heterogeneity is usually a major concern in meta-analysis. Although there are some statistical approaches for as­sessing variability across studies, here we present a new approach to heterogeneity using "MetaPlot" that investigate the influ­ence of a single study on the overall heterogeneity.
Methods: MetaPlot is a two-way (x, y) graph, which can be considered as a complementary graphical approach for testing hetero­geneity. This method shows graphically as well as numerically the results of an influence analysis, in which Higgins' I² statistic with 95% (Confidence interval) CI are computed omitting one study in each turn and then are plotted against recipro­cal of standard error (1/SE) or "precision". In this graph, "1/SE" lies on x axis and "I² results" lies on y axe.
Results: Having a first glance at MetaPlot, one can predict to what extent omission of a single study may influence the over­all heterogeneity. The precision on x-axis enables us to distinguish the size of each trial. The graph describes I² statistic with 95% CI graphically as well as numerically in one view for prompt comparison. It is possible to implement MetaPlot for meta-analysis of different types of outcome data and summary measures.
Conclusion: This method presents a simple graphical approach to identify an outlier and its effect on overall heterogeneity at a glance. We wish to suggest MetaPlot to Stata experts to prepare its module for the software.