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The diamond ratio: A visual indicator of the extent of heterogeneity in meta‐analysis
Author(s) -
Cairns Maxwell,
Cumming Geoff,
CalinJageman Robert,
Prendergast Luke A.
Publication year - 2022
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/bmsp.12258
Subject(s) - meta analysis , forest plot , statistics , plot (graphics) , diamond , measure (data warehouse) , confidence interval , random effects model , econometrics , code (set theory) , publication bias , study heterogeneity , mathematics , interval (graph theory) , computer science , data mining , combinatorics , medicine , chemistry , organic chemistry , set (abstract data type) , programming language
The result of a meta‐analysis is conventionally pictured in the forest plot as a diamond, whose length is the 95% confidence interval (CI) for the summary measure of interest. The Diamond Ratio (DR) is the ratio of the length of the diamond given by a random effects meta‐analysis to that given by a fixed effect meta‐analysis. The DR is a simple visual indicator of the amount of change caused by moving from a fixed‐effect to a random‐effects meta‐analysis. Increasing values of DR greater than 1.0 indicate increasing heterogeneity relative to the effect variances. We investigate the properties of the DR, and its relationship to four conventional but more complex measures of heterogeneity. We propose for the first time a CI on the DR, and show that it performs well in terms of coverage. We provide example code to calculate the DR and its CI, and to show these in a forest plot. We conclude that the DR is a useful indicator that can assist students and researchers to understand heterogeneity, and to appreciate its extent in particular cases.