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American Journal of Epidemiology Advance Access originally published online on December 8, 2008
American Journal of Epidemiology 2009 169(2):249-255; doi:10.1093/aje/kwn340
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American Journal of Epidemiology © The Author 2008. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

PRACTICE OF EPIDEMIOLOGY

More Than Numbers: The Power of Graphs in Meta-Analysis

Leon Bax, Noriaki Ikeda, Naohito Fukui, Yukari Yaju, Harukazu Tsuruta and Karel G. M. Moons

Correspondence to Dr. Leon Bax, Kitasato Clinical Research Center, Kitasato University, 1-15-1 Kitasato, Sagamihara, 228-8555 Kanagawa, Japan (e-mail: leonbax{at}kitasato-crc.org).

Received for publication March 17, 2008. Accepted for publication July 25, 2008.

In meta-analysis, the assessment of graphs is widely used in an attempt to identify or rule out heterogeneity and publication bias. A variety of graphs are available for this purpose. To date, however, there has been no comparative evaluation of the performance of these graphs. With the objective of assessing the reproducibility and validity of graph ratings, the authors simulated 100 meta-analyses from 4 scenarios that covered situations with and without heterogeneity and publication bias. From each meta-analysis, the authors produced 11 types of graphs (box plot, weighted box plot, standardized residual histogram, normal quantile plot, forest plot, 3 kinds of funnel plots, trim-and-fill plot, Galbraith plot, and L'Abbé plot), and 3 reviewers assessed the resulting 1,100 plots. The intraclass correlation coefficients (ICCs) for reproducibility of the graph ratings ranged from poor (ICC = 0.34) to high (ICC = 0.91). Ratings of the forest plot and the standardized residual histogram were best associated with parameter heterogeneity. Association between graph ratings and publication bias (censorship of studies) was poor. Meta-analysts should be selective in the graphs they choose for the exploration of their data.

epidemiologic methods; evaluation studies; meta-analysis; publication bias; review

Abbreviations: ICC, intraclass correlation coefficient


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