Extreme between-study homogeneity in meta-analyses could offer useful insights

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Ioannidis, J. P.
Trikalinos, T. A.
Zintzaras, E.

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peer-reviewed

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J Clin Epidemiol

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OBJECTIVES: Meta-analyses are routinely evaluated for the presence of large between-study heterogeneity. We examined whether it is also important to probe whether there is extreme between-study homogeneity. STUDY DESIGN: We used heterogeneity tests with left-sided statistical significance for inference and developed a Monte Carlo simulation test for testing extreme homogeneity in risk ratios across studies, using the empiric distribution of the summary risk ratio and heterogeneity statistic. A left-sided P=0.01 threshold was set for claiming extreme homogeneity to minimize type I error. RESULTS: Among 11,803 meta-analyses with binary contrasts from the Cochrane Library, 143 (1.21%) had left-sided P-value <0.01 for the asymptotic Q statistic and 1,004 (8.50%) had left-sided P-value <0.10. The frequency of extreme between-study homogeneity did not depend on the number of studies in the meta-analyses. We identified examples where extreme between-study homogeneity (left-sided P-value <0.01) could result from various possibilities beyond chance. These included inappropriate statistical inference (asymptotic vs. Monte Carlo), use of a specific effect metric, correlated data or stratification using strong predictors of outcome, and biases and potential fraud. CONCLUSION: Extreme between-study homogeneity may provide useful insights about a meta-analysis and its constituent studies.

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Databases, Bibliographic, Epidemiologic Methods, Humans, *Meta-Analysis as Topic, Research Design, Review Literature as Topic, Scientific Misconduct

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http://www.ncbi.nlm.nih.gov/pubmed/16980141
http://ac.els-cdn.com/S0895435606001363/1-s2.0-S0895435606001363-main.pdf?_tid=096785cda84448f6d2085cb46d41a022&acdnat=1333364197_6a0aad50f2569395bd88bccc0d17af0e

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en

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Πανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικής

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