Extreme between-study homogeneity in meta-analyses could offer useful insights
| dc.contributor.author | Ioannidis, J. P. | en |
| dc.contributor.author | Trikalinos, T. A. | en |
| dc.contributor.author | Zintzaras, E. | en |
| dc.date.accessioned | 2015-11-24T19:16:21Z | |
| dc.date.available | 2015-11-24T19:16:21Z | |
| dc.identifier.issn | 0895-4356 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/21650 | |
| dc.rights | Default Licence | - |
| dc.subject | Databases, Bibliographic | en |
| dc.subject | Epidemiologic Methods | en |
| dc.subject | Humans | en |
| dc.subject | *Meta-Analysis as Topic | en |
| dc.subject | Research Design | en |
| dc.subject | Review Literature as Topic | en |
| dc.subject | Scientific Misconduct | en |
| dc.title | Extreme between-study homogeneity in meta-analyses could offer useful insights | en |
| heal.abstract | 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. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.primary | 10.1016/j.jclinepi.2006.02.013 | - |
| heal.identifier.secondary | http://www.ncbi.nlm.nih.gov/pubmed/16980141 | - |
| heal.identifier.secondary | http://ac.els-cdn.com/S0895435606001363/1-s2.0-S0895435606001363-main.pdf?_tid=096785cda84448f6d2085cb46d41a022&acdnat=1333364197_6a0aad50f2569395bd88bccc0d17af0e | - |
| heal.journalName | J Clin Epidemiol | en |
| heal.journalType | peer-reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2006 | - |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Επιστημών Υγείας. Τμήμα Ιατρικής | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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