dc.creator | Ioannidis, J. P. A. | en |
dc.creator | Trikalinos, T. A. | en |
dc.creator | Zintzaras, E. | en |
dc.date.accessioned | 2015-11-23T10:30:25Z | |
dc.date.available | 2015-11-23T10:30:25Z | |
dc.date.issued | 2006 | |
dc.identifier | 10.1016/j.jclinepi.2006.02.013 | |
dc.identifier.issn | 0895-4356 | |
dc.identifier.uri | http://hdl.handle.net/11615/28594 | |
dc.description.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. (c) 2006 Elsevier Inc. All rights reserved. | en |
dc.source | Journal of Clinical Epidemiology | en |
dc.source.uri | <Go to ISI>://WOS:000241064500003 | |
dc.subject | meta-analysis | en |
dc.subject | heterogeneity | en |
dc.subject | homogeneity | en |
dc.subject | bias | en |
dc.subject | Monte Carlo | en |
dc.subject | risk ratio | en |
dc.subject | 2 X-2 TABLES | en |
dc.subject | CRITICALLY-ILL | en |
dc.subject | VOLUME THERAPY | en |
dc.subject | RANDOMIZED-TRIAL | en |
dc.subject | DUPLICATE PUBLICATION | en |
dc.subject | TESTS | en |
dc.subject | HETEROGENEITY | en |
dc.subject | DIFFERENCE | en |
dc.subject | STUDIES/ | en |
dc.subject | RATIO | en |
dc.subject | Health Care Sciences & Services | en |
dc.subject | Public, Environmental & Occupational | en |
dc.subject | Health | en |
dc.title | Extreme between-study homogeneity in meta-analyses could offer useful insights | en |
dc.type | journalArticle | en |