期刊
JOURNAL OF CLINICAL EPIDEMIOLOGY
卷 59, 期 10, 页码 1023-1032出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jclinepi.2006.02.013
关键词
meta-analysis; heterogeneity; homogeneity; bias; Monte Carlo; risk ratio
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.
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