American Journal of Epidemiology Vol. 150, No. 2: 206-215
Copyright © 1999 by The Johns Hopkins University School of Hygiene and Public Health
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Evaluation of Old and New Tests of Heterogeneity in Epidemiologic Meta-Analysis
1 Department of Preventive Medicine, School of Medicine, University of Santiago de Compostela Santiago de Compostela, Spain
2 Department of Biostatistics, School of Medicine, University of Santiago de Compostela Santiago de Compostela, Spain
3 Departments of Epidemiology and Biostatistics, Harvard School of Public Health Boston, MA
The identification of heterogeneity in effects between studies is a key issue in meta-analyses of observational studies, since it is critical for determining whether it is appropriate to pool the individual results into one summary measure. The result of a hypothesis test is often used as the decision criterion. In this paper, the authors use a large simulation study patterned from the key features of five published epidemiologic meta-analyses to investigate the type I error and statistical power of five previously proposed asymptotic homogeneity tests, a parametric bootstrap version of each of the tests, and t2-bootstrap, a test proposed by the authors. The results show that the asymptotic DerSimonian and Laird Q statistic and the bootstrap versions of the other tests give the correct type I error under the null hypothesis but that all of the tests considered have low statistical power, especially when the number of studies included in the meta-analysis is small (<20). From the point of view of validity, power, and computational ease, the Q statistic is clearly the best choice. The authors found that the performance of all of the tests considered did not depend appreciably upon the value of the value of the pooled odds ratio, both for size and for power. Because tests for heterogeneity will often be underpowered, random effects models can be used routinely, and heterogeneity can be quantified by means of RI', the proportion of the total variance of the pooled effect measure due to between-study variance, and CVB', the between-study coefficient of variation. Am J Epidemiol 1999;150:20615.
epidemiologic methods; heterogeneity; meta-analysis; models, statistical; significance tests
Reprint requests to Dr. Bahi Takkouche, Facultad de Medicina, Area de Medicina Preventiva, Universidad de Santiago de Compostela, 15705 Santiago de Compostela, Spain.
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