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 tau2-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 pooled odds ratio, both for size and for power. (...)
Mots-clés Pascal : Espagne, Europe, Epidémiologie, Homme, Santé, Méthodologie, Test signification, Hétérogénéité, Métaanalyse, Modèle statistique
Mots-clés Pascal anglais : Spain, Europe, Epidemiology, Human, Health, Methodology, Significance test, Heterogeneity, Metaanalysis, Statistical model
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 99-0392216
Code Inist : 002B30A01A1. Création : 22/03/2000.