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Improved tests for a random effects meta‐regression with a single covariate

Datos Bibliográficos

ID23329397
AutoresGuido Knapp (TU Dortmund University, autor de correspondencia), Joachim Hartung (TU Dortmund University)
Año2003
Volumen22
Número17
Páginas2693-2710
Fecha de publicación2003-09-15
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaStatistics in Medicine (JOURNAL)
Identificadores de la revistaISSN: 0277-6715 • E-ISSN: 1097-0258
EditorialWiley (PUBLISHER • GB)
DOI10.1002/sim.1482
PMID12939780
OpenAlexW2036756589
IdiomaEN
Citas recibidas200
Referencias citadas15

The explanation of heterogeneity plays an important role in meta‐analysis. The random effects meta‐regression model allows the inclusion of trial‐specific covariates which may explain a part of the heterogeneity. We examine the commonly used tests on the parameters in the random effects meta‐regression with one covariate and propose some new test statistics based on an improved estimator of the variance of the parameter estimates. The approximation of the distribution of the newly proposed tests is based on some theoretical considerations. Moreover, the newly proposed tests can easily be extended to the case of more than one covariate. In a simulation study, we compare the tests with regard to their actual significance level and we consider the log relative risk as the parameter of interest. Our simulation study reflects the meta‐analysis of the efficacy of a vaccine for the prevention of tuberculosis originally discussed in Berkey et al . The simulation study shows that the newly proposed tests are superior to the commonly used test in holding the nominal significance level. Copyright © 2003 John Wiley & Sons, Ltd.

Covariate · Econometrics · Estimator · Meta-analysis · Random effects model · Regression · Regression analysis · Statistics · Computer Science · Mathematics · Medicine · Meta-analysis and systematic reviews · Statistical Methods and Bayesian Inference · Statistical Methods in Clinical Trials

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Citas por año9,09
Intervalo de citas2004 - 2026 (23)
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