Joachim Härtung

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.

Abstract For the meta‐analysis of controlled clinical trials with binary outcome a test statistic for testing an overall treatment effect is proposed, which is based on a refined estimator for the variance of the treatment effect estimator usually used in the random‐effects model of meta‐analysis. In simulation studies it is shown that the proposed test keeps the prescribed significance level much better than the commonly used tests in the fixed‐effects and random‐effects model, respectively. Moreover, when using the test it is not necessary to choose between fixed effects and random effects approaches in advance. The proposed method applies in the same way to the analysis of a controlled multi‐centre study with binary outcome, including a possible interaction between drugs and centres. Copyright © 2001 John Wiley & Sons, Ltd.

For the meta-analysis of controlled clinical trials or epidemiological studies, in which the responses are at least approximately normally distributed, a refined test for the hypothesis of no overall treatment effect is proposed. The test statistic is based on a direct estimation function for the variance of the overall treatment effect estimator. As outcome measures, the absolute and the standardized difference between means are considered. In simulation studies it is shown that the proposed test keeps the prescribed significance level very well in contrast to the commonly used tests in the fixed effects and random effects model, respectively, which can become very liberal. Furthermore, just for using the proposed test it is not necessary to choose between the fixed effects and the random effects approach in advance.

"Statistical Meta-Analysis with Applications presents the necessary statistical methodologies that allow readers to tackle the four main stages of meta-analysis: problem formulation, data collection, data evaluation, and data analysis and interpretation. Combining the authors' expertise on the topic with a wealth of up-to-date information, this book successfully introduces the essential statistical practices for making thorough and accurate discoveries across a wide array of diverse fields, such as business, public health, biostatistics, and environmental studies."--BOOK JACKET.

Dieses Werk ist nicht nur ein umfassendes Lehrbuch der Statistik im klassischen Sinn, sondern zugleich ein Handbuch für jeden, der statistische Probleme im Zusammenhang mit Experiment und Erhebung zu lösen hat. Hier dient es fächerübergreifend dem Mediziner, dem Ingenieur, dem Sozial- und Naturwissenschaftler sowie dem Volks- und Betriebswirt. Die 15. Auflage wurde um das Kapitel "Meta-Analyse zur Kombination von Studien, Experimenten und Prognosen" erweitert, in dem erstmalig auch Time-To-Event-Studien, inhomogene Wirtschaftsprognosen, kreuzkorrelierte Zeitreihen, konkurrierende Labore sowie Pareto-optimale Wertpapierportfolios kombiniert werden.