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Comparison of Heterogeneity and Heterogeneity Interval Estimators in Random-Effects Meta-Analysis

Peter Boedeker

Published 2018 in Unknown venue

ABSTRACT

Meta-analyses are conducted to synthesize the quantitative results of related studies. The random-effects meta-analysis model is based on the assumption that a distribution of true effects exists in the population. This distribution is often assumed to be normal with a mean and variance. The population variance, also called heterogeneity, can be estimated numerous ways. Accurate estimation of heterogeneity is necessary as a description of the distribution and for determining weights applied in the estimation of the summary effect when using inverse-variance weighting. To evaluate a wide range of estimators, we compared 16 estimators (Bayesian and non-Bayesian) of heterogeneity with regard to bias and mean square error over conditions based on reviews of educational and psychological meta-analyses. Three simulation conditions were varied: (a) sample size per meta-analysis, (b) true heterogeneity, and (c) sample size per effect size within each meta-analysis. Confidence or highest density intervals can be calculated for heterogeneity. The heterogeneity estimators that performed best over the widest range of conditions were paired with heterogeneity interval estimators. Interval estimators were evaluated based on coverage probability, interval width, and coverage of the estimated value. The combination of the Paule Manel estimator and Q-Profile interval method is recommended when synthesizing standardized mean difference effect sizes.

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