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Estimating Heterogeneity Variance in Meta-Analysis

Published

Author(s)

Andrew L. Rukhin

Abstract

Several new estimators of the between-study variability in a heterogeneous random effects meta-analysis model are derived. One of them can be interpreted as the empirical Bayes procedure for a diffuse prior with the given prior mean. Another is the unbiased estimator which is locally optimal for small values of the parameter. These procedures are compared to the traditional DerSimonian-Laird procedure and the Hedges estimator by means of their mean absolute error, as well as by the quadratic risk of the treatment effect. Confidence intervals are derived by using these estimators and studied via a Monte Carlo study which supports their usage.
Citation
Journal of the Royal Statistical Society Series B-Statistical Methodology

Keywords

Confidence intervals, DerSimonian-Laird estimator, diffuse prior, empirical Bayes approach, heteroscedasticity, random effects model, unbiased estimators.

Citation

Rukhin, A. (2012), Estimating Heterogeneity Variance in Meta-Analysis, Journal of the Royal Statistical Society Series B-Statistical Methodology (Accessed April 18, 2024)
Created August 6, 2012, Updated February 19, 2017