Estimating heterogeneity variance in meta‐analysis

By Andrew L. Rukhin. 2013

64 citations recorded by the source corpus.

Summary. Several new estimators of the between‐study variability in a heterogeneous random effects meta‐analysis model are derived. One is the unbiased statistic which is locally optimal for small values of the parameter. Others are Bayes procedures within a class of quadratic statistics for a diffuse prior with different choices of the prior mean. These estimators are compared with the DerSimonian–Laird procedure and the Hedges statistic in particular via the quadratic risk of the treatment effect estimator. A Monte Carlo study supports the usage of confidence intervals derived from the new estimators.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c9a450bde8ed4f607961455e361d876118ba9ce3

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Citations in the corpus

Methods to estimate the between‐study variance and its uncertainty in meta‐analysis† (2015, methodology): “However, the need to truncate negative values to zero introduces positive bias into the estimator (Rukhin, 2013; Viechtbauer, 2005) and consequently the DL estimator is positively biased, over-estimating the true amount of between-study variance on average.”
A comparison of heterogeneity variance estimators in simulated random‐effects meta‐analyses (2018, methodology): “The same estimator has been derived independently of the methods of moments approach and is therefore often referred to as the empirical Bayes estimator in the literature.(26) 4.”