Methods to estimate the between-study variance and its uncertainty in meta-analysis

Areti Angeliki Veroniki*, Dan Jackson, Wolfgang Viechtbauer, Ralf Bender, Jack Bowden, Guido Knapp, Oliver Kuss, Julian P T Higgins, Dean Langan, Georgia Salanti

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

315 Citations (Scopus)
719 Downloads (Pure)


Meta-analyses are typically used to estimate the overall/mean of an outcome of interest. However, inference about between-study variability, which is typically modelled using a between-study variance parameter, is usually an additional aim. The DerSimonian and Laird method, currently widely used by default to estimate the between-study variance, has been long challenged. Our aim is to identify known methods for estimation of the between-study variance and its corresponding uncertainty, and to summarise the simulation and empirical evidence that compares them. We identified 16 estimators for the between-study variance, seven methods to calculate confidence intervals, and several comparative studies. Simulation studies suggest that for both dichotomous and continuous data the estimator proposed by Paule and Mandel and for continuous data the restricted maximum likelihood estimator are better alternatives to estimate the between-study variance. Based on the scenarios and results presented in the published studies, we recommend the Q-profile method and the alternative approach based on a 'generalised Cochran between-study variance statistic' to compute corresponding confidence intervals around the resulting estimates. Our recommendations are based on a qualitative evaluation of the existing literature and expert consensus. Evidence-based recommendations require an extensive simulation study where all methods would be compared under the same scenarios.

Original languageEnglish
Pages (from-to)55-79
Number of pages25
JournalResearch Synthesis Methods
Issue number1
Early online date2 Sep 2015
Publication statusPublished - 1 Mar 2016


  • Bias
  • Confidence interval
  • Coverage probability
  • Heterogeneity
  • Mean squared error

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