Comparative performance of heterogeneity variance estimators in meta-analysis: A review of simulation studies

Dean Langan*, Julian P T Higgins, Mark Simmonds

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Random-effects meta-analysis methods include an estimate of between-study heterogeneity variance. We present a systematic review of simulation studies comparing the performance of different estimation methods for this parameter. We summarise the performance of methods in relation to estimation of heterogeneity and of the overall effect estimate, and of confidence intervals for the latter. Among the twelve included simulation studies, the DerSimonian and Laird method was most commonly evaluated. This estimate is negatively biased when heterogeneity is moderate to high and therefore most studies recommended alternatives. The Paule-Mandel method was recommended by three studies: it is simple to implement, is less biased than DerSimonian and Laird and performs well in meta-analyses with dichotomous and continuous outcomes. In many of the included simulation studies, results were based on data that do not represent meta-analyses observed in practice, and only small selections of methods were compared. Furthermore, potential conflicts of interest were present when authors of novel methods interpreted their results. On the basis of current evidence, we provisionally recommend the Paule-Mandel method for estimating the heterogeneity variance, and using this estimate to calculate the mean effect and its 95% confidence interval. However, further simulation studies are required to draw firm conclusions.

Original languageEnglish
Number of pages18
JournalResearch Synthesis Methods
Early online date6 Apr 2016
Publication statusE-pub ahead of print - 6 Apr 2016


  • DerSimonian-Laird
  • Heterogeneity
  • Meta-analysis
  • Random effects
  • Simulation

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