Getting more out of meta-analyses: a new approach to meta-analysis in light of unexplained heterogeneity

Amit Saad*, Daniel Yekutieli, Shaul Lev-Ran, Raz Gross, Gordon Guyatt

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Background and Objectives: Meta-analyses sometimes summarize results in the presence of substantial unexplained between-study heterogeneity. As GRADE criteria highlight, unexplained heterogeneity reduces certainty in the evidence, resulting in limited confidence in average effect estimates. The aim of this paper is to provide a new clinically useful approach to estimating an intervention effect in light of unexplained heterogeneity. Methods: We used a random-effects model to estimate the distribution of an intervention-effect across various groups of patients given data derived from meta-analysis. The model provides a distribution of the probabilities of various possible effects in a new group of patients. We examined how our method influenced the conclusions of two meta-analyses. Results: In one example, our method illustrated that evidence from a meta-analysis did not support authors’ highly publicized conclusion that hypericum is as effective as other antidepressants. In the second example, our method provided insight into a subgroup analysis of the effect of ribavirin in hepatitis C, demonstrating clear important benefit in one subgroup but not in others. Conclusion: Analysing the distribution of an intervention-effect in random-effects models may enable clinicians to improve their understanding of the probability of particular-intervention effects in a new population.

Original languageEnglish
Pages (from-to)101-106
Number of pages6
JournalJournal of Clinical Epidemiology
StatePublished - Mar 2019


  • Heterogeneity
  • I² statistic, between study variance
  • Meta-analyses
  • Random-effects models
  • Systematic reviews


Dive into the research topics of 'Getting more out of meta-analyses: a new approach to meta-analysis in light of unexplained heterogeneity'. Together they form a unique fingerprint.

Cite this