Alternative measures of between-study heterogeneity in meta-analysis: Reducing the impact of outlying studies

Lifeng Lin, Haitao Chu, James S Hodges

Research output: Contribution to journalArticlepeer-review

64 Scopus citations


Meta-analysis has become a widely used tool to combine results from independent studies. The collected studies are homogeneous if they share a common underlying true effect size; otherwise, they are heterogeneous. A fixed-effect model is customarily used when the studies are deemed homogeneous, while a random-effects model is used for heterogeneous studies. Assessing heterogeneity in meta-analysis is critical for model selection and decision making. Ideally, if heterogeneity is present, it should permeate the entire collection of studies, instead of being limited to a small number of outlying studies. Outliers can have great impact on conventional measures of heterogeneity and the conclusions of a meta-analysis. However, no widely accepted guidelines exist for handling outliers. This article proposes several new heterogeneity measures. In the presence of outliers, the proposed measures are less affected than the conventional ones. The performance of the proposed and conventional heterogeneity measures are compared theoretically, by studying their asymptotic properties, and empirically, using simulations and case studies.

Original languageEnglish (US)
Pages (from-to)156-166
Number of pages11
Issue number1
StatePublished - Mar 1 2017

Bibliographical note

Funding Information:
We thank two anonymous reviewers and the associate editor for many helpful and constructive comments. This research was supported in part by NIAID R21 AI103012 (HC, LL), NIDCR R03 DE024750 (HC), NLM R21 LM012197 (HC), and NIDDK U01 DK106786 (HC).

Publisher Copyright:
© 2016, The International Biometric Society


  • Absolute deviation
  • Heterogeneity
  • I statistic
  • Meta-analysis
  • Outliers


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