Network meta-analysis of rare events using the Mantel-Haenszel method

Orestis Efthimiou, Gerta Rücker, Guido Schwarzer, Julian Higgins, Matthias Egger, Georgia Salanti

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

39 Citations (Scopus)
166 Downloads (Pure)


The Mantel-Haenszel method has been used for decades to synthesize data obtained from studies that compare two interventions with respect to a binary outcome. It has been shown to perform better than the inverse-variance method or Peto’s odds ratio when data is sparse. Network meta-analysis (NMA) is increasingly used to compare the safety of medical interventions, synthesising for example data on mortality or serious adverse events. In this setting, sparse data occur often and yet there is to-date no extension of the Mantel-Haenszel method for the case of NMA. In this paper we fill this gap by presenting a Mantel-Haenszel NMA method for odds ratios. Similarly to the pairwise Mantel-Haenszel method, we assume common treatment effects. We implement our approach in R, and we provide freely available, easy-to-use routines. We illustrate our approach using data from two previously published networks. We compare our results to those obtained from three other approaches to NMA: NMA with non-central hypergeometric likelihood, an inverse-variance NMA and a Bayesian NMA with a binomial likelihood. We also perform simulations to assess the performance of our method and compare it with alternative methods. We conclude that our Mantel-Haenszel NMA method offers a reliable approach to the network meta-analysis of binary outcomes, especially in the case or sparse data, and when the assumption of methodological and clinical homogeneity is justifiable.
Original languageEnglish
Pages (from-to)2992-3012
Number of pages21
JournalStatistics in Medicine
Issue number16
Early online date17 Apr 2019
Publication statusPublished - 20 Jul 2019


  • adverse events
  • mixed treatment comparison
  • Multiple treatments meta-analysis
  • rare events
  • rare outcomes


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