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

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)2992-3012
Number of pages21
JournalStatistics in Medicine
Volume38
Issue number16
Early online date17 Apr 2019
DOIs
DateAccepted/In press - 14 Mar 2019
DateE-pub ahead of print - 17 Apr 2019
DatePublished (current) - 20 Jul 2019

Abstract

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.

    Research areas

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

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Wiley at https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8158. Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 667 KB, PDF document

    Embargo ends: 17/04/20

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