Extending DerSimonian and Laird's methodology to perform network meta-analyses with random inconsistency effects

Dan Jackson*, Martin Law, Jessica K. Barrett, Rebecca Turner, Julian Pt Higgins, Georgia Salanti, Ian R. White

*Corresponding author for this work

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

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Abstract

Network meta-analysis is becoming more popular as a way to compare multiple treatments simultaneously. Here, we develop a new estimation method for fitting models for network meta-analysis with random inconsistency effects. This method is an extension of the procedure originally proposed by DerSimonian and Laird. Our methodology allows for inconsistency within the network. The proposed procedure is semi-parametric, non-iterative, fast and highly accessible to applied researchers. The methodology is found to perform satisfactorily in a simulation study provided that the sample size is large enough and the extent of the inconsistency is not very severe. We apply our approach to two real examples.
Original languageEnglish
Pages (from-to)819-839
Number of pages21
JournalStatistics in Medicine
Volume35
Issue number6
Early online date30 Sep 2015
DOIs
Publication statusPublished - 15 Mar 2016

Structured keywords

  • ConDuCT-II

Keywords

  • Method of moments
  • Mixed treatment comparisons
  • Multiple treatments meta-analysis
  • Network meta-analysis

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