Detecting outlying trials in network meta-analysis

Jing Zhang, Haoda Fu, Bradley P. Carlin

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Network meta-analysis (NMA) expands the scope of a conventional pairwise meta-analysis to simultaneously handle multiple treatment comparisons. However, some trials may appear to deviate markedly from the others and thus be inappropriate to be synthesized in the NMA. In addition, the inclusion of these trials in evidence synthesis may lead to bias in estimation. We call such trials trial-level outliers. To the best of our knowledge, while heterogeneity and inconsistency in NMA have been extensively discussed and well addressed, few previous papers have considered the proper detection and handling of trial-level outliers. In this paper, we propose several Bayesian outlier detection measures, which are then applied to a diabetes data set. Simulation studies comparing our approaches in both arm-based and contrast-based model settings are provided in two supporting appendices.

Original languageEnglish (US)
Pages (from-to)2695-2707
Number of pages13
JournalStatistics in Medicine
Volume34
Issue number19
DOIs
StatePublished - Aug 30 2015

Keywords

  • Detection measures
  • Network meta-analysis
  • Trial-level outliers

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