Hierarchical Bayesian approaches for detecting inconsistency in network meta-analysis

Hong Zhao, James S Hodges, Haijun Ma, Qi Jiang, Brad Carlin

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Network meta-analysis (NMA), also known as multiple treatment comparisons, is commonly used to incorporate direct and indirect evidence comparing treatments. With recent advances in methods and software, Bayesian approaches to NMA have become quite popular and allow models of previously unanticipated complexity. However, when direct and indirect evidence differ in an NMA, the model is said to suffer from inconsistency. Current inconsistency detection in NMA is usually based on contrast-based (CB) models; however, this approach has certain limitations. In this work, we propose an arm-based random effects model, where we detect discrepancy of direct and indirect evidence for comparing two treatments using the fixed effects in the model while flagging extreme trials using the random effects. We define discrepancy factors to characterize evidence of inconsistency for particular treatment comparisons, which is novel in NMA research. Our approaches permit users to address issues previously tackled via CB models. We compare sources of inconsistency identified by our approach and existing loop-based CB methods using real and simulated datasets and demonstrate that our methods can offer powerful inconsistency detection.

Original languageEnglish (US)
Pages (from-to)3524-3536
Number of pages13
JournalStatistics in Medicine
Volume35
Issue number20
DOIs
StatePublished - Sep 10 2016

Keywords

  • Bayesian analysis
  • inconsistency detection
  • multiple treatment comparisons

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