Sensitivity of treatment recommendations to bias in network meta-analysis

David Phillippo, Sofia Dias, Tony Ades, Vanessa Didelez, Nicky J. Welton

Research output: Contribution to journalArticlepeer-review


Network meta-analysis (NMA) pools evidence on multiple treatments to estimate relative treatment effects. Included studies are typically assessed for risk of bias; however, this provides no indication of the impact of potential bias on a decision based on the NMA. We propose methods to derive bias adjustment thresholds which measure the smallest changes to the data that result in a change of treatment decision. The methods use efficient matrix operations and can be applied to explore the consequences of bias in individual studies or aggregate treatment contrasts, in both fixed and random-effects NMA models. Complex models with multiple types of data input are handled by using an approximation to the hypothetical aggregate likelihood. The methods are illustrated with a simple NMA of thrombolytic treatments and a more complex example comparing social anxiety interventions. An accompanying R package is provided.
Original languageUndefined/Unknown
Pages (from-to)843-867
Number of pages25
JournalJournal of the Royal Statistical Society: Series A (Statistics in Society)
Issue number3
Early online date6 Dec 2017
Publication statusPublished - 1 Jun 2018

Bibliographical note

© 2017 The Authors Journal of the Royal Statistical Society: Series A (Statistics in Society) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society.


  • Evidence synthesis, Influence matrix, Mixed treatment comparison, Quality of evidence, Risk of bias, Treshold analysis

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