The use of single armed observational data to closing the gap in otherwise disconnected evidence networks: A network meta-analysis in multiple myeloma

Susanne Schmitz*, Áine Maguire, James Morris, Kai Ruggeri, Elisa Haller, Isla Kuhn, Joy Leahy, Natalia Homer, Ayesha Khan, Jack Bowden, Vanessa Buchanan, Michael O'Dwyer, Gordon Cook, Cathal Walsh

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

24 Citations (Scopus)


Background: Network meta-analysis (NMA) allows for the estimation of comparative effectiveness of treatments that have not been studied in head-to-head trials; however, relative treatment effects for all interventions can only be derived where available evidence forms a connected network. Head-to-head evidence is limited in many disease areas, regularly resulting in disconnected evidence structures where a large number of treatments are available. This is also the case in the evidence of treatments for relapsed or refractory multiple myeloma. Methods: Randomised controlled trials (RCTs) identified in a systematic literature review form two disconnected evidence networks. Standard Bayesian NMA models are fitted to obtain estimates of relative effects within each network. Observational evidence was identified to fill the evidence gap. Single armed trials are matched to act as each other's control group based on a distance metric derived from covariate information. Uncertainty resulting from including this evidence is incorporated by analysing the space of possible matches. Results: Twenty five randomised controlled trials form two disconnected evidence networks; 12 single armed observational studies are considered for bridging between the networks. Five matches are selected to bridge between the networks. While significant variation in the ranking is observed, daratumumab in combination with dexamethasone and either lenalidomide or bortezomib, as well as triple therapy of carfilzomib, ixazomib and elozumatab, in combination with lenalidomide and dexamethasone, show the highest effects on progression free survival, on average. Conclusions: The analysis shows how observational data can be used to fill gaps in the existing networks of RCT evidence; allowing for the indirect comparison of a large number of treatments, which could not be compared otherwise. Additional uncertainty is accounted for by scenario analyses reducing the risk of over confidence in interpretation of results.

Original languageEnglish
Article number66
JournalBMC Medical Research Methodology
Issue number1
Publication statusPublished - 28 Jun 2018


  • Evidence synthesis
  • Network meta-analysis
  • Relapsed or refractory myeloma
  • Single armed studies


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