Borrowing of strength from indirect evidence in 40 network meta-analyses

Lifeng Lin, Aiwen Xing, Michael J. Kofler, Mohammad Hassan Murad

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Objectives: Network meta-analysis (NMA) is increasingly being used to synthesize direct and indirect evidence and help decision makers simultaneously compare multiple treatments. We empirically evaluate the incremental gain in precision achieved by incorporating indirect evidence in NMAs. Study Design and Setting: We performed both network and pairwise meta-analyses on 40 published data sets of multiple-treatment comparisons. Their results were compared using the recently proposed borrowing of strength (BoS) statistic, which quantifies the percentage reduction in the uncertainty of the effect estimate when adding indirect evidence to an NMA. Results: We analyzed 915 possible treatment comparisons, from which 484 (53%) had no direct evidence (BoS = 100%). In 181 comparisons with only one study contributing direct evidence, NMAs resulted in reduced precision (BoS < 0) and no appreciable improvements in precision (0 < BoS < 30%) for 104 (57.5%) and 23 (12.7%) comparisons, respectively. In 250 comparisons with at least two studies contributing direct evidence, NMAs provided increased precision with BoS ≥ 30% for 166 (66.4%) comparisons. Conclusion: Although NMAs have the potential to provide more precise results than those only based on direct evidence, the incremental gain may reliably occur only when at least two head-to-head studies are available and treatments are well connected. Researchers should routinely report and compare the results from both network and pairwise meta-analyses.

Original languageEnglish (US)
Pages (from-to)41-49
Number of pages9
JournalJournal of Clinical Epidemiology
StatePublished - Feb 2019


  • Bayesian analysis
  • Borrowing of strength
  • Indirect evidence
  • Network meta-analysis
  • Research synthesis
  • Systematic review

ASJC Scopus subject areas

  • Epidemiology


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