Skip to content

Automated methods to test connectedness and quantify indirectness of evidence in network meta‐analysis

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)113-124
Number of pages12
JournalResearch Synthesis Methods
Issue number1
Early online date4 Dec 2018
DateAccepted/In press - 30 Oct 2018
DateE-pub ahead of print - 4 Dec 2018
DatePublished (current) - 19 Mar 2019


Network meta-analysis compares multiple treatments from studies that form a connected network of evidence. However, for complex networks it is not easy to see if the network is connected. We use simple techniques from graph theory to test the connectedness of evidence networks in network meta-analysis. The method is to build the adjacency matrix for a network, with rows and columns corresponding to the treatments in the network and entries being one or zero depending on whether the treatments have been compared or not, and with zeros along the diagonal. Manipulation of this matrix gives the indirect connection matrix. The entries of this matrix determine whether two treatments can be compared, directly or indirectly. We also describe the distance matrix which gives the minimum number of steps in the network required to compare a pair of treatments. This is a useful assessment of an indirect comparison as each additional step requires further assumptions of homogeneity in, for example, design and target populations of included trials. If there are no loops in the network, the distance is a measure of the degree of assumptions needed; it is approximately this with loops. We illustrate our methods using several constructed examples and giving R code for computation. We have also implemented the techniques in the STATA package ‘network’. The methods provide a fast way to ensure comparisons are only made between connected treatments and to assess the degree of indirectness of a comparison.

    Research areas

  • Connectedness testing, Disconnected network, Graph theory, Indirect comparison, Network meta‐analysis

Download statistics

No data available



  • Full-text PDF (final published version)

    Rights statement: This is the final published version of the article (version of record). It first appeared online via Wiley at . Please refer to any applicable terms of use of the publisher.

    Final published version, 413 KB, PDF document

    Licence: CC BY


View research connections

Related faculties, schools or groups