Performing arm-based network meta-analysis in R with the pcnetmeta package

Lifeng Lin, Jing Zhang, James S Hodges, Haitao Chu

Research output: Contribution to journalArticlepeer-review

66 Scopus citations


Network meta-analysis is a powerful approach for synthesizing direct and indirect evidence about multiple treatment comparisons from a collection of independent studies. At present, the most widely used method in network meta-analysis is contrast-based, in which a baseline treatment needs to be specified in each study, and the analysis focuses on modeling relative treatment effects (typically log odds ratios). However, populationaveraged treatment-specific parameters, such as absolute risks, cannot be estimated by this method without an external data source or a separate model for a reference treatment. Recently, an arm-based network meta-analysis method has been proposed, and the R package pcnetmeta provides user-friendly functions for its implementation. This package estimates both absolute and relative effects, and can handle binary, continuous, and count outcomes.

Original languageEnglish (US)
JournalJournal of Statistical Software
StatePublished - 2017

Bibliographical note

Funding Information:
LL and JZ were supported in part by the NIAID AI103012. HC was supported in part by the NIAID AI103012, NIDCR R03DE024750, NLM R21LM012197, NCI P30CA077598, NIMHD U54MD008620, and NIDDK U01DK106786.

Publisher Copyright:
© 2017, American Statistical Association. All rights reserved.


  • Absolute effect
  • Arm-based method
  • Bayesian inference
  • Network meta-analysis


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