title: Network meta-analysis using R: a review of currently available automated packages.
abstract: Network meta-analysis (NMA)--a statistical technique that allows comparison of multiple treatments in the same meta-analysis simultaneously--has become increasingly popular in the medical literature in recent years. The statistical methodology underpinning this technique and software tools for implementing the methods are evolving. Both commercial and freely available statistical software packages have been developed to facilitate the statistical computations using NMA with varying degrees of functionality and ease of use. This paper aims to introduce the reader to three R packages, namely, gemtc, pcnetmeta, and netmeta, which are freely available software tools implemented in R. Each automates the process of performing NMA so that users can perform the analysis with minimal computational effort. We present, compare and contrast the availability and functionality of different important features of NMA in these three packages so that clinical investigators and researchers can determine which R packages to implement depending on their analysis needs. Four summary tables detailing (i) data input and network plotting, (ii) modeling options, (iii) assumption checking and diagnostic testing, and (iv) inference and reporting tools, are provided, along with an analysis of a previously published dataset to illustrate the outputs available from each package. We demonstrate that each of the three packages provides a useful set of tools, and combined provide users with nearly all functionality that might be desired when conducting a NMA.
authors.affiliation: Department of Clinical Epidemiology and Biostatistics, McMaster University, MDCL 3200, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1.
authors.forename: Binod
authors.initials: B
authors.lastname: Neupane
authors.affiliation: Department of Clinical Epidemiology and Biostatistics, McMaster University, MDCL 3200, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1.
authors.forename: Danielle
authors.initials: D
authors.lastname: Richer
authors.affiliation: Department of Clinical Epidemiology and Biostatistics, McMaster University, MDCL 3200, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1.
authors.forename: Ashley Joel
authors.initials: AJ
authors.lastname: Bonner
authors.affiliation: Department of Mathematics and Statistics, McMaster University, HH 218, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1.
authors.forename: Taddele
authors.initials: T
authors.lastname: Kibret
authors.affiliation: Department of Clinical Epidemiology and Biostatistics, McMaster University, MDCL 3200, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1; Department of Mathematics and Statistics, McMaster University, HH 218, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1.
authors.forename: Joseph
authors.initials: J
authors.lastname: Beyene
mesh_terms: D003627:Data Interpretation, Statistical; D003920:Diabetes Mellitus / Q000453:epidemiology; D006801:Humans; D006973:Hypertension / Q000188:drug therapy; D015994:Incidence; D015201:Meta-Analysis as Topic; D012984:Software