R package netmeta (Balduzzi et al., 2023) provides frequentist methods for network meta-analysis and supports Schwarzer et al. (2015), Chapter 8 on network meta-analysis https://link.springer.com/book/10.1007/978-3-319-21416-0.
Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de, Gerta Rücker gerta.ruecker@uniklinik-freiburg.de
R package netmeta is an add-on package for meta providing the following network meta-analysis models:
frequentist network meta-analysis (function
netmeta) based on Rücker (2012) and Rücker &
Schwarzer (2014);
additive network meta-analysis for combinations of treatments
(netcomb for connected networks,
discomb for disconnected networks) (Rücker et al.,
2020a);
network meta-analysis of binary data
(netmetabin) using the Mantel-Haenszel or
non-central hypergeometric distribution method (Efthimiou et al.,
2019), or penalised logistic regression (Evrenoglou et al., 2022).
The following methods are available to present results of a network meta-analysis:
network graphs (netgraph) described in Rücker &
Schwarzer (2016);
forest plots (forest.netmeta,
forest.netcomb);
league tables with network meta-analysis results
(netleague);
tables with network, direct and indirect estimates
(nettable) looking similar to the statistical part
of a GRADE table for a network meta-analysis (Puhan et al.,
2014).
The following methods are implemented to rank treatments:
rankograms (rankogram) (Salanti et al., 2011);
ranking of treatments (netrank) based on
P-scores (Rücker & Schwarzer, 2015) or the Surface Under the
Cumulative RAnking curve (SUCRA) (Salanti et al., 2011);
partial order of treatment rankings (netposet,
plot.netposet) and Hasse diagram
(hasse) according to Carlsen & Bruggemann (2014)
and Rücker & Schwarzer (2017).
Available functions to evaluate network inconsistency:
split direct and indirect evidence (netsplit)
to check for consistency (Dias et al., 2010; Efthimiou et al.,
2019);
net heat plot (netheat) and design-based
decomposition of Cochran's Q (decomp.design)
described in Krahn et al. (2013).
Additional methods and functions:
network meta-regression with a single continuous or binary covariate
(netmetareg);
subgroup network meta-analysis (subgroup.netmeta);
information on network connectivity
(netconnection);
contribution of direct comparisons to network estimates
(netcontrib) (Papakonstantinou et al., 2018; Davies
et al., 2022);
importance of individual studies measured by reduction of
precision if removed from network (netimpact)
(Rücker et al., 2020b);
‘comparison-adjusted’ funnel plot
(funnel.netmeta) to assess funnel plot asymmetry in
network meta-analysis (Chaimani & Salanti, 2012);
conduct pairwise meta-analyses for all comparisons with
direct evidence in a network meta-analysis
(netpairwise);
results of several network meta-analyses can be combined with
netbind to show these results in a forest plot
(forest.netbind).
measures characterizing the flow of evidence between two
treatments (netmeasures) described in König et
al. (2013);
calculate comparison effects of two arbitrary complex
interventions in component network meta-analysis
(netcomparison);
calculate effect of arbitrary complex interventions in
component network meta-analysis (netcomplex).
R package netmeta provides two vignettes:
vignette("netmeta-workflow") with an overview of the
work flow and the main functions,
vignette("netmeta") with the PDF file for
Balduzzi et al. (2023).
Functions and datasets from netmeta are utilised in Schwarzer et al. (2015), Chapter 8 "Network Meta-Analysis", https://link.springer.com/book/10.1007/978-3-319-21416-0.
Type help(package = "netmeta") for a listing of all R
functions available in netmeta.
Type citation("netmeta") on how to cite netmeta in
publications.
To report problems and bugs
type bug.report(package = "netmeta") if you do not use
RStudio,
send an email to Guido Schwarzer guido.schwarzer@uniklinik-freiburg.de if you use RStudio.
The development version of netmeta is available on GitHub https://github.com/guido-s/netmeta.
Balduzzi S, Rücker G, Nikolakopoulou A, Papakonstantinou T, Salanti G, Efthimiou O, Schwarzer G (2023): netmeta: An R Package for network meta-analysis using frequentist methods. Journal of Statistical Software, 106, 1--40
Carlsen L, Bruggemann R (2014): Partial order methodology: a valuable tool in chemometrics. Journal of Chemometrics, 28, 226--34
Chaimani A & Salanti G (2012): Using network meta-analysis to evaluate the existence of small-study effects in a network of interventions. Research Synthesis Methods, 3, 161--76
Davies AL, Papakonstantinou T, Nikolakopoulou A, Rücker G, Galla T (2022): Network meta-analysis and random walks. Statistics in Medicine, 41, 2091--2114
Dias S, Welton NJ, Caldwell DM, Ades AE (2010): Checking consistency in mixed treatment comparison meta-analysis. Statistics in Medicine, 29, 932--44
Efthimiou O, Rücker G, Schwarzer G, Higgins J, Egger M, Salanti G (2019): A Mantel-Haenszel model for network meta-analysis of rare events. Statistics in Medicine, 38, 2992--3012
Evrenoglou T, White IR, Afach S, Mavridis D, Chaimani A (2022): Network Meta-Analysis of Rare Events Using Penalized Likelihood Regression. Statistics in Medicine, 41, 5203--19.
König J, Krahn U, Binder H (2013): Visualizing the flow of evidence in network meta-analysis and characterizing mixed treatment comparisons. Statistics in Medicine, 32, 5414--29
Krahn U, Binder H, König J (2013): A graphical tool for locating inconsistency in network meta-analyses. BMC Medical Research Methodology, 13, 35
Papakonstantinou, T., Nikolakopoulou, A., Rücker, G., Chaimani, A., Schwarzer, G., Egger, M., Salanti, G. (2018): Estimating the contribution of studies in network meta-analysis: paths, flows and streams. F1000Research
Puhan MA, Schünemann HJ, Murad MH, et al. (2014): A GRADE working group approach for rating the quality of treatment effect estimates from network meta-analysis. British Medical Journal, 349, g5630
Rücker G (2012): Network meta-analysis, electrical networks and graph theory. Research Synthesis Methods, 3, 312--24
Rücker G, Schwarzer G (2014): Reduce dimension or reduce weights? Comparing two approaches to multi-arm studies in network meta-analysis. Statistics in Medicine, 33, 4353--69
Rücker G, Schwarzer G (2015): Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Medical Research Methodology, 15, 58
Rücker G, Schwarzer G (2016): Automated drawing of network plots in network meta-analysis. Research Synthesis Methods, 7, 94--107
Rücker G, Schwarzer G (2017): Resolve conflicting rankings of outcomes in network meta-analysis: Partial ordering of treatments. Research Synthesis Methods, 8, 526--36
Rücker G, Petropoulou M, Schwarzer G (2020a): Network meta-analysis of multicomponent interventions. Biometrical Journal, 62, 808--21
Rücker G, Nikolakopoulou A, Papakonstantinou T, Salanti G, Riley RD, Schwarzer G (2020b): The statistical importance of a study for a network meta-analysis estimate. BMC Medical Research Methodology, 20, 190
Salanti G, Ades AE, Ioannidis JP (2011): Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. Journal of Clinical Epidemiology, 64, 163--71
Schwarzer G, Carpenter JR and Rücker G (2015): Meta-Analysis with R (Use R!). Springer International Publishing, Switzerland.
Useful links: