netmeta: Network Meta-Analysis using Frequentist Methods

Official Git repository of R package netmeta

Authors

Gerta Rücker, Ulrike Krahn, Jochem König, Orestis Efthimiou, Annabel Davies, Theodoros Papakonstantinou, Guido Schwarzer

Description

R package netmeta (Balduzzi et al., 2023) provides frequentist methods for network meta-analysis and supports Schwarzer et al. (2015), Chapter 8 "Network Meta-Analysis".

Available network meta-analysis models

• frequentist network meta-analysis (Rücker, 2012; Rücker & Schwarzer, 2014);

• additive network meta-analysis for combinations of treatments (Rücker, Petropoulou et al., 2020);

• network meta-analysis of binary data using the Mantel-Haenszel method or the non-central hypergeometric distribution (Efthimiou et al., 2019).

Methods to present results of a network meta-analysis

• network graphs (Rücker & Schwarzer, 2016);

• forest plots;

• league tables with network meta-analysis results;

• tables with network, direct and indirect estimates looking similar to the statistical part of a GRADE table for a network meta-analysis (Puhan et al., 2014).

Methods to rank treatments

• rankograms and ranking by the Surface Under the Cumulative RAnking curve (SUCRA) (Salanti et al., 2011);

• ranking of treatments by P-scores (frequentist analogue of SUCRAs without resampling) (Rücker & Schwarzer, 2015);

• partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014; Rücker & Schwarzer, 2017).

Methods to evaluate network inconsistency

• split direct and indirect evidence to check consistency (Dias et al., 2010);

• net heat plot and design-based decomposition of Cochran's Q (Krahn et al., 2013).

Additional methods

• contribution of direct comparisons to network estimates (Papakonstantinou et al., 2018; Davies et al., 2022)

• importance of individual studies measured by reduction of precision if removed from network (Rücker, Nikolakopoulou et al., 2020)

• 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012);

• measures characterizing the flow of evidence between two treatments (König et al., 2013).

Installation

Current official release:

install.packages("netmeta")

Current release on GitHub:

Installation using R package remotes:

install.packages("remotes")
remotes::install_github("guido-s/netmeta",
  ref = "develop", build_vignettes = TRUE)

How to cite 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

A BibTeX entry for LaTeX users is provided by

citation(package = "netmeta")

Bug Reports:

You can report bugs on GitHub under Issues.

or using the R command

bug.report(package = "netmeta")

(which is not supported in RStudio).

References

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, 1-21

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, 7, 610

Puhan MA, Schünemann HJ, Murad MH, Li T, Brignardello-Petersen R, Singh JA, Kessels AG, Guyatt GH, for the GRADE Working Group (2014): A GRADE Working Group approach for rating the quality of treatment effect estimates from network meta-analysis. BMJ, 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 (2020): Network meta-analysis of multicomponent interventions. Biometrical Journal, 62, 808-21

Rücker G, Nikolakopoulou A, Papakonstantinou T, Salanti G, Riley RD, Schwarzer G (2020): The statistical importance of a study for a network meta-analysis estimate. BMC Medical Research Methodology, 20, 190

Salanti G, Ades AE, Ioannidis JPA (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