netmeta-package: netmeta: Brief overview of methods and general hints

In netmeta: Network Meta-Analysis using Frequentist Methods

netmeta: Brief overview of methods and general hints

Description

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.

Details

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).

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:

• 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).

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 [e-mail] if you use RStudio.

The development version of netmeta is available on GitHub https://github.com/guido-s/netmeta.

Author(s)

Guido Schwarzer [e-mail], Gerta Rücker [e-mail]

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, 38, 2992–3012

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.

netmeta documentation built on June 23, 2024, 9:06 a.m.