Network Meta-Analysis using Frequentist Methods
Create a data frame from an object of class netconnection
Create a data frame from an object of class netmeta
Auxiliary functions for component network meta-analysis
Create a netmeta object from a crossnma object
Design-based decomposition of Cochran's Q in network meta-analysis
Additive network meta-analysis for combinations of treatments (disconn...
Forest plot showing results of two or more network meta-analyses
Forest plot for additive network meta-analysis
Forest plot for complex interventions in component network meta-analys...
Forest plot for complex interventions in component network meta-analys...
Forest plot for network meta-analysis
Forest plot for direct and indirect evidence
Forest plot showing results of network meta-analysis with subgroups
Comparison-adjusted funnel plot
Hasse diagram
Derive hat matrix from network meta-analysis
Heat Plot
Generic function for heat plots
Moore-Penrose Pseudoinverse of a Matrix
Merge pairwise object with additional data
Test of funnel plot asymmetry in network meta-analysis
Combine network meta-analysis objects
Additive network meta-analysis for combinations of treatments
Calculate comparison effects of two arbitrary complex interventions in...
Calculate effect of arbitrary complex interventions in component netwo...
Get information on network connectivity (number of subnetworks, distan...
Contribution matrix in network meta-analysis
Calculate distance matrix for an adjacency matrix
Network graph for objects of class discomb
Network graph for objects of class netcomb
Network graph for objects of class netconnection
Network graph for objects of class netimpact
Network graph
Generic function for network graphs
Net heat plot
Determine the importance of individual studies in network meta-analysi...
Create league table with network meta-analysis results
Create a matrix with additional information for pairwise comparisons
Measures for characterizing a network meta-analysis
netmeta: Brief overview of methods and general hints
Network meta-analysis using graph-theoretical method
Network meta-analysis of binary outcome data
Network meta-regression with a single continuous or binary covariate
Conduct pairwise meta-analyses for all comparisons with direct evidenc...
Partial order of treatments in network meta-analysis
Frequentist method to rank treatments in network
Split direct and indirect evidence in network meta-analysis
Table with network meta-analysis results
Scatter plot or biplot showing partially order of treatment ranks
Plot treatment ranking(s) of network meta-analyses
Plot rankograms
Print method for objects of class decomp.design
Print method for objects of class netbind
Print method for objects of class netcomb
Print method for objects of class netimpact
Print method for objects of class netmeta
Print method for rankograms
Print detailed information for component network meta-analysis
Print detailed results of network meta-analysis
Comparison-adjusted radial plot
Calculate rankogram from treatment effect samples
Calculate rankogram
Generic function for rankograms
Print and change default network meta-analysis settings in R package n...
Subgroup analysis for network meta-analysis
Summary method for objects of class netcomb
Summary method for objects of class netconnection
Summary method for objects of class netmeta
Summary method for objects of class netmetareg
Summary method for objects of class rankogram
Abbreviate treatment names
A comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) <doi:10.18637/jss.v106.i02> and supporting Schwarzer et al. (2015) <doi:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis": - frequentist network meta-analysis following Rücker (2012) <doi:10.1002/jrsm.1058>; - additive network meta-analysis for combinations of treatments (Rücker et al., 2020) <doi:10.1002/bimj.201800167>; - network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) <doi:10.1002/sim.8158>, or penalised logistic regression (Evrenoglou et al., 2022) <doi:10.1002/sim.9562>; - rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) <doi:10.1016/j.jclinepi.2010.03.016>; - ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) <doi:10.1186/s12874-015-0060-8>; - split direct and indirect evidence to check consistency (Dias et al., 2010) <doi:10.1002/sim.3767>, (Efthimiou et al., 2019) <doi:10.1002/sim.8158>; - league table with network meta-analysis results; - 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) <doi:10.1002/jrsm.57>; - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <doi:10.1186/1471-2288-13-35>; - measures characterizing the flow of evidence between two treatments by König et al. (2013) <doi:10.1002/sim.6001>; - automated drawing of network graphs described in Rücker & Schwarzer (2016) <doi:10.1002/jrsm.1143>; - partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) <doi:10.1002/cem.2569>; (Rücker & Schwarzer, 2017) <doi:10.1002/jrsm.1270>; - contribution matrix as described in Papakonstantinou et al. (2018) <doi:10.12688/f1000research.14770.3> and Davies et al. (2022) <doi:10.1002/sim.9346>; - network meta-regression with a single continuous or binary covariate; - subgroup network meta-analysis.