Network Meta-Analysis using Frequentist Methods
Description
A comprehensive set of functions providing frequentist methods for network meta-analysis (Balduzzi et al., 2023) and supporting Schwarzer et al. (2015) , Chapter 8 "Network Meta-Analysis":
- frequentist network meta-analysis following Rücker (2012) ;
- additive network meta-analysis for combinations of treatments (Rücker et al., 2020) ;
- network meta-analysis of binary data using the Mantel-Haenszel or non-central hypergeometric distribution method (Efthimiou et al., 2019) , or penalised logistic regression (Evrenoglou et al., 2022) ;
- rankograms and ranking of treatments by the Surface under the cumulative ranking curve (SUCRA) (Salanti et al., 2013) ;
- ranking of treatments using P-scores (frequentist analogue of SUCRAs without resampling) according to Rücker & Schwarzer (2015) ;
- split direct and indirect evidence to check consistency (Dias et al., 2010) , (Efthimiou et al., 2019) ;
- scatter plot to visualize local inconsistency (Wilson et al., 2026) ;
- league table with network meta-analysis results;
- 'comparison-adjusted' funnel plot (Chaimani & Salanti, 2012) ;
- net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) ;
- measures characterizing the flow of evidence between two treatments by König et al. (2013) ;
- automated drawing of network graphs described in Rücker & Schwarzer (2016) ;
- partial order of treatment rankings ('poset') and Hasse diagram for 'poset' (Carlsen & Bruggemann, 2014) ; (Rücker & Schwarzer, 2017) ;
- contribution matrix as described in Papakonstantinou et al. (2018) and Davies et al. (2022) ;
- path-based approach for detecting and assessing inconsistency;
- network meta-regression with a single continuous or binary covariate (Kwarteng et al., 2026) ;
- subgroup network meta-analysis.