Table 9 Software for fitting meta-analysis models (full references in bibliography)

Stata: networkplotfrom the mvmeta package [39]

Chaimani et al. (2013) [40]

R: netgraph command in netmeta [41], gemtc [42], pcnetmeta [43]

Rucker and Schwarzer (2016) [44]

WinBUGS: flexible modelling framework, model code available from the Univerisity of Bristol website (https://www.bristol. ac.uk/population-health-sciences/ centres/cresyda/mpes/code/).

Welton et al. (2012) [45], Dias and Caldwell (2019) [17]

Dias et al. [46]

R2WinBUGS [47], BUGSnet [48]

R: netgraph command in netmeta [41], gemtc [42], pcnetmeta [43]

[27], Neupane et al. (2014) [49]

Stata: mvmeta package [50]

Chaimani and Salanti (2015) [51], Chaimani et al. (2013) [40]

Webtools: MetaInsight [52], MetaDTA [53] and CINeMA [54]

WinBUGS: can utilise study level covariates from NMA models, model code available from the Univerisity of Bristol website (https://www.bristol. ac.uk/population-health-sciences/ centres/cresyda/mpes/code/).

Stata: metareg command, mvmeta package

R: mvmeta package, metafor package [55], GeMTC package [42]

WinBUGS: inclusion of IPD and aggregated data model available in paper by Saramago et al. (2012) [21]

Freeman et al. (2018) [24], Freeman and Carpenter (2017) [56], Riley et al. (2008) [57]

Debray et al. (2015) [58], Tierney et al. (2015) [59]

PRISMA-IPD checklist [60]

WinBUGS: component NMA model code available from the Univerisity of Bristol website (https://www.bristol. ac.uk/population-health-sciences/ centres/cresyda/mpes/code/).

Welton et al. (2009) [11]

R: The additive model can be implemented using the discomb command in the frequentist netmeta package [41]

Higgins et al. (2019) [2], Caldwell and Welton (2016) [5]

Melendez-Torres et al. (2015) [6]

Inclusion of covariates in component NMA models by Freeman et al. (2018) [24]