Table 2. Modeling options from NMA R packages gemtc, pcnetmeta and netmeta.

Tasks	Features	gemtc	pcnetmeta	netmeta
NMA Model	Based on	Generalized Linear Models [6], [17]	Multivariate methods [18]	Graph theory [32]
Model options regarding the Homogeneity assumption	Fixed-effect model	✓	✓	✓
	Random-effect model with a common heterogeneity parameter	✓	✓	✓
	Random-effect model with different heterogeneity parameters	✗	✓	✗
Model options regarding the Consistency assumption	Consistency model	✓	✓	✓
	Inconsistency model	✓	✗	✓
Inclusion of covariates	Meta-regression	✗	✗	✗
Estimation framework	Frequentist	✗	✗	✓
	Bayesian	✓	✓	✗
Bayesian Modeling				NA
Prior distributions for baseline and relative effect parameters	Default distribution and parameter values	✓ Normal distribution, heuristic initial values	✓ Normal distribution, heuristic initial values
	Option for user-specified distribution and parameter values	✓ Restricted to specifying variance	✓ Restricted to Normal distribution
Prior distribution for heterogeneity parameters	Default distribution and parameter values	✓ Uniform distribution, heuristic initial values	✓ Inverse-Gamma distribution, specific values
	Option for user-specified distribution and parameter values	✓ Uniform or Gamma distribution, specify values	✓ Inverse-Gamma or Wishart distribution, specify values
Markov Chain Monte Carlo (MCMC) Sampler	WinBUGS	✓	✗
	OpenBUGS	✓	✗
	JAGS	✓	✓
Control over posterior samples	Total iterations	✓	✓
	Adaptation phase	✓	✓
	Burn-in phase	✓	✓
	Thinning	✓	✓
Model convergence diagnostics	Option for multiple chains	✓	✓
	Time-series plot	✓	✓
	Trace plot	✓	✗
	Brooks-Gelman-Rubin (BGR) diagnostic test	✓	✗