GeMTC: Network meta-analysis in R
An R package for performing network meta-analyses (mixed treatment comparisons). package
Network meta-analysis, or mixed treatment comparison (MTC) is a technique to meta-analyze networks of trials comparing two or more treatments at the same time [Dias et al. 2013a]. Using a Bayesian hierarchical model, all direct and indirect comparisons are taken into account to arrive at a single consistent estimate of the effect of all included treatments based on all included studies.
This package allows the automated generation of network meta-analysis models [van Valkenhoef et al. 2012], inclusing both fixed effect and random effects network meta-analysis, node-splitting models to identify inconsistency, and network meta-regression models. Models are estimated using JAGS (through the rjags package).
It is possible to get reproducible results, but as JAGS uses its own pseudo-random number generator, this is somewhat more involved. See mtc.model for details.
The source for GeMTC is available under the GPL-3 on Github.
Gert van Valkenhoef
S. Dias, N.J. Welton, D.M. Caldwell, and A.E. Ades (2010), Checking consistency in mixed treatment comparison meta-analysis, Statistics in Medicine 29(7-8, Sp. Iss. SI):932-944.
[tools:::Rd_expr_doi("10.1002/sim.3767") ]
S. Dias, A.J. Sutton, A.E. Ades, and N.J. Welton (2013a), A Generalized Linear Modeling Framework for Pairwise and Network Meta-analysis of Randomized Controlled Trials, Medical Decision Making 33(5):607-617. [tools:::Rd_expr_doi("10.1177/0272989X12458724") ]
S. Dias, A.J. Sutton, N.J. Welton, and A.E. Ades (2013b), Heterogeneity - Subgroups, Meta-Regression, Bias, and Bias-Adjustment, Medical Decision Making 33(5):618-640.
[tools:::Rd_expr_doi("10.1177/0272989X13485157") ]
S. Dias, N.J. Welton, A.J. Sutton, D.M. Caldwell, G. Lu, and A.E. Ades (2013c), Inconsistency in Networks of Evidence Based on Randomized Controlled Trials, Medical Decision Making 33(5):641-656. [tools:::Rd_expr_doi("10.1177/0272989X12455847") ]
A. Gelman, A. Jakulin, M. Grazia Pittau, Y.-S. Su (2008), A weakly informative default prior distribution for logistic and other regression models, The Annals of Applied Statistics 2(4):1360-1383. [tools:::Rd_expr_doi("10.1214/08-AOAS191") ]
R.M. Turner, J. Davey, M.J. Clarke, S.G. Thompson, J.P.T. Higgins (2012), Predicting the extent of heterogeneity in meta-analysis, using empirical data from the Cochrane Database of Systematic Reviews, International Journal of Epidemiology 41(3):818-827. [tools:::Rd_expr_doi("10.1093/ije/dys041") ]
G. van Valkenhoef, G. Lu, B. de Brock, H. Hillege, A.E. Ades, and N.J. Welton (2012), Automating network meta-analysis, Research Synthesis Methods 3(4):285-299. [tools:::Rd_expr_doi("10.1002/jrsm.1054") ]
G. van Valkenhoef, S. Dias, A.E. Ades, and N.J. Welton (2015), Automated generation of node-splitting models for assessment of inconsistency in network meta-analysis, Research Synthesis Methods, accepted manuscript. [tools:::Rd_expr_doi("10.1002/jrsm.1167") ]
G. van Valkenhoef et al. (draft), Modeling inconsistency as heterogeneity in network meta-analysis, draft manuscript.
D.E. Warn, S.G. Thompson, and D.J. Spiegelhalter (2002), Bayesian random effects meta-analysis of trials with binary outcomes: methods for the absolute risk difference and relative risk scales, Statistics in Medicine 21(11):1601-1623. [tools:::Rd_expr_doi("10.1002/sim.1189") ]
mtc.network, mtc.model, mtc.run
# Load the example network and generate a consistency model: model <- mtc.model(smoking, type="consistency") # Load pre-generated samples instead of runing the model: ## Not run: results <- mtc.run(model, thin=10) results <- readRDS(system.file("extdata/luades-smoking-samples.rds", package="gemtc")) # Print a basic statistical summary of the results: summary(results) ## Iterations = 5010:25000 ## Thinning interval = 10 ## Number of chains = 4 ## Sample size per chain = 2000 ## ## 1. Empirical mean and standard deviation for each variable, ## plus standard error of the mean: ## ## Mean SD Naive SE Time-series SE ## d.A.B 0.4965 0.4081 0.004563 0.004989 ## d.A.C 0.8359 0.2433 0.002720 0.003147 ## d.A.D 1.1088 0.4355 0.004869 0.005280 ## sd.d 0.8465 0.1913 0.002139 0.002965 ## ## 2. Quantiles for each variable: ## ## 2.5% 25% 50% 75% 97.5% ## d.A.B -0.2985 0.2312 0.4910 0.7530 1.341 ## d.A.C 0.3878 0.6720 0.8273 0.9867 1.353 ## d.A.D 0.2692 0.8197 1.0983 1.3824 2.006 ## sd.d 0.5509 0.7119 0.8180 0.9542 1.283