gemtc-package: GeMTC: Network meta-analysis in R

Description

An R package for performing network meta-analyses (mixed treatment comparisons).

Arguments

Details

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. 2013). 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 that can be run using JAGS (using the rjags package), OpenBUGS (using the BRugs package) or WinBUGS (using the R2WinBUGS package). Furthermore it allows interoperability with GeMTC files that were created by the GeMTC GUI or exported from http://drugis.org/addis{ADDIS}. The source for GeMTC is available under GPL3 on https://github.com/gertvv/gemtc{Github}.

See http://drugis.org/gemtc for more information.

References

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. [http://dx.doi.org/10.1002/jrsm.1054{doi:10.1002/jrsm.1054}]

S. Dias, A.J. Sutton, A.E. Ades, and N.J. Welton (2013), A Generalized Linear Modeling Framework for Pairwise and Network Meta-analysis of Randomized Controlled Trials, Medical Decision Making 33(5):607-617. [http://dx.doi.org/10.1177/0272989X12458724{doi:10.1177/0272989X12458724}]

Examples

Run this code

# Load the example network and generate a consistency model:
file <- system.file("extdata/luades-smoking.gemtc", package="gemtc")
network <- read.mtc.network(file) 
model <- mtc.model(network, type="consistency")

# Load pre-generated samples instead of runing the model:
results <- mtc.run(model, thin=10)
results <- dget(system.file("extdata/luades-smoking.samples.gz", 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

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