Sauter R Held L 2015 Analyzing the collected evidence of a systematic review in form of a network meta-analysis (NMA) enjoys increasing popularity and provides a valuable instrument for decision making. Bayesian inference of NMA models is often propagated, especially if correlated random effects for multiarm trials are included. The standard choice for Bayesian inference is Markov chain Monte Carlo (MCMC) sampling, which is computationally intensive. An alternative to MCMC sampling is the recently suggested approximate Bayesian method of integrated nested Laplace approximations (INLA) that dramatically saves computation time without any substantial loss in accuracy. We show how INLA apply to NMA models for summary level as well as trial-arm level data. Specifically, we outline the modeling of multiarm trials and inference for functional contrasts with INLA. We demonstrate how INLA facilitate the assessment of network inconsistency with node-splitting. Three applications illustrate the use of INLA for a NMA. https://sonar.ch/global/documents/243888 eng info:eu-repo/semantics/altIdentifier/doi/10.1002/bimj.201400163 info:eu-repo/semantics/altIdentifier/pmid/26360927 Biometrical journal. Biometrische Zeitschrift. - 2015 Bayesian inference Integrated nested Laplace approximations Network meta-analysis Node-splitting Acute Disease Bayes Theorem Biometry Clinical Trials as Topic Fibrinolytic Agents Humans Markov Chains Meta-Analysis as Topic Monte Carlo Method Myocardial Infarction Smoking Cessation Software Network meta-analysis with integrated nested Laplace approximations. http://purl.org/coar/resource_type/c_6501