title: Network meta-analysis with integrated nested Laplace approximations.
abstract: 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.
authors.affiliation: Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001 Zürich, Switzerland.
authors.forename: Rafael
authors.initials: R
authors.lastname: Sauter
authors.affiliation: Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, CH-8001 Zürich, Switzerland.
authors.forename: Leonhard
authors.initials: L
authors.lastname: Held
mesh_terms: D000208:Acute Disease; D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic; D005343:Fibrinolytic Agents / Q000627:therapeutic use; D006801:Humans; D008390:Markov Chains; D015201:Meta-Analysis as Topic; D009010:Monte Carlo Method; D009203:Myocardial Infarction / Q000188:drug therapy; D016540:Smoking Cessation; D012984:Software
chemical_list: D005343:Fibrinolytic Agents
keywords: Bayesian inference; Integrated nested Laplace approximations; Network meta-analysis; Node-splitting