A design-by-treatment interaction model for network meta-analysis and meta-regression with integrated nested Laplace approximations.

ABSTRACT: Network meta-analysis (NMA) is gaining popularity for comparing multiple treatments in a single analysis. Generalized linear mixed models provide a unifying framework for NMA, allow us to analyze datasets with dichotomous, continuous or count endpoints, and take into account multiarm trials, potential heterogeneity between trials and network inconsistency. To perform inference within such NMA models, the use of Bayesian methods is often advocated. The standard inference tool is Markov chain Monte Carlo (MCMC), which is computationally expensive and requires convergence diagnostics. A deterministic approach to do fully Bayesian inference for latent Gaussian models can be achieved by integrated nested Laplace approximations (INLA), which is a fast and accurate alternative to MCMC. We show ho

SUBMITTER: Gunhan BK 

PROVIDER: S-EPMC6001639 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature