title: Two new methods to fit models for network meta-analysis with random inconsistency effects.
abstract: Meta-analysis is a valuable tool for combining evidence from multiple studies. Network meta-analysis is becoming more widely used as a means to compare multiple treatments in the same analysis. However, a network meta-analysis may exhibit inconsistency, whereby the treatment effect estimates do not agree across all trial designs, even after taking between-study heterogeneity into account. We propose two new estimation methods for network meta-analysis models with random inconsistency effects.
  
  
  
  The model we consider is an extension of the conventional random-effects model for meta-analysis to the network meta-analysis setting and allows for potential inconsistency using random inconsistency effects. Our first new estimation method uses a Bayesian framework with empirically-based prior distributions for both the heterogeneity and the inconsistency variances. We fit the model using importance sampling and thereby avoid some of the difficulties that might be associated with using Markov Chain Monte Carlo (MCMC). However, we confirm the accuracy of our importance sampling method by comparing the results to those obtained using MCMC as the gold standard. The second new estimation method we describe uses a likelihood-based approach, implemented in the metafor package, which can be used to obtain (restricted) maximum-likelihood estimates of the model parameters and profile likelihood confidence intervals of the variance components.
  
  
  
  We illustrate the application of the methods using two contrasting examples. The first uses all-cause mortality as an outcome, and shows little evidence of between-study heterogeneity or inconsistency. The second uses "ear discharge" as an outcome, and exhibits substantial between-study heterogeneity and inconsistency. Both new estimation methods give results similar to those obtained using MCMC.
  
  
  
  The extent of heterogeneity and inconsistency should be assessed and reported in any network meta-analysis. Our two new methods can be used to fit models for network meta-analysis with random inconsistency effects. They are easily implemented using the accompanying R code in the Additional file 1. Using these estimation methods, the extent of inconsistency can be assessed and reported.
authors.affiliation: MRC Biostatistics Unit, Cambridge, UK. martin.law@mrc-bsu.cam.ac.uk.
authors.forename: Martin
authors.identifier: 0000-0001-9594-348X
authors.initials: M
authors.lastname: Law
authors.affiliation: MRC Biostatistics Unit, Cambridge, UK.
authors.forename: Dan
authors.initials: D
authors.lastname: Jackson
authors.affiliation: MRC Biostatistics Unit, Cambridge, UK.
authors.forename: Rebecca
authors.initials: R
authors.lastname: Turner
authors.affiliation: MRC Biostatistics Unit, Cambridge, UK.
authors.forename: Kirsty
authors.initials: K
authors.lastname: Rhodes
authors.affiliation: MRC Biostatistics Unit, Cambridge, UK.
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Viechtbauer
mesh_terms: D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000627:therapeutic use; D001499:Bayes Theorem; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008390:Markov Chains; D008962:Models, Theoretical; D009010:Monte Carlo Method; D011471:Prostatic Neoplasms / Q000628:therapy; D016896:Treatment Outcome; D000071076:Network Meta-Analysis as Topic
chemical_list: D000900:Anti-Bacterial Agents
keywords: Importance sampling; Informative priors; Network meta-analysis; Random inconsistency effects