title: Network meta-analysis: development of a three-level hierarchical modeling approach incorporating dose-related constraints.
abstract: BACKGROUND
  Network meta-analysis (NMA) is commonly used in evidence synthesis; however, in situations in which there are a large number of treatment options, which may be subdivided into classes, and relatively few trials, NMAs produce considerable uncertainty in the estimated treatment effects, and consequently, identification of the most beneficial intervention remains inconclusive.
  
  
  OBJECTIVE
  To develop and demonstrate the use of evidence synthesis methods to evaluate extensive treatment networks with a limited number of trials, making use of classes.
  
  
  METHODS
  Using Bayesian Markov chain Monte Carlo methods, we build on the existing work of a random effects NMA to develop a three-level hierarchical NMA model that accounts for the exchangeability between treatments within the same class as well as for the residual between-study heterogeneity. We demonstrate the application of these methods to a continuous and binary outcome, using a motivating example of overactive bladder. We illustrate methods for incorporating ordering constraints in increasing doses, model selection, and assessing inconsistency between the direct and indirect evidence.
  
  
  RESULTS
  The methods were applied to a data set obtained from a systematic literature review of trials for overactive bladder, evaluating the mean reduction in incontinence episodes from baseline and the number of patients reporting one or more adverse events. The data set involved 72 trials comparing 34 interventions that were categorized into nine classes of interventions, including placebo.
  
  
  CONCLUSIONS
  Bayesian three-level hierarchical NMAs have the potential to increase the precision in the effect estimates while maintaining the interpretability of the individual interventions for decision making.
authors.affiliation: Department of Health Sciences, University of Leicester, Leicester, UK. Electronic address: rhiannon.owen@leicester.ac.uk.
authors.forename: Rhiannon K
authors.initials: RK
authors.lastname: Owen
authors.affiliation: Reproductive Science Section, Department of Cancer Studies and Molecular Medicine, University of Leicester, Leicester, UK.
authors.forename: Douglas G
authors.initials: DG
authors.lastname: Tincello
authors.affiliation: Department of Health Sciences, University of Leicester, Leicester, UK.
authors.forename: R Abrams
authors.initials: RA
authors.lastname: Keith
mesh_terms: D001499:Bayes Theorem; D018680:Cholinergic Antagonists / Q000008:administration & dosage*; D002986:Clinical Trials as Topic / Q000379:methods; D004305:Dose-Response Relationship, Drug; D006801:Humans; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D053201:Urinary Bladder, Overactive / Q000188:drug therapy
chemical_list: D018680:Cholinergic Antagonists
keywords: mixed treatment comparisons; network meta-analysis; overactive bladder; statistical methods