1-s2.0-S1098301514047317-main.pdf (1.3 MB)
Download file

Network meta-analysis: development of a three-level hierarchical modeling approach incorporating dose-related constraints

Download (1.3 MB)
journal contribution
posted on 24.08.2015, 14:12 by Rhiannon K. Owen, Douglas G. Tincello, R. Abrams Keith
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.



Value in Health, 2015, 18 (1), pp. 116-126

Author affiliation

/Organisation/COLLEGE OF MEDICINE, BIOLOGICAL SCIENCES AND PSYCHOLOGY/School of Medicine/Department of Cancer Studies and Molecular Medicine


VoR (Version of Record)

Published in

Value in Health


Elsevier for International Society for Pharmacoeconomics and Outcomes Research





Copyright date


Available date


Publisher version