Is network meta-analysis as valid as standard pairwise meta-analysis? It all depends on the distribution of effect modifiers
1 Mapi, 180 Canal Street, Suite 503, Boston, MA 02114, USA
2 Tufts University School of Medicine, 145 Harrison Avenue, Boston, MA 02111, USA
3 LSE Health & Social Care, London School of Economics & Political Science, Cowdray House, 20 Houghton Street, London WC2A 2AE, UK
BMC Medicine 2013, 11:159 doi:10.1186/1741-7015-11-159Published: 4 July 2013
In the last decade, network meta-analysis of randomized controlled trials has been introduced as an extension of pairwise meta-analysis. The advantage of network meta-analysis over standard pairwise meta-analysis is that it facilitates indirect comparisons of multiple interventions that have not been studied in a head-to-head fashion. Although assumptions underlying pairwise meta-analyses are well understood, those concerning network meta-analyses are perceived to be more complex and prone to misinterpretation.
In this paper, we aim to provide a basic explanation when network meta-analysis is as valid as pairwise meta-analysis. We focus on the primary role of effect modifiers, which are study and patient characteristics associated with treatment effects. Because network meta-analysis includes different trials comparing different interventions, the distribution of effect modifiers cannot only vary across studies for a particular comparison (as with standard pairwise meta-analysis, causing heterogeneity), but also between comparisons (causing inconsistency). If there is an imbalance in the distribution of effect modifiers between different types of direct comparisons, the related indirect comparisons will be biased. If it can be assumed that this is not the case, network meta-analysis is as valid as pairwise meta-analysis.
The validity of network meta-analysis is based on the underlying assumption that there is no imbalance in the distribution of effect modifiers across the different types of direct treatment comparisons, regardless of the structure of the evidence network.