Is network meta-analysis as valid as standard pairwise meta-analysis? It all depends on the distribution of effect modifiers

By Jeroen P. Jansen, Huseyin Naci. BMC Medicine, 2013

574 citations recorded by the source corpus.

BackgroundIn 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.DiscussionIn 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.SummaryThe 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.

Subfields: Ecology, Evolution, Behavior and Systematics, Statistics and Probability, Surgery

View Paper: https://www.semanticscholar.org/paper/13986e1f8347def295af5b908c02c8ee32904a9a

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Citations in the corpus

Evaluating the Quality of Evidence from a Network Meta-Analysis (2014, methodology): “by using directed acyclic graphs see [18]) can also be employed (for a review see [19]).”
Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016 (2017, background): “A recent Cochrane meta-analysis did not find strong evidence that DSI alters the duration of mechanical ventilation, mortality, ICU or hospital LOS, adverse event rates, or drug consumption for critically ill adults receiving mechanical ventilation compared to sedation strategies that do not include”
GetReal in network meta‐analysis: a review of the methodology (2016, background): “Statistical inconsistency can be thought of as another form of heterogeneity: heterogeneity results from the variation of effect modifiers within a treatment comparison, while inconsistency results from the variation of effect modifiers across treatment comparisons (Jansen and Naci, 2013).”
Visualizing Assumptions and Results in Network Meta-analysis: The Network Graphs Package (2015, result): “Sometimes, diﬀerences in comparisons can be an indication of potential violation of the assumption underlying network meta-analysis (Jansen and Naci 2013; Salanti 2012).”
Automated generation of node‐splitting models for assessment of inconsistency in network meta‐analysis (2015, background): “This is so because different estimates from studies that include the same set of treatments are more appropriately viewed as heterogeneity (Jansen and Naci, 2013).”