GetReal in network meta‐analysis: a review of the methodology

Abstract

Pairwise meta-analysis is an established statistical tool for synthesizing evidence from multiple trials, but it is informative only about the relative efficacy of two specific interventions. The usefulness of pairwise meta-analysis is thus limited in real-life medical practice, where many competing interventions may be available for a certain condition and studies informing some of the pairwise comparisons may be lacking. This commonly encountered scenario has led to the development of network meta-analysis (NMA). In the last decade, several applications, methodological developments, and empirical studies in NMA have been published, and the area is thriving as its relevance to public health is increasingly recognized. This article presents a review of the relevant literature on NMA methodology aiming to pinpoint the developments that have appeared in the field. Copyright © 2016 John Wiley & Sons, Ltd.

Cognitive-Behavioral Analysis System of Psychotherapy, Drug, or Their Combination for Persistent Depressive Disorder: Personalizing the Treatment Choice Using Individual Participant Data Network Metaregression

“…Consistency refers to the statistical agreement between direct and indirect estimates in the network and is a prerequisite of network meta-analysis [35]. If consistency does not hold, network meta-analytic results may be biased.…”

Side effect profile and comparative tolerability of 21 antidepressants in the acute treatment of major depression in adults: protocol for a network meta-analysis

“…Assessment of inconsistency is an important part of NMA. It offers an additional quantitative method of exploring the validity of transitivity assumption 9. Large inconsistency implies a breach of transitivity, which in turn suggests that synthesising data in an NMA should be avoided.…”

Meta-Analysis of Complex Interventions

“…Barriers to network meta-analysis involve some important statistical assumptions that may be unreasonable or untenable in some meta-analyses of complex public health interventions (33,66). First, as with traditional pairwise meta-analysis of direct effects, using network meta-analysis to estimate mean effect sizes for subgroups of trials assumes homogeneity (or residual homogeneity) within the subgroups of trials of interest (33).…”

“…First, as with traditional pairwise meta-analysis of direct effects, using network meta-analysis to estimate mean effect sizes for subgroups of trials assumes homogeneity (or residual homogeneity) within the subgroups of trials of interest (33). Network meta-analysis also assumes transitivity (sometimes called similarity or exchangeability), which implies that any effect modifiers are comparably distributed across any pairs of trials being compared within the network.…”