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Review
. 2021 Oct;74(5):371-382.
doi: 10.4097/kja.21358. Epub 2021 Sep 23.

Concepts and emerging issues of network meta-analysis

EunJin Ahn  1 , Hyun Kang  1
Affiliations
Review

Concepts and emerging issues of network meta-analysis

EunJin Ahn et al. Korean J Anesthesiol. 2021 Oct.

Abstract

Most diseases have more than two interventions or treatment methods, and the application of network meta-analysis (NMA) studies to compare and evaluate the superiority of each intervention or treatment method is increasing. Understanding the concepts and processes of systematic reviews and meta-analyses is essential to understanding NMA. As with systematic reviews and meta-analyses, NMA involves specifying the topic, searching for and selecting all related studies, and extracting data from the selected studies. To evaluate the effects of each treatment, NMA compares and analyzes three or more interventions or treatment methods using both direct and indirect evidence. There is a possibility of several biases when performing NMA. Therefore, key assumptions like similarity, transitivity, and consistency should be satisfied when performing NMA. Among these key assumptions, consistency can be evaluated and quantified by statistical tests. This review aims to introduce the concepts of NMA, analysis methods, and interpretation and presentation of the results of NMA. It also briefly introduces the emerging issues in NMA, including methods for evaluation of consistency.

Keywords: Bayesian approach; Meta-analysis; Mixed treatment meta-analysis; Multiple treatment comparison meta-analysis.; Network meta-analysis; Statistics; Systematic review.

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Conflict of interest statement

Conflicts of Interest

No potential conflict of interest relevant to this article was reported.

Figures

Fig. 1.
Fig. 1.
Direct and indirect comparisons in network meta-analysis. (A) Direct (TA versus TB, TA versus TC, and TA versus TD) and indirect (TB versus TC and TC versus TD) comparisons anchored by TA. Anchored indirect treatment comparison is called an open triangle. (B) Direct comparisons are called a closed loop.
Fig. 2.
Fig. 2.
The examples to calculate treatment effects between studies. (A) TBC (difference of treatment effect between TB and TC) can be calculated as TC−TB. However, an error can occur when a difference in the treatment effect of A exists. (B) No connection between the treatments exists. TAB and TCD cannot be assumed. (C) TBC can be assumed using the common comparator TA in study AB (a study that compares TA and TB) and study AC (a study that compares TA and TC), and TAD can be assumed using the common comparator TC.
Fig. 3.
Fig. 3.
Transitivity-visualized diagram. Transitivity between studies AB and AC via TA as a common comparator. The assumption of transitivity is violated when the effect modifier D (between TA and TB) is not similar to effect modifier E (between TA and TC).
Fig. 4.
Fig. 4.
Network of six treatments that includes three two-arm studies comparing treatments A with B + C, B with A + C, and A + B with C. (A) A disconnected network of three two-arm studies with six treatments. (B) The component network meta-analysis (CNMA) model enables connections between the treatments having common components.
See this image and copyright information in PMC

References

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