Network meta-analyses: Methodological prerequisites and clinical usefulness

Abstract

It is an undeniable fact that systematic reviews play a crucial role in informing clinical practice; however, conventional head-to-head meta-analyses do have limitations. In particular, studies can only be compared in a pair-wise fashion, and conclusions can only be drawn in the light of direct evidence. In contrast, network meta-analyses can not only compare multiple interventions but also utilize indirect evidence which increases their precision. On top of that, they can also rank competing interventions. In this mini-review, we have aimed to elaborate on the principles and techniques governing network meta-analyses to achieve a methodologically sound synthesis, thus enabling safe conclusions to be drawn in clinical practice. We have emphasized the prerequisites of a well-conducted Network Meta-Analysis (NMA), the value of selecting appropriate outcomes according to guidelines for transparent reporting, and the clarity achieved via sophisticated graphical tools. What is more, we have addressed the importance of incorporating the level of evidence into the results and interpreting the findings according to validated appraisal systems (i.e., the Grade of Recommendations, Assessment, Development, and Evaluation system - GRADE). Lastly, we have addressed the possibility of planning future research via NMAs. Thus, we can conclude that NMAs could be of great value to clinical practice.

Keywords: Network meta-analysis, Quality of evidence, Evidence-based medicine, Systematic reviews

Core Tip: Systematic reviews with or without meta-analyses provide the highest quality of evidence, thus lying on the top of evidence-based medicine hierarchy. However, pair-wise meta-analyses present the inherent limitation of exclusively comparing direct evidence. By contrast, Network Meta-Analyses (NMAs) also consider indirect evidence, thereby offering additional useful information. Conducting an NMA, however, has certain requirements such as assuming that transitivity across the included studies exists. What is more, maintaining sufficient statistical power in the analyses is crucial. In addition, performing head-to-head statistical comparisons before setting up networks of interventions is a prerequisite for a methodologically sound NMA, and selecting not only positive but also negative outcomes is required. Lastly, implementing quality appraisal systems to grade the level of evidence is highly recommended. Should all the above criteria be fulfilled, then accurate clinical conclusions can be drawn from an NMA.