Abstract
In the last decade, a new statistical methodology, namely, network meta-analysis, has been developed to address limitations in traditional pairwise meta-analysis. Network meta-analysis incorporates all available evidence into a general statistical framework for comparisons of all available treatments. A further development in the network meta-analysis is to use a Bayesian statistical approach, which provides a more flexible modelling framework to take into account heterogeneity in the evidence and complexity in the data structure. The aim of this paper is therefore to provide a nontechnical introduction to network meta-analysis for dental research community and raise the awareness of it. An example was used to demonstrate how to conduct a network meta-analysis and the differences between it and traditional meta-analysis. The statistical theory behind network meta-analysis is nevertheless complex, so we strongly encourage close collaboration between dental researchers and experienced statisticians when planning and conducting a network meta-analysis. The use of more sophisticated statistical approaches such as network meta-analysis will improve the efficiency in comparing the effectiveness between multiple treatments across a set of trials.
Highlights
With the rise of evidence-based medicine movement in the last two decades, systematic reviews and meta-analyses have been widely used for synthesis of evidence on beneficial and/or harmful effects of different treatments
A possible consequence is that results from multiple pairwise meta-analyses may not be consistent: for example, in three pairwise comparisons, treatment A is shown to be better than treatment B, and B better than treatment C; but A is inferior to C
The outcome variable for our illustration is change in clinical attachment level (CAL), and we found 18 randomised controlled trials (RCTs) that compared at least two of the three treatments [29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46]
Summary
With the rise of evidence-based medicine movement in the last two decades, systematic reviews and meta-analyses have been widely used for synthesis of evidence on beneficial and/or harmful effects of different treatments. Results from those reviews and meta-analyses provide important information for drawing clinical guidelines and making health policy recommendations. A further development in the network meta-analysis is to use a Bayesian statistical approach, which provides a more flexible modelling framework to take into account of heterogeneity in the evidence and complexity in the data structure [1,2,3,4]. We discussed a few practical issues to be considered when conducting a network meta-analysis
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