A primer on network meta-analysis with emphasis on mental health

Abstract

Objective: A quantitative synthesis of evidence via standard pair-wise meta-analysis lies on the top of the hierarchy for evaluating the relative effectiveness or safety between two interventions. In most healthcare problems, however, there is a plethora of competing interventions. Network meta-analysis allows to rank competing interventions and evaluate their relative effectiveness even if they have not been compared in an individual trial. The aim of this paper is to explain and discuss the main features of this statistical technique.

Methods: We present the key assumptions underlying network meta-analysis and the graphical methods to visualise results and information in the network. We used one illustrative example that compared the relative effectiveness of 15 antimanic drugs and placebo in acute mania.

Results: A network plot allows to visualise how information flows in the network and reveals important information about network geometry. Discrepancies between direct and indirect evidence can be detected using inconsistency plots. Relative effectiveness or safety of competing interventions can be presented in a league table. A contribution plot reveals the contribution of each direct comparison to each network estimate. A comparison-adjusted funnel plot is an extension of simple funnel plot to network meta-analysis. A rank probability matrix can be estimated to present the probabilities of all interventions assuming each rank and can be represented using rankograms and cumulative probability plots.

Conclusions: Network meta-analysis is very helpful in comparing the relative effectiveness and acceptability of competing treatments. Several issues, however, still need to be addressed when conducting a network meta-analysis for the results to be valid and correctly interpreted.

Figure 1

Network plot for efficacy. Size of nodes is proportional to the number of patients randomised to interventions. Thickness of lines is proportional to the number of studies contributing to the direct comparison. In the right-hand side plot, light grey lines denote that the majority of studies for that comparison were conducted before 2003, whereas dark grey lines denote that the majority of studies were conducted after 2003.

Figure 2

Inconsistency plot for the outcome ‘mean change’. Inconsistency factors (IF) along with their 95% CIs and the corresponding p values for testing equality between direct and indirect evidence are displayed. IFs are calculated as the absolute difference between direct and indirect estimates, and therefore CIs are truncated to zero. Loops of which the lower CI limit does not reach the zero line are considered to present statistically significant inconsistency.

Figure 3

Rankograms for the efficacy outcome.

Figure 4

Cumulative ranking probability plots for the efficacy outcome. The SUCRA value for each intervention is given.

Figure 5

Clustered ranking plot for efficacy and acceptability. Cluster techniques (single linkage clustering) were used to cluster interventions in groups defined by different symbols.

Figure 6

Comparison-adjusted funnel plot for efficacy.