Indirect Comparisons and Network Meta-Analyses

Abstract

Background: Systematic reviews provide a structured summary of the results of trials that have been carried out on any particular subject. If the data from multiple trials are sufficiently homogenous, a meta-analysis can be performed to calculate pooled effect estimates. Traditional meta-analysis involves groups of trials that compare the same two interventions directly (head to head). Lately, however, indirect comparisons and network metaanalyses have become increasingly common.

Methods: Various methods of indirect comparison and network meta-analysis are presented and discussed on the basis of a selective review of the literature. The main assumptions and requirements of these methods are described, and a checklist is provided as an aid to the evaluation of published indirect comparisons and network meta-analyses.

Results: When no head-to-head trials of two interventions are available, indirect comparisons and network metaanalyses enable the estimation of effects as well as the simultaneous analysis of networks involving more than two interventions. Network meta-analyses and indirect comparisons can only be useful if the trial or patient characteristics are similar and the observed effects are sufficiently homogeneous. Moreover, there should be no major discrepancy between the direct and indirect evidence. If trials are available that compare each of two treatments against a third one, but not against each other, then the third intervention can be used as a common comparator to enable a comparison of the other two.

Conclusion: Indirect comparisons and network metaanalyses are an important further development of traditional meta-analysis. Clear and detailed documentation is needed so that findings obtained by these new methods can be reliably judged.

Figure

Examples of network diagrams a) Simple indirect comparison b) Star-shaped network c) More complex network containing 6 interventions