Evidence-based health-care decision making requires comparisons of all relevant competing interventions. In the absence of randomized, controlled trials involving a direct comparison of all treatments of interest, indirect treatment comparisons and network meta-analysis provide useful evidence for judiciously selecting the best choice(s) of treatment. Mixed treatment comparisons, a special case of network meta-analysis, combine direct and indirect evidence for particular pairwise comparisons, thereby synthesizing a greater share of the available evidence than a traditional meta-analysis. This report from the ISPOR Indirect Treatment Comparisons Good Research Practices Task Force provides guidance on the interpretation of indirect treatment comparisons and network meta-analysis to assist policymakers and health-care professionals in using its findings for decision making. We start with an overview of how networks of randomized, controlled trials allow multiple treatment comparisons of competing interventions. Next, an introduction to the synthesis of the available evidence with a focus on terminology, assumptions, validity, and statistical methods is provided, followed by advice on critically reviewing and interpreting an indirect treatment comparison or network meta-analysis to inform decision making. We finish with a discussion of what to do if there are no direct or indirect treatment comparisons of randomized, controlled trials possible and a health-care decision still needs to be made.

Evidence-based health care decision making requires comparison of all relevant competing interventions. In the absence of randomized controlled trials involving a direct comparison of all treatments of interest, indirect treatment comparisons and network meta-analysis provide useful evidence for judiciously selecting the best treatment(s). Mixed treatment comparisons, a special case of network meta-analysis, combine direct evidence and indirect evidence for particular pairwise comparisons, thereby synthesizing a greater share of the available evidence than traditional meta-analysis. This report from the International Society for Pharmacoeconomics and Outcomes Research Indirect Treatment Comparisons Good Research Practices Task Force provides guidance on technical aspects of conducting network meta-analyses (our use of this term includes most methods that involve meta-analysis in the context of a network of evidence). We start with a discussion of strategies for developing networks of evidence. Next we briefly review assumptions of network meta-analysis. Then we focus on the statistical analysis of the data: objectives, models (fixed-effects and random-effects), frequentist versus Bayesian approaches, and model validation. A checklist highlights key components of network meta-analysis, and substantial examples illustrate indirect treatment comparisons (both frequentist and Bayesian approaches) and network meta-analysis. A further section discusses eight key areas for future research.

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