David C. Hoaglin
Jeroen P. Jansen
David A. Scott
Joseph C. Cappelleri
David Thompson, Australian Catholic UniversityFollow
Kay M. Larholt
Hoaglin, D. C, Hawkins, N., Jansen, J. P, Scott, D. A, Itzler, R., Cappelleri, J. C, Boersma, C., Thompson, D., Larholt, K. M, Diaz, M. & Barrett, A. (2011). Conducting indirect-treatment-comparison and network-meta-analysis studies: Report of the ISPOR task force on indirect treatment comparisons good research practices: Part 2. Value in Health, United Kingdom: Blackwell Publishing Inc.. Retrieved from https://doi.org/10.1016/j.jval.2011.01.011
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.