An overview of conducting systematic reviews with network meta-analysis

Systematic reviews with network meta-analysis (NMA) are published with increasing frequency in the health care literature. Prior to 2008, very few systematic reviews contained a NMA 1; however, there has been a marked increase, to mid-2012 Lee recorded 201 published networks 2. The statistical method has been available since 2002 3,4 and owes its origins to much earlier work 5,6. NMA has matured and models are available for all types of underlying data and summary effect measures 7-12 and can be readily implemented in both frequentist and Bayesian frameworks with pre-written programmes available in widely used softwares 8,13-15. Recently, focus has shifted to making NMA more accessible 16,17; however, the conduct of systematic reviews for NMA has received less attention 18. In this special thematic series on network meta-analysis, the editors of Systematic Reviews are encouraging submissions of methodological papers concerning the conduct and reporting of meta-analyses and results papers (http://www.systematicreviewsjournal.com/about/update/SysRevCFP). As a preface to the series, this editorial provides an overview of the basic principles of NMA and summarises some of the key challenges for those conducting a systematic review. The need for network meta-analysis in comparative effectiveness research Why has NMA increased in popularity? To illustrate, consider the relative effectiveness of six psychotherapies vs. treatment as usual for treatment of moderate to severe depression 19. In a pairwise meta-analysis, the systematic reviewer has three synthesis options: (1) “lump” all six psychotherapies together to form a single comparator, (2) conduct six separate pairwise meta-analyses in a single systematic review, or (3) conduct six separate systematic reviews. If the question of interest to the decision-maker is “which psychotherapy should I recommend for depression?” the results of pairwise syntheses do not satisfactorily translate into practice. A clinician does not recommend an “average” psychotherapy to a patient but a specific one, such as cognitive behavioural therapy. To use results from options 2 and 3, the decision-maker must summarise across multiple analyses/reviews without formal assessment of whether the body evidence was coherent or similar enough to form a treatment recommendation. Such an approach makes effect estimates problematic to interpret and is not recommended 20. NMA came to prominence within this decision-making context 21,22. NMA is the simultaneous comparison of multiple competing treatments in a single statistical model 23. In its simplest form, it is the combination of direct and indirect estimates of relative treatment effect, where indirect evidence refers to evidence on treatment C relative to B obtained from A vs. B and A vs. C studies. This is commonly depicted by the equation θBCI=θACD-θABD where θ denotes the true underlying treatment effect estimate (e.g. log odds ratio, mean difference, etc.) and the superscript either Direct or Indirect evidence. If both direct and indirect estimates are available, they can be pooled to produce an internally coherent set of effect estimates of each treatment relative to every other whether or not they have been compared in head-to-head trials. It is also possible to calculate the probability of one treatment being the best for a specific outcome. Treatment options can then be ranked from the best to the worst for each outcome.