Your patient is a 45-year-old woman who experiences frequent migraine headaches that last from 4 to 24 hours and prevent her from attending work or looking after her children. She has exhausted efforts to manage the symptoms with nonsteroidal anti-inflammatory drugs and seeks additional treatment. You decide to recommend a triptan for the patient's migraine headaches but are wondering how to choose from the 7 available triptans. You retrieve a network meta-analysis (NMA) that evaluates the different triptans among this patient population.1 You are not familiar with this type of study, and you wonder if there are special issues to which you should attend in evaluating its methods and results.
You start by typing "migraine triptans" in the search box of an evidence-based summary website with which you are familiar. You find several chapters related to the management of migraine and drug information on the different drugs that are available. However, despite the profusion of evidence comparing single regimens, you wonder if all triptans have been compared, ideally in a single systematic review. To search for such a review, you type "migraine triptans comparison" in PubMed's Clinical Queries (http://www.ncbi.nlm.nih.gov/pubmed/clinical; see Chapter 5, Finding Current Best Evidence). In the results page, the middle column, which applies a broad filter for potential systematic reviews, retrieves 21 citations. The first strikes you as the most relevant to your question. It is a network meta-analysis that evaluates the different triptans among your patient population.1 You are not familiar with this type of study, and you wonder if there are special issues to which you should attend in evaluating its methods and results.
Traditionally, a meta-analysis addresses the merits of one intervention vs another (eg, placebo or another active intervention). Data are combined from all studies-often randomized clinical trials (RCTs)-that meet eligibility criteria in what we will term a pairwise meta-analysis. Compared with a single RCT, a meta-analysis improves the power to detect differences and also facilitates examination of the extent to which there are important differences in treatment effects across eligible RCTs-variability that is frequently called heterogeneity.2,3 Large unexplained heterogeneity may reduce a reader's confidence in estimates of treatment effect (see Chapter 23, Understanding and Applying the Results of a Systematic Review and Meta-analysis).
A drawback of traditional pairwise meta-analysis is that it evaluates the effects of only 1 intervention vs 1 comparator and does not permit inferences about the relative effectiveness of several interventions. For many medical conditions, however, there are a selection of interventions that have most frequently been compared with placebo and occasionally with one another.4,5 For example, despite 91 completed and ongoing RCTs that address the effectiveness of the 9 biologic drugs for the treatment of rheumatoid arthritis, only 5 compare biologics directly against each ...