Appropriate treatment of AR might improve patients' quality of life and school and work productivity. ARIA recommendations support patients, their caregivers, and health care providers in choosing the optimal treatment.

Network meta-analysis (NMA), combining direct and indirect comparisons, is increasingly being used to examine the comparative effectiveness of medical interventions. Minimal guidance exists on how to rate the quality of evidence supporting treatment effect estimates obtained from NMA. We present a four-step approach to rate the quality of evidence in each of the direct, indirect, and NMA estimates based on methods developed by the GRADE working group. Using an example of a published NMA, we show that the quality of evidence supporting NMA estimates varies from high to very low across comparisons, and that quality ratings given to a whole network are uninformative and likely to mislead. Network meta-analysis (NMA) that simultaneously addresses the comparative effectiveness and/or safety of multiple interventions through combining direct and indirect estimates of effect is rapidly gaining popularity and influence. 1-6 Although NMA approaches appear attractive, 6-8 application of their results requires understanding the quality of the evidence. By quality of evidence, we mean the degree of confidence or certainty one can place in estimates of treatment effects.NMA is sufficiently new that terminology differs between authors and continues to evolve. Box 1 presents a glossary of terms used in this article.Rationale for an approach to rate the quality of evidence from NMA Recently, several articles have provided guidance regarding identification of the evidence for a NMA, 9 statistical aspects of conducting NMA, 10-17 and critical appraisal and interpretation of published NMA.18 19 Few of these, however, provide explicit guidance on how to rate the quality of the evidence. Reports of NMAs often describe the risk of bias of trials included in a NMA (such as method of randomisation, concealment of random allocation, masking, etc). [22][23][24] For example, a recent NMA compared the effects of coronary artery bypass grafting, various stents, and medical treatment on mortality, myocardial infarction, and the need for revascularisation among patients with stable coronary artery disease. The authors stated that appropriate methods of concealment of random allocation were reported for 71 trials (71%). 25 Fifty six trials (56%) reported blind adjudication of clinical outcomes, and for 69 trials (69%) data from intention to treat analyses were available. Although such an assessment of risk of bias describes the entire body of evidence (that is, all trials contributing evidence to the NMA), it does not acknowledge that the risk of bias is likely to differ across the comparisons of the network.1 For example, the risk of bias of studies comparing sirolimus eluting stents versus medical treatment may be considerably less than the risk of bias of studies comparing coronary artery bypass grafting with medical treatment. In addition, risk of bias is only one determinant of quality of evidence. Our confidence in effect estimates will, for instance, also decrease if there are large differences in results from study to study (for exampl...

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ObjectiveTo compare the effects of treatments for coronavirus disease 2019 (covid-19).DesignLiving systematic review and network meta-analysis.Data sourcesUS Centers for Disease Control and Prevention COVID-19 Research Articles Downloadable Database, which includes 25 electronic databases and six additional Chinese databases to 20 July 2020.Study selectionRandomised clinical trials in which people with suspected, probable, or confirmed covid-19 were randomised to drug treatment or to standard care or placebo. Pairs of reviewers independently screened potentially eligible articles.MethodsAfter duplicate data abstraction, a bayesian random effects network meta-analysis was conducted. Risk of bias of the included studies was assessed using a modification of the Cochrane risk of bias 2.0 tool, and the certainty of the evidence using the grading of recommendations assessment, development and evaluation (GRADE) approach. For each outcome, interventions were classified in groups from the most to the least beneficial or harmful following GRADE guidance.Results23 randomised controlled trials were included in the analysis performed on 26 June 2020. The certainty of the evidence for most comparisons was very low because of risk of bias (lack of blinding) and serious imprecision. Glucocorticoids were the only intervention with evidence for a reduction in death compared with standard care (risk difference 37 fewer per 1000 patients, 95% credible interval 63 fewer to 11 fewer, moderate certainty) and mechanical ventilation (31 fewer per 1000 patients, 47 fewer to 9 fewer, moderate certainty). These estimates are based on direct evidence; network estimates for glucocorticoids compared with standard care were less precise because of network heterogeneity. Three drugs might reduce symptom duration compared with standard care: hydroxychloroquine (mean difference −4.5 days, low certainty), remdesivir (−2.6 days, moderate certainty), and lopinavir-ritonavir (−1.2 days, low certainty). Hydroxychloroquine might increase the risk of adverse events compared with the other interventions, and remdesivir probably does not substantially increase the risk of adverse effects leading to drug discontinuation. No other interventions included enough patients to meaningfully interpret adverse effects leading to drug discontinuation.ConclusionGlucocorticoids probably reduce mortality and mechanical ventilation in patients with covid-19 compared with standard care. The effectiveness of most interventions is uncertain because most of the randomised controlled trials so far have been small and have important study limitations.Systematic review registrationThis review was not registered. The protocol is included as a supplement.Readers’ noteThis article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication.