Research Techniques Made Simple: Network Meta-Analysis.

When making treatment decisions, it is often necessary to consider the relative efficacy and safety of multiple potential interventions. Unlike traditional pairwise meta-analysis, which allows for a comparison between two interventions by pooling head-to-head data, network meta-analysis (NMA) allows for the simultaneous comparison of more than two interventions and for comparisons to be made between interventions that have not been directly compared in a randomized controlled trial. Given these advantages, NMAs are being published in the medical literature with increasing frequency. However, there are important assumptions that researchers and knowledge users (e.g., patients, clinicians, and policy makers) must consider when conducting and evaluating an NMA: network connectivity, homogeneity, transitivity, and consistency. There are also multiple NMA outputs that researchers and knowledge users should familiarize themselves with in order to understand NMA results (e.g., network plots, mean ranks). Our goals in this article are to: (i) demonstrate how NMAs differ from pairwise meta-analyses, (ii) describe types of evidence in a NMA, (iii) explain NMA model assumptions, (iv) provide readers with an approach to interpreting a NMA, (v) discuss areas of ongoing methodological research, and (vi) provide a brief overview of how to conduct a systematic review and NMA.

[1]  Nicky J Welton,et al.  Network Meta-Analysis for Decision-Making , 2018 .

[2]  Panagiota Spyridonos,et al.  Graphical Tools for Network Meta-Analysis in STATA , 2013, PloS one.

[3]  A. Burden,et al.  Quantitative Evaluation of Biologic Therapy Options for Psoriasis: A Systematic Review and Network Meta-Analysis , 2017, The Journal of investigative dermatology.

[4]  J B Kadane,et al.  Prime time for Bayes. , 1995, Controlled clinical trials.

[5]  Areti Angeliki Veroniki,et al.  A scoping review of indirect comparison methods and applications using individual patient data , 2016, BMC Medical Research Methodology.

[6]  Gordon H Guyatt,et al.  Advances in the GRADE approach to rate the certainty in estimates from a network meta-analysis. , 2018, Journal of clinical epidemiology.

[7]  Cathal Walsh,et al.  Incorporating data from various trial designs into a mixed treatment comparison model , 2013, Statistics in medicine.

[8]  R. Dellavalle,et al.  The role of systematic reviews and meta-analysis in dermatology. , 2012, The Journal of investigative dermatology.

[9]  Sharon E. Straus,et al.  Characteristics and knowledge synthesis approach for 456 network meta-analyses: a scoping review , 2017, BMC Medicine.

[10]  Kristian Thorlund,et al.  The PRISMA Extension Statement for Reporting of Systematic Reviews Incorporating Network Meta-analyses of Health Care Interventions: Checklist and Explanations , 2015, Annals of Internal Medicine.

[11]  Joseph C Cappelleri,et al.  Indirect treatment comparison/network meta-analysis study questionnaire to assess relevance and credibility to inform health care decision making: an ISPOR-AMCP-NPC Good Practice Task Force report. , 2014, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[12]  Areti Angeliki Veroniki,et al.  The rank-heat plot is a novel way to present the results from a network meta-analysis including multiple outcomes. , 2016, Journal of clinical epidemiology.

[13]  Anna Chaimani,et al.  Evaluating the Quality of Evidence from a Network Meta-Analysis , 2014, PloS one.

[14]  Kristian Thorlund,et al.  How to use an article reporting a multiple treatment comparison meta-analysis. , 2012, JAMA.

[15]  K. Reich,et al.  Efficacy of biologics in the treatment of moderate to severe psoriasis: a network meta‐analysis of randomized controlled trials , 2012, The British journal of dermatology.