• Tue, Feb 12th 2019 at 16:00 - 17:00 UTC (Other timezones)
  • General participation info   |   Participate online   |   + Phone in United States (Toll Free): 1 877 309 2073 United States: +1 (571) 317-3129 Australia (Toll Free): 1 800 193 385 Australia: +61 2 8355 1020 Austria (Toll Free): 0 800 202148 Belgium (Toll Free): 0 800 78884 Canada (Toll Free): 1 888 455 1389 Denmark (Toll Free): 8090 1924 France (Toll Free): 0 805 541 047 Germany (Toll Free): 0 800 184 4222 Greece (Toll Free): 00 800 4414 3838 Hungary (Toll Free): (06) 80 986 255 Iceland (Toll Free): 800 9869 Ireland (Toll Free): 1 800 946 538 Israel (Toll Free): 1 809 454 830 Italy (Toll Free): 800 793887 Japan (Toll Free): 0 120 663 800 Luxembourg (Toll Free): 800 22104 Netherlands (Toll Free): 0 800 020 0182 New Zealand (Toll Free): 0 800 47 0011 Norway (Toll Free): 800 69 046 Poland (Toll Free): 00 800 1213979 Portugal (Toll Free): 800 819 575 Spain (Toll Free): 800 900 582 Sweden (Toll Free): 0 200 330 905 Switzerland (Toll Free): 0 800 740 393 United Kingdom (Toll Free): 0 800 169 0432 Access Code: 731-636-357

Background: A quantitative synthesis of evidence via standard pair-wise meta-analysis lies on the top of the hierarchy for evaluating the relative effectiveness or safety between two interventions. In most healthcare problems, however, there is a plethora of competing interventions. Network meta-analysis allows to rank competing interventions and evaluate their relative effectiveness even if they have not been compared in an individual trial.

Aim: The aim of this presentation is to explain and discuss the main features of this statistical technique (key assumptions underlying network meta-analysis and graphical methods to visualise results and information in the network).

Methods: We will use one illustrative example that compared the relative effectiveness of 21 antidepressants and placebo in major depression.

Results: A network plot allows to visualise how information flows in the network and reveals important information about network geometry. Discrepancies between direct and indirect evidence can be detected using inconsistency plots. Relative effectiveness or safety of competing interventions can be presented in a league table. A contribution plot reveals the contribution of each direct comparison to each network estimate. A comparison-adjusted funnel plot is an extension of simple funnel plot to network meta-analysis. A rank probability matrix can be estimated to present the probabilities of all interventions assuming each rank and can be represented using rankograms and cumulative probability plots.

Conclusions: Network meta-analysis is very helpful in comparing the relative effectiveness and acceptability of competing treatments. Several issues, however, still need to be addressed when conducting a network meta-analysis for the results to be valid and correctly interpreted.

Prof. Andrea Cipriani
Associate Professor, Department of Psychiatry, University of Oxford
Honorary Consultant Psychiatrist, Oxford Health NHS Foundation Trust, Oxford
Associate Director, R&D, Oxford Health NHS Foundation Trust, Oxford

 

 

 

Andrea Cipriani – Innovative methods of evidence synthesis in Evidence Based Mental Health: a primer on network meta-analyses