Areti Angeliki Veroniki1, Sharon E Straus2, Alexandros Fyraridis1, Andrea C Tricco3. 1. Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, 209 Victoria Street, East Building, Toronto, Ontario M5B 1T8, Canada. 2. Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, 209 Victoria Street, East Building, Toronto, Ontario M5B 1T8, Canada; Department of Geriatric Medicine, University of Toronto, Toronto, Ontario, Canada. 3. Knowledge Translation Program, Li Ka Shing Knowledge Institute, St. Michael's Hospital, 209 Victoria Street, East Building, Toronto, Ontario M5B 1T8, Canada; Epidemiology Division, Dalla Lana School of Public Health, University of Toronto, 155 College Street, 6th Floor, Toronto, Ontario M5T 3M7, Canada. Electronic address: triccoa@smh.ca.
Abstract
OBJECTIVES: To present a novel and simple graphical approach to improve the presentation of the treatment ranking in a network meta-analysis (NMA) including multiple outcomes. STUDY DESIGN AND SETTING: NMA simultaneously compares many relevant interventions for a clinical condition from a network of trials, and allows ranking of the effectiveness and/or safety of each intervention. There are numerous ways to present the NMA results, which can challenge their interpretation by research users. The rank-heat plot is a novel graph that can be used to quickly recognize which interventions are most likely the best or worst interventions with respect to their effectiveness and/or safety for a single or multiple outcome(s) and may increase interpretability. RESULTS: Using empirical NMAs, we show that the need for a concise and informative presentation of results is imperative, particularly as the number of competing treatments and outcomes in an NMA increases. CONCLUSION: The rank-heat plot is an efficient way to present the results of ranking statistics, particularly when a large amount of data is available, and it is targeted to users from various backgrounds.
OBJECTIVES: To present a novel and simple graphical approach to improve the presentation of the treatment ranking in a network meta-analysis (NMA) including multiple outcomes. STUDY DESIGN AND SETTING: NMA simultaneously compares many relevant interventions for a clinical condition from a network of trials, and allows ranking of the effectiveness and/or safety of each intervention. There are numerous ways to present the NMA results, which can challenge their interpretation by research users. The rank-heat plot is a novel graph that can be used to quickly recognize which interventions are most likely the best or worst interventions with respect to their effectiveness and/or safety for a single or multiple outcome(s) and may increase interpretability. RESULTS: Using empirical NMAs, we show that the need for a concise and informative presentation of results is imperative, particularly as the number of competing treatments and outcomes in an NMA increases. CONCLUSION: The rank-heat plot is an efficient way to present the results of ranking statistics, particularly when a large amount of data is available, and it is targeted to users from various backgrounds.
Authors: Andrea C Tricco; Sonia M Thomas; Areti Angeliki Veroniki; Jemila S Hamid; Elise Cogo; Lisa Strifler; Paul A Khan; Reid Robson; Kathryn M Sibley; Heather MacDonald; John J Riva; Kednapa Thavorn; Charlotte Wilson; Jayna Holroyd-Leduc; Gillian D Kerr; Fabio Feldman; Sumit R Majumdar; Susan B Jaglal; Wing Hui; Sharon E Straus Journal: JAMA Date: 2017-11-07 Impact factor: 56.272
Authors: M A Siciliano; G Caridà; D Ciliberto; M d'Apolito; C Pelaia; D Caracciolo; C Riillo; P Correale; A Galvano; A Russo; V Barbieri; P Tassone; P Tagliaferri Journal: ESMO Open Date: 2022-04-12
Authors: Andrea C Tricco; Erik Blondal; Areti Angeliki Veroniki; Charlene Soobiah; Afshin Vafaei; John Ivory; Lisa Strifler; Roberta Cardoso; Emily Reynen; Vera Nincic; Huda Ashoor; Joanne Ho; Carmen Ng; Christy Johnson; Erin Lillie; Jesmin Antony; Derek J Roberts; Brenda R Hemmelgarn; Sharon E Straus Journal: BMC Med Date: 2016-12-23 Impact factor: 8.775
Authors: Ahreum Lee; Caitlin McArthur; Areti Angeliki Veroniki; Monika Kastner; George Ioannidis; Lauren E Griffith; Lehana Thabane; Jonathan D Adachi; Alexandra Papaioannou Journal: BMJ Open Date: 2021-07-05 Impact factor: 2.692