Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial.

By Georgia Salanti, A. Ades, John P. A. Ioannidis. Journal of Clinical Epidemiology, 2011

3,404 citations recorded by the source corpus.

Subfields: Statistics and Probability, Surgery

View Paper: https://www.semanticscholar.org/paper/3c216dc5035893717c47f0fe4375af0c529598b8

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Citations in the corpus

Conceptual and Technical Challenges in Network Meta-analysis (2013, background): “Probabilities for each treatment taking each possible rank can be plotted in absolute and cumulative rankograms (37, 41–43) or presented in a table (44) ( Appendix Figures 2 and 3 , available at www.annals.org).”
GetReal in network meta‐analysis: a review of the methodology (2016, background): “…Chung and Lumley, 2008; Senn et al., 2013), the assumptions made (Chaimani et al., 2013a; Krahn et al., 2013), and the results obtained from an NMA (Chaimani et al., 2013a; Chung and Lumley, 2008; Fadda et al., 2011; König et al., 2013; Salanti et al., 2011; Senn et al., 2013; Tan et al., 2014).”
Network Meta-Analysis Using R: A Review of Currently Available Automated Packages (2014, methodology): “However, neither package has an in-built function for plotting the surface under the cumulative ranking probabilities (SUCRA) [36].”
Graphical Tools for Network Meta-Analysis in STATA (2013, methodology): “So-called rankograms and cumulative ranking probability plots have been suggested as a reliable and comprehensive graphical way to present ranking probabilities and their uncertainty [2].”
Approaches to interpreting and choosing the best treatments in network meta-analyses (2017, background): “Other discussions have addressed reporting options, including ranking approaches, often assuming that readers have a sophisticated knowledge of analytic methods [9, 10].”
Evaluating the Quality of Evidence from a Network Meta-Analysis (2014, methodology): “The ranking of the treatments should be done using probabilistic methods, for example using ‘rankograms’ or the surface under the cumulative ranking curve (SUCRA), which take into account the estimated effect sizes and their accompanying uncertainty [8].”
Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression (2012, result): “For example, the trials that do not include treatment A may include a population for whom A is inappropriate and hence, their results may differ systematically from trials that do include A (Song et al., 2009).”
A systematic review and network meta‐analysis of immunotherapy and targeted therapy for advanced melanoma (2017, background): “0 with probability better than chemotherapy ≥ 95% were deemed to have a meaningful benefit as compared to chemotherapy [19].”
netmeta: An R Package for Network Meta-Analysis Using Frequentist Methods (2023, neutral): “Surface under the cumulative ranking curve (SUCRA) values provide a summary of the rankograms (Salanti, Ades, and Ioannidis 2011).”
Ranking treatments in frequentist network meta-analysis works without resampling methods (2015, neutral): “They also introduced several graphical presentations of ranking, such as rankograms, bar graphs and scatterplots [10, 17], and a numerical summary of the rank distribution, called the Surface Under the Cumulative RAnking curve (SUCRA) for each treatment [6, 18, 19].”
Network Meta-analysis (2015, methodology): “The SUCRA is the rescaled mean rank: it is 1 when a treatment is certain to be the best and 0 when a treatment is certain to be the worst. saving( ﬁlename , replace ) writes the draws from the posterior distribution (indexed by the identiﬁer and the replication number) to ﬁlename .”
Indirect and mixed‐treatment comparison, network, or multiple‐treatments meta‐analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool (2012, background): “A review of several of these methods can be found in Salanti et al. (2010a).”