Graphical Tools for Network Meta-Analysis in STATA

By A. Chaimani, Julian P. T. Higgins, Dimitris Mavridis, Panagiota Spyridonos, Georgia Salanti. PLoS ONE, 2013

2,263 citations recorded by the source corpus.

Network meta-analysis synthesizes direct and indirect evidence in a network of trials that compare multiple interventions and has the potential to rank the competing treatments according to the studied outcome. Despite its usefulness network meta-analysis is often criticized for its complexity and for being accessible only to researchers with strong statistical and computational skills. The evaluation of the underlying model assumptions, the statistical technicalities and presentation of the results in a concise and understandable way are all challenging aspects in the network meta-analysis methodology. In this paper we aim to make the methodology accessible to non-statisticians by presenting and explaining a series of graphical tools via worked examples. To this end, we provide a set of STATA routines that can be easily employed to present the evidence base, evaluate the assumptions, fit the network meta-analysis model and interpret its results.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c99555607f865f8e0d32e5054195b29ccecc06cf

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Citations in the corpus

Network Meta-Analysis: An Introduction for Clinicians (2016, background): “appropriate to infer on treatment ranking using the mean rank, or the cumulative ranking probabilities [22].”
Visualizing Assumptions and Results in Network Meta-analysis: The Network Graphs Package (2015, methodology): “Diﬀerences in the relative eﬀects between small and large trials in a network of interventions often challenge the interpretation of the pairwise summary eﬀects and need exploration (Chaimani et al. 2013b; Moreno et al. 2011).”
netmeta: An R Package for Network Meta-Analysis Using Frequentist Methods (2023, positive): “The function funnel.netmeta() generates a “comparison-adjusted” funnel plot to assess funnel plot asymmetry in an NMA (Chaimani and Salanti 2012; Chaimani, Higgins, Mavridis, Spyridonos, and Salanti 2013).”
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].”
Approaches to interpreting and choosing the best treatments in network meta-analyses (2017, background): “However, the results of NMAs may be complex and difficult to interpret for clinicians especially when there are many alternative strategies and outcomes to consider [6, 7].”
GetReal in network meta‐analysis: a review of the methodology (2016, methodology): “2016, 7 236–263 2 4 6 (2013), and Zhang et al. (2013); some papers used STATA (StataCorp., 2013): Ballesteros (2005), Chaimani et al. (2013a), Franchini et al. (2012), Higgins et al. (2012), Jonas et al. (2013), Madan et al. (2011), Norton et al. (2012), Tu and Faggion (2012), Veroniki et al.…”
Evaluating the Quality of Evidence from a Network Meta-Analysis (2014, methodology): “The contribution plot can be obtained in STATA (see [14]).”
Network Meta-analysis (2015, methodology): “Many graphical tools for displaying the evidence base and the results of network meta-analysis have been written by Chaimani et al. (2013).”