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Description of network meta-analysis geometry: A metrics design study.

ABSTRACT: BACKGROUND:The conduction and report of network meta-analysis (NMA), including the presentation of the network-plot, should be transparent. We aimed to propose metrics adapted from graph theory and social network-analysis literature to numerically describe NMA geometry. METHODS:A previous systematic review of NMAs of pharmacological interventions was performed. Data on the graph's presentation were collected. Network-plots were reproduced using Gephi 0.9.1. Eleven geometric metrics were tested. The Spearman test for non-parametric correlation analyses and the Bland-Altman and Lin's Concordance tests were performed (IBM SPSS Statistics 24.0). RESULTS:From the 477 identified NMAs only 167 graphs could be reproduced because they provided enough information on the plot characteristics. The median nodes and edges were 8 (IQR 6-11) and 10 (IQR 6-16), respectively, with 22 included studies (IQR 13-35). Metrics such as density (median 0.39, ranged 0.07-1.00), median thickness (2.0, IQR 1.0-3.0), percentages of common comparators (median 68%), and strong edges (median 53%) were found to contribute to the description of NMA geometry. Mean thickness, average weighted degree and average path length produced similar results than other metrics, but they can lead to misleading conclusions. CONCLUSIONS:We suggest the incorporation of seven simple metrics to report NMA geometry. Editors and peer-reviews should ensure that guidelines for NMA report are strictly followed before publication.

SUBMITTER: Tonin FS

PROVIDER: S-EPMC6382117 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Description of network meta-analysis geometry: A metrics design study.

Tonin Fernanda S FS Borba Helena H HH Mendes Antonio M AM Wiens Astrid A Fernandez-Llimos Fernando F Pontarolo Roberto R

PloS one 20190220 2

<h4>Background</h4>The conduction and report of network meta-analysis (NMA), including the presentation of the network-plot, should be transparent. We aimed to propose metrics adapted from graph theory and social network-analysis literature to numerically describe NMA geometry.<h4>Methods</h4>A previous systematic review of NMAs of pharmacological interventions was performed. Data on the graph's presentation were collected. Network-plots were reproduced using Gephi 0.9.1. Eleven geometric metric ...[more]

PMID: 30785955

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Project description:ObjectiveTo empirically explore the level of agreement of the treatment hierarchies from different ranking metrics in network meta-analysis (NMA) and to investigate how network characteristics influence the agreement.DesignEmpirical evaluation from re-analysis of NMA.Data232 networks of four or more interventions from randomised controlled trials, published between 1999 and 2015.MethodsWe calculated treatment hierarchies from several ranking metrics: relative treatment effects, probability of producing the best value [Formula: see text] and the surface under the cumulative ranking curve (SUCRA). We estimated the level of agreement between the treatment hierarchies using different measures: Kendall's τ and Spearman's ρ correlation; and the Yilmaz [Formula: see text] and Average Overlap, to give more weight to the top of the rankings. Finally, we assessed how the amount of the information present in a network affects the agreement between treatment hierarchies, using the average variance, the relative range of variance and the total sample size over the number of interventions of a network.ResultsOverall, the pairwise agreement was high for all treatment hierarchies obtained by the different ranking metrics. The highest agreement was observed between SUCRA and the relative treatment effect for both correlation and top-weighted measures whose medians were all equal to 1. The agreement between rankings decreased for networks with less precise estimates and the hierarchies obtained from [Formula: see text] appeared to be the most sensitive to large differences in the variance estimates. However, such large differences were rare.ConclusionsDifferent ranking metrics address different treatment hierarchy problems, however they produced similar rankings in the published networks. Researchers reporting NMA results can use the ranking metric they prefer, unless there are imprecise estimates or large imbalances in the variance estimates. In this case treatment hierarchies based on both probabilistic and non-probabilistic ranking metrics should be presented.

Dataset Information

Description of network meta-analysis geometry: A metrics design study.

Publications

Description of network meta-analysis geometry: A metrics design study.

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