A graphical tool for locating inconsistency in network meta-analyses

By U. Krahn, Harald Binder, Jochem König. BMC Medical Research Methodology, 2013

421 citations recorded by the source corpus.

BackgroundIn network meta-analyses, several treatments can be compared by connecting evidence from clinical trials that have investigated two or more treatments. The resulting trial network allows estimating the relative effects of all pairs of treatments taking indirect evidence into account. For a valid analysis of the network, consistent information from different pathways is assumed. Consistency can be checked by contrasting effect estimates from direct comparisons with the evidence of the remaining network. Unfortunately, one deviating direct comparison may have side effects on the network estimates of others, thus producing hot spots of inconsistency.MethodsWe provide a tool, the net heat plot, to render transparent which direct comparisons drive each network estimate and to display hot spots of inconsistency: this permits singling out which of the suspicious direct comparisons are sufficient to explain the presence of inconsistency. We base our methods on fixed-effects models. For disclosure of potential drivers, the plot comprises the contribution of each direct estimate to network estimates resulting from regression diagnostics. In combination, we show heat colors corresponding to the change in agreement between direct and indirect estimate when relaxing the assumption of consistency for one direct comparison. A clustering procedure is applied to the heat matrix in order to find hot spots of inconsistency.ResultsThe method is shown to work with several examples, which are constructed by perturbing the effect of single study designs, and with two published network meta-analyses. Once the possible sources of inconsistencies are identified, our method also reveals which network estimates they affect.ConclusionOur proposal is seen to be useful for identifying sources of inconsistencies in the network together with the interrelatedness of effect estimates. It opens the way for a further analysis based on subject matter considerations.

Subfields: Experimental and Cognitive Psychology, Food Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/43c819d7e5ae0b7f2f2960aed6965f87dcc97982

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Citations in the corpus

Network Meta-Analysis Using R: A Review of Currently Available Automated Packages (2014, methodology): “Details about the specific data input and analysis options, statistical models, methods, and formulations used in the three packages can be found in the respective reference manuals and original articles: van Valkenhoef et al [17] for gemtc , Zhang et al [18] for pcnetmeta , and Konig et al [19] and”
GetReal in network meta‐analysis: a review of the methodology (2016, methodology): “…articles that reported the use of R (R Core Team, 2014): Chung and Lumley (2008), Dias et al. (2010b), Donegan et al. (2012, 2013a), Jansen (2012), Krahn et al. (2013), Lu et al. (2011), Lu and Ades (2009), Lumley (2002), Mills et al. (2011b, 2013a), Naudet et al. (2013), Rücker (2012), Rücker…”
Evaluating the Quality of Evidence from a Network Meta-Analysis (2014, background): “direct estimate to the network as a whole [13].”
netmeta: An R Package for Network Meta-Analysis Using Frequentist Methods (2023, positive): “The net heat plot (Krahn et al. 2013) implemented in netheat() visualizes hot spots of inconsistency in an NMA.”
Graphical Tools for Network Meta-Analysis in STATA (2013, background): “Graphical tools may help investigators to spot parts of the evidence network that appear inconsistent, or to inform judgments about the plausibility of consistency [8].”
Visualizing Assumptions and Results in Network Meta-analysis: The Network Graphs Package (2015, methodology): “When performing a network meta-analysis, each direct comparison contributes to the estimation of each network meta-analytic summary eﬀect by a diﬀerent weight (Krahn, Binder, and K¨onig 2013; Lu et al. 2011).”
Advances in the GRADE approach to rate the certainty in estimates from a network meta-analysis. (2018, background): “Therefore, reviewers can use the matrix to derive the proportion of the information contributing to each network estimate that comes from direct and indirect evidence [11].”