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Traditional visualization in meta-analysis uses forest plots to illustrate the combined treatment effect, along with the respective results from primary trials. While the purpose of visualization is clear in the pairwise setting, additional treatments broaden the focus and extend the results to be illustrated in network meta-analysis. The complexity increases further in situations where all potential contrasts in the network are compared to a predefined fixed value of interest, such as the 95% coverage evaluated against the nominal value of 95% in simulation studies. We propose utilizing radar graphs to illustrate results from network meta-analysis in cases where the interest lies in the comparison of estimated results (after fitting a network meta-analysis in a specific data set) or a performance measure (simulation study) to a pre-defined fixed reference value. Accounting for the complex high-dimensional data structure, the general picture of the full network is captured at once without increasing the space needed for visualization. Especially in large simulation studies, where multiple scenarios need to be visually combined to gain an overview on different scenarios, this type of illustration facilitates the discussion of results. Further properties, such as the expected variation due to the Monte-Carlo error or the differentiation between directly and indirectly estimated treatment contrasts in simulation studies, as well as the indication of well-connected and sparsely connected treatments in an applied network meta-analysis, can additionally be included in the visualization. While we used the radar-graph mainly for a simulation study, other applications are suitable whenever relative contributions of treatment (contrasts) are of interest.

The performance of statistical methods is often evaluated by means of simulation studies. In case of network meta-analysis of binary data, however, simulations are not currently available for many practically relevant settings. We perform a simulation study for sparse networks of trials under between-trial heterogeneity and including multi-arm trials. Results of the evaluation of two popular frequentist methods and a Bayesian approach using two different prior specifications are presented. Methods are evaluated using coverage, width of intervals, bias, and root mean squared error (RMSE). In addition, deviations from the theoretical surface under the cumulative rankings (SUCRAs) or P-scores of the treatments are evaluated. Under low heterogeneity and when a large number of trials informs the contrasts, all methods perform well with respect to the evaluated performance measures. Coverage is observed to be generally higher for the Bayesian than the frequentist methods. The width of credible intervals is larger than those of confidence intervals and is increasing when using a flatter prior for between-trial heterogeneity. Bias was generally small, but increased with heterogeneity, especially in netmeta. In some scenarios, the direction of bias differed between frequentist and Bayesian methods. The RMSE was comparable between methods but larger in indirectly than in directly estimated treatment effects. The deviation of the SUCRAs or P-scores from their theoretical values was mostly comparable over the methods but differed depending on the heterogeneity and the geometry of the investigated network. Multivariate meta-regression or Bayesian estimation using a half-normal prior scaled to 0.5 seems to be promising with respect to the evaluated performance measures in network meta-analysis of sparse networks.

Aims Whether and how iron affects the progression of atherosclerosis remains highly debated. Here, we investigate susceptibility to atherosclerosis in a mouse model (ApoE−/− FPNwt/C326S), which develops the disease in the context of elevated non-transferrin bound serum iron (NTBI). Methods and results Compared with normo-ferremic ApoE−/− mice, atherosclerosis is profoundly aggravated in iron-loaded ApoE−/− FPNwt/C326S mice, suggesting a pro-atherogenic role for iron. Iron heavily deposits in the arterial media layer, which correlates with plaque formation, vascular oxidative stress and dysfunction. Atherosclerosis is exacerbated by iron-triggered lipid profile alterations, vascular permeabilization, sustained endothelial activation, elevated pro-atherogenic inflammatory mediators, and reduced nitric oxide availability. NTBI causes iron overload, induces reactive oxygen species production and apoptosis in cultured vascular cells, and stimulates massive MCP-1-mediated monocyte recruitment, well-established mechanisms contributing to atherosclerosis. NTBI-mediated toxicity is prevented by transferrin- or chelator-mediated iron scavenging. Consistently, a low-iron diet and iron chelation therapy strongly improved the course of the disease in ApoE−/− FPNwt/C326S mice. Our results are corroborated by analyses of serum samples of haemochromatosis patients, which show an inverse correlation between the degree of iron depletion and hallmarks of endothelial dysfunction and inflammation. Conclusion Our data demonstrate that NTBI-triggered iron overload aggravates atherosclerosis and unravel a causal link between NTBI and the progression of atherosclerotic lesions. Our findings support clinical applications of iron restriction in iron-loaded individuals to counteract iron-aggravated vascular dysfunction and atherosclerosis. Open in new tab Download slide Open in new tab Download slide [ABSTRACT FROM AUTHOR]