Communication of Treatment Rankings Obtained From Network Meta-Analysis Using Data Visualization.

The number of published network meta-analysis (NMA) reports has increased substantially in recent years. NMAs combine direct and indirect evidence and enable comparisons between all relevant treatment options for a given disease, even when some treatments have not been directly compared with each other. In the absence of randomized trials comparing all treatment options to each other, NMAs address important information needs of patients and clinicians about the comparative effectiveness of treatment alternatives. NMA results may be difficult to communicate and interpret effectively given the large volume of complex information generated on multiple alternative treatments with multiple benefit and harm outcomes. For example, NMA comparing 5 treatments result in 10 pair-wise comparisons; if results are available for 3 benefit and 3 harm outcomes, decision makers are faced with 60 sets of results. Identifying the best treatment option to initiate therapy is, thus, not straightforward. Although several graphical and tabular displays exist to report the pertinent results of NMAs, existing reporting guidelines differ in their recommendations. Consequently, there is significant variation in the current way NMA findings are reported and presented. A key strength of NMAs is the ability to rank treatments. However, such rankings are specific to individual outcomes and often change significantly across different benefit and harm end points. For example, a treatment that performs well in prolonging survival may fare unfavorably in terms of increasing the likelihood of side effects. Combining the relative performance of different treatments on multiple outcomes remains a challenge. One option for generating a single coherent ranking of treatments is to quantitatively combine NMA findings with patient preferences. Preference information captures the relative importance of attributes that differ among alternative treatments. For example, a patient near the end of life may prefer a therapeutic strategy that minimizes drugrelated side effects (however minor), even if the therapy has less potential for prolonging survival than its alternatives. In such a scenario, survival end points would carry less weight than adverse outcomes, favoring the treatments that have superior side effect profiles. Combination of NMA findings with preference information would allow for generating and communicating a coherent ranking of all treatment alternatives. Statin Ranking Tool A recent series of NMAs evaluated the comparative benefits and harms of widely popular cholesterol-lowering medications, statins, on the basis of a systematically identified set of randomized trials published since 1990. While some statins performed well in terms of benefit outcomes (measured in terms of reducing the risk of all-cause mortality, coronary, and cerebrovascular events), others had relatively favorable side effect profiles (measured in terms of increasing the risk of experiencing creatine kinase elevations, transaminase elevations, myalgia, and treatment discontinuations because of other adverse events). These analyses did not identify a single statin that outperformed its comparators in terms of both benefit and harm outcomes. Data visualization offers an opportunity to communicate the top-level findings of these detailed analyses. The Statin Ranking Tool (available at http://lse.live.kiln.digital/statins/) uses a visually appealing, intuitive, and easy-to-navigate interactive interface to summarize the key findings of several published NMAs comparing the effectiveness of individual statins on 3 benefit and 4 harm outcomes. Focusing on five of the most commonly used statins (atorvastatin, lovastatin, pravastatin, rosuvastatin, and simvastatin), it excludes fluvastatin (rarely used in clinical practice) and pitavastatin (previous NMAs did not consider its effect on mortality, coronary, and cerebrovascular events). This tool was developed as part of the London School of Economics and Political Science Institute of Public Affairs data visualization project, which aims to make academic research visible and accessible to the wider public. The Statin Ranking Tool has 2 data visualization features. The first feature presents the previously published rankings of individual statins on 7 outcomes, both separately and in combination with each other (Figure 1). Treatment rankings are expressed as probabilities (adding up to one for each statin and for each rank) and reflect the estimated effect sizes obtained from NMAs. As described previously, the surface under the cumulative ranking line (also known as SUCRA) for each treatment provides a numeric summary of the overall performance of each statin on each outcome (1.0 when a statin is certain to the best and 0.0 when it is certain to be the worst), taking into account not only the magnitude of the effect but also the uncertainty around it.