Communication of Treatment Rankings Obtained From Network Meta-Analysis Using Data Visualization
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- cardiovascular agents
- cardiovascular diseases
- coronary disease
- decision making, shared
- evidence-based medicine
- hydroxymethylglutaryl-CoA reductase inhibitors
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.1 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 drug-related side effects (however minor), even …