Ranking of Treatments in Network Meta-Analysis: Incorporating Minimally Important Differences

Abstract Background In network meta-analysis (NMA), the magnitude of difference between treatment effects is typically ignored in the calculation of ranking metrics, such as probability best and surface under the cumulative ranking curve (SUCRAs). Methods Analogues to commonly available NMA ranking metrics that account for minimally important differences (MIDs) are provided. In particular, definitions are provided for MID-adjusted median ranks, MID-adjusted probability jth, MID-adjusted cumulative probability jth, and MID-adjusted SUCRA values. Since adjustment for MIDs allows for ties between treatments in a network, methods for handling ties in ranking are discussed, with it shown that the midpoint method for handling ties retains the property that the average value of all SUCRA values in a network is one half. Comparability of MID-adjusted P-scores and MID-adjusted SUCRA values is discussed, and a Bayesian software implementation of the MID-adjusted ranking metrics is provided. Results Two real-world applications of MID-adjusted ranking metrics are presented to illustrate their use. Specifically, NMAs are conducted based on published networks on treatments for diabetes and Parkinson’s disease. To present the results, MIDs are selected from relevant literature to interpret MID-adjusted ranking metrics for these networks. Conclusions Although dependent on the magnitude of the MID and the magnitude of difference between treatments, failure to consider MIDs in ranking treatments can lead to erroneous conclusions of differences when ranking treatments where none of clinical relevance exists.