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Abstract:
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Network meta-analysis (NMA) is an important tool to provide high-quality evidence about available treatments for comparative effectiveness research. Compared with conventional meta-analyses that synthesize related studies for pairs of treatments separately, an NMA uses both direct and indirect evidence to simultaneously compare all available treatments for a certain disease. It is of primary interest for clinicians to rank these treatments and select the optimal ones for patients. Various methods have been proposed to evaluate treatment ranking; among them, the mean rank and the surface under the cumulative ranking curve (SUCRA) are widely used in current practice of NMAs. However, these measures only summarize treatment ranks among the studies collected in the NMA; due to heterogeneity between studies, they cannot predict treatment ranks in a future study and thus may not be directly applied to healthcare for new patients. We propose innovative measures to predict treatment ranks by accounting for the heterogeneity between the existing studies in an NMA and a new study. We use two illustrative examples and a simulation study to evaluate the performance of the proposed measures.
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