A0996
Title: What works best: Methods for ranking competing treatments in network meta-analysis
Authors: Adriani Nikolakopoulou - Aristotle University of Thessaloniki (Greece) [presenting]
Abstract: The most critical question raised by patients and clinicians at the point of care is: What is the drug of choice for the given condition? Comparative effectiveness research and its quantitative component, network meta-analysis (NMA), have been used to answer this question. NMA estimates all relative effects between competing treatments and can produce statistical ranking metrics (such as the SUCRA, the surface under the cumulative ranking curve) that lead to a treatment hierarchy from the least desirable to the most desirable option. While clinicians and guideline developers unanimously agree that a treatment hierarchy is essential, methodologists debate several issues underpinning the ranking metrics obtained from NMA. Although about half of the published NMAs report a treatment hierarchy for their primary outcome, methods for simultaneously ranking treatments for multiple outcomes, for example, efficacy and safety, are underdeveloped. Finally, although uncertainty around ranks could be calculated, a single measure of the precision of the most likely treatment hierarchy is still needed. The aim is to present emerging methods for ranking treatments in NMA, including re-parametrization of the standard NMA model and different probabilistic summary statistics that account for clinically important differences between treatments and patient values and a newly suggested measure of the precision of treatment hierarchy.
