A0682
Title: Precision of treatment hierarchy: A metric for quantifying certainty in treatment hierarchies from network meta-analysis
Authors: Augustine Wigle - University of Waterloo (Canada) [presenting]
Audrey Beliveau - University of Waterloo (Canada)
Georgia Salanti - University of Bern (Switzerland)
Gerta Rucker - University of Freiburg (Germany)
Guido Schwarzer - University of Freiburg (Germany)
Dimitris Mavridis - University of Ioannina (Greece)
Adriani Nikolakopoulou - Aristotle University of Thessaloniki (Greece)
Abstract: Network meta-analysis (NMA) is an extension of pairwise meta-analysis which facilitates the estimation of relative effects for multiple competing treatments. A hierarchy of treatments is a useful output of an NMA. Treatment hierarchies are produced using ranking metrics. Common ranking metrics include the Surface Under the Cumulative RAnking curve (SUCRA) and P-scores, which are the frequentist analogue to SUCRAs. Both metrics consider the size and uncertainty of the estimated treatment effects, with larger values indicating a more preferred treatment. Although SUCRAs and P-scores themselves consider uncertainty, treatment hierarchies produced by these ranking metrics are typically reported without a measure of certainty, which might be misleading to practitioners. We propose a new metric, Precision of Treatment Hierarchy (POTH), which quantifies the certainty of producing a treatment hierarchy from SUCRAs or P-scores. POTH provides a single, interpretable value which quantifies the degree of certainty in producing a treatment hierarchy. We show how the metric can be adapted to apply to subsets of treatments in a network, for example, to quantify the certainty in the hierarchy of the top three treatments. We calculate POTH for a database of NMAs to investigate its empirical properties, and we demonstrate its use on three published networks.
