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Ranking treatments in frequentist network meta-analysis works without resampling methods

Authors
  • Rücker, Gerta1
  • Schwarzer, Guido1
  • 1 Medical Center – University of Freiburg, Institute for Medical Biometry and Statistics, Stefan-Meier-Strasse 26, Freiburg, 79104, Germany , Freiburg (Germany)
Type
Published Article
Journal
BMC Medical Research Methodology
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Jul 31, 2015
Volume
15
Issue
1
Identifiers
DOI: 10.1186/s12874-015-0060-8
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundNetwork meta-analysis is used to compare three or more treatments for the same condition. Within a Bayesian framework, for each treatment the probability of being best, or, more general, the probability that it has a certain rank can be derived from the posterior distributions of all treatments. The treatments can then be ranked by the surface under the cumulative ranking curve (SUCRA). For comparing treatments in a network meta-analysis, we propose a frequentist analogue to SUCRA which we call P-score that works without resampling.MethodsP-scores are based solely on the point estimates and standard errors of the frequentist network meta-analysis estimates under normality assumption and can easily be calculated as means of one-sided p-values. They measure the mean extent of certainty that a treatment is better than the competing treatments.ResultsUsing case studies of network meta-analysis in diabetes and depression, we demonstrate that the numerical values of SUCRA and P-Score are nearly identical.ConclusionsRanking treatments in frequentist network meta-analysis works without resampling. Like the SUCRA values, P-scores induce a ranking of all treatments that mostly follows that of the point estimates, but takes precision into account. However, neither SUCRA nor P-score offer a major advantage compared to looking at credible or confidence intervals.

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