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dc.contributor.authorRücker, Gerta
dc.contributor.authorSchwarzer, Guido
dc.date.accessioned2015-09-01T18:44:08Z
dc.date.available2015-09-01T18:44:08Z
dc.date.issued2015-07-31
dc.identifier.citationBMC Medical Research Methodology. 2015 Jul 31;15(1):58
dc.identifier.urihttp://dx.doi.org/10.1186/s12874-015-0060-8
dc.identifier.urihttp://hdl.handle.net/10724/32058
dc.description.abstractAbstract Background Network 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. Methods P-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. Results Using case studies of network meta-analysis in diabetes and depression, we demonstrate that the numerical values of SUCRA and P-Score are nearly identical. Conclusions Ranking 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.
dc.titleRanking treatments in frequentist network meta-analysis works without resampling methods
dc.typeJournal Article
dc.date.updated2015-07-31T03:47:32Z
dc.language.rfc3066en
dc.rights.holderRücker and Schwarzer.


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