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

BMC Medical Research Methodology

Guido Schwarzer¹  Gerta Rücker¹ 

[1] Institute for Medical Biometry and Statistics, Medical Center – University of Freiburg, Stefan-Meier-Strasse 26, Freiburg 79104, Germany

关键词: AUC;    p-value;    SUCRA;    Surface under the cumulative ranking;    ‘Probability of being best’-statistic;    Ranking;    Network meta-analysis;   

Others  :  1222424
DOI  :  10.1186/s12874-015-0060-8

 received in 2015-05-06, accepted in 2015-07-23,  发布年份 2015

【 摘 要 】

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.

【 授权许可】

   
2015 Rücker and Schwarzer.

【 图 表 】

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