A comparison of Bayesian and frequentist methods in random‐effects network meta‐analysis of binary data. (20th February 2020)

• Record Type: Journal Article
• Authors: Seide, Svenja E.; Jensen, Katrin; Kieser, Meinhard
• Is Part Of: Research synthesis methods. Volume 11:Number 3(2020)
• Page Start: 363
• Page End: 378
• Publication Date: 2020-02-20
• Subjects: Bayesian and frequentist methods -- binary data -- multi‐arm trials -- network meta‐analysis -- random‐effects model -- simulation study; Research -- Methodology -- Periodicals; Research -- Statistical methods -- Periodicals; 507.2
• DOI: 10.1002/jrsm.1397
• Languages: English
• ISSNs: 1759-2879

Abstract

Abstract : The performance of statistical methods is often evaluated by means of simulation studies. In case of network meta‐analysis of binary data, however, simulations are not currently available for many practically relevant settings. We perform a simulation study for sparse networks of trials under between‐trial heterogeneity and including multi‐arm trials. Results of the evaluation of two popular frequentist methods and a Bayesian approach using two different prior specifications are presented. Methods are evaluated using coverage, width of intervals, bias, and root mean squared error (RMSE). In addition, deviations from the theoretical surface under the cumulative rankings (SUCRAs) or P‐scores of the treatments are evaluated. Under low heterogeneity and when a large number of trials informs the contrasts, all methods perform well with respect to the evaluated performance measures. Coverage is observed to be generally higher for the Bayesian than the frequentist methods. The width of credible intervals is larger than those of confidence intervals and is increasing when using a flatter prior for between‐trial heterogeneity. Bias was generally small, but increased with heterogeneity, especially in netmeta. In some scenarios, the direction of bias differed between frequentist and Bayesian methods. The RMSE was comparable between methods but larger in indirectly than in directly estimated treatment effects. The deviation of the SUCRAs or P‐scores from their theoretical values was mostly comparable over the methods but differed depending on the heterogeneity and the geometry of the investigated network. Multivariate meta‐regression or Bayesian estimation using a half‐normal prior scaled to 0.5 seems to be promising with respect to the evaluated performance measures in network meta‐analysis of sparse networks.