Bayesian network meta-analysis for cluster randomized trials with binary outcomes

• Verfasst von: Uhlmann, Lorenz [VerfasserIn]
• Verfasst von: Jensen, Katrin [VerfasserIn]
• Verfasst von: Kieser, Meinhard [VerfasserIn]
• Titel: Bayesian network meta-analysis for cluster randomized trials with binary outcomes
• Verf.angabe: Lorenz Uhlmann, Katrin Jensen and Meinhard Kieser
• Jahr: 2017
• Umfang: 15 S.
• Fussnoten: Article was first published on 07 July 2016 ; Gesehen am 27.09.2018
• Titel Quelle: Enthalten in: Research synthesis methods
• Ort Quelle: Hoboken, NJ [u.a.] : Wiley, 2010
• Jahr Quelle: 2017
• Band/Heft Quelle: 8(2017), 2, Seite 236-250
• ISSN Quelle: 1759-2887
• DOI: doi:10.1002/jrsm.1210
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: network meta-analysis
• Sach-SW: binary outcome
• Sach-SW: cluster randomized trials
• Sach-SW: variance inflation
• K10plus-PPN: 1581366469

Abstract

Network meta-analysis is becoming a common approach to combine direct and indirect comparisons of several treatment arms. In recent research, there have been various developments and extensions of the standard methodology. Simultaneously, cluster randomized trials are experiencing an increased popularity, especially in the field of health services research, where, for example, medical practices are the units of randomization but the outcome is measured at the patient level. Combination of the results of cluster randomized trials is challenging. In this tutorial, we examine and compare different approaches for the incorporation of cluster randomized trials in a (network) meta-analysis. Furthermore, we provide practical insight on the implementation of the models. In simulation studies, it is shown that some of the examined approaches lead to unsatisfying results. However, there are alternatives which are suitable to combine cluster randomized trials in a network meta-analysis as they are unbiased and reach accurate coverage rates. In conclusion, the methodology can be extended in such a way that an adequate inclusion of the results obtained in cluster randomized trials becomes feasible.