Methods used to conduct and report Bayesian mixed treatment comparisons published in the medical literature: a systematic review. Issue 7 (21st July 2013)

• Record Type: Journal Article
• Authors: Sobieraj, Diana M; Cappelleri, Joseph C; Baker, William L; Phung, Olivia J; White, C Michael; Coleman, Craig I
• Is Part Of: BMJ open. Volume 3:Issue 7(2013)
• Publication Date: 2013-07-21
• Subjects: Statistics & Research Methods -- mixed treatment comparison -- network meta-analysis; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2013-003111
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: To identify published closed-loop Bayesian mixed treatment comparisons (MTCs) and to summarise characteristics regarding their conduct and reporting. Design: Systematic review. Methods: We searched multiple bibliographic databases (January 2006–31 July 2011) for full-text, English language publications of Bayesian MTCs comparing the effectiveness or safety of ≥3 interventions based on randomised controlled trials and having at least one closed loop. Methodological and reporting characteristics of MTCs were extracted in duplicate and summarised descriptively. Results: We identified 34 Bayesian MTCs spanning 13 clinical areas. Publication of MTCs increased over the 5-year period; with 76.5% published during or after 2009. MTCs included a mean (±SD) of 35.9±30.1 trials (n=33 459±71 233 participants) and 8.5±4.3 interventions (85.7% pharmacological). Non-informative and informative prior distributions were reported to be used in 44.1% and 8.8% of MTCs, respectively, with the remainder failing to specify the prior used. A random-effects model was used to analyse the networks of trials in 58.5% of MTCs, all using WinBUGS; however, code was infrequently provided (20.6%). More than two-thirds of MTCs (76.5%) also conducted traditional meta-analysis. Methods used to evaluate convergence, heterogeneity and inconsistency were infrequently reported, but from those providing detail, methods appeared varied. MTCs most often used a binary effect measure (85.3%) and ranking of interventions based on probability was common (61.8%), although rarely displayed in a figure (8.8% of MTCs). MTCs were published in 24 different journals with a mean impact factor of 9.20±8.71. While 70.8% of journals imposed limits on word counts and 45.8% limits on the number of tables/figures, online supplements/appendices were allowed in 79.2% of journals. Publication of closed-loop Bayesian MTCs is increasing in frequency, but details regarding their methodology are often poorly described. Efforts in clarifying the appropriate methods and reporting of Bayesian MTCs should be of priority.