Using the contribution matrix to evaluate complex study limitations in a network meta-analysis: a case study of bipolar maintenance pharmacotherapy review. (December 2016)

• Record Type: Journal Article
• Authors: Furukawa, Toshi; Miura, Tomofumi; Chaimani, Anna; Leucht, Stefan; Cipriani, Andrea; Noma, Hisashi; Mitsuyasu, Hiroshi; Kanba, Shegenobu; Salanti, Georgia
• Is Part Of: BMC research notes. Volume 9:Number 1(2016)
• Page Start: 1
• Page End: 13
• Publication Date: 2016-12
• Subjects: Network meta-analysis -- GRADE -- Study limitations -- Risk of bias -- Enrichment design; Medicine -- Periodicals; Biology -- Periodicals; 610.5
• DOI: 10.1186/s13104-016-2019-1
• Languages: English
• ISSNs: 1756-0500

Abstract

Abstract Background Limitations in the primary studies constitute one important factor to be considered in the grading of recommendations assessment, development, and evaluation (GRADE) system of rating quality of evidence. However, in the network meta-analysis (NMA), such evaluation poses a special challenge because each network estimate receives different amounts of contributions from various studies via direct as well as indirect routes and because some biases have directions whose repercussion in the network can be complicated. Findings In this report we use the NMA of maintenance pharmacotherapy of bipolar disorder (17 interventions, 33 studies) and demonstrate how to quantitatively evaluate the impact of study limitations usingnetweight, a STATA command for NMA. For each network estimate, the percentage of contributions from direct comparisons at high, moderate or low risk of bias were quantified, respectively. This method has proven flexible enough to accommodate complex biases with direction, such as the one due to the enrichment design seen in some trials of bipolar maintenance pharmacotherapy. Conclusions Usingnetweight, therefore, we can evaluate in a transparent and quantitative manner how study limitations of individual studies in the NMA impact on the quality of evidence of each network estimate, even when such limitations have clear directions.