Skip to Main content Skip to Navigation
Journal articles

CINeMA: An approach for assessing confidence in the results of a network meta-analysis

Abstract : Background: The evaluation of the credibility of results from a meta-analysis has become an important part of the evidence synthesis process. We present a methodological framework to evaluate confidence in the results from network meta-analyses, Confidence in Network Meta-Analysis (CINeMA), when multiple interventions are compared. Methodology: CINeMA considers 6 domains: (i) within-study bias, (ii) reporting bias, (iii) indirectness, (iv) imprecision, (v) heterogeneity, and (vi) incoherence. Key to judgments about within-study bias and indirectness is the percentage contribution matrix, which shows how much information each study contributes to the results from network meta-analysis. The contribution matrix can easily be computed using a freely available web application. In evaluating imprecision, heterogeneity, and incoherence, we consider the impact of these components of variability in forming clinical decisions. Conclusions: Via 3 examples, we show that CINeMA improves transparency and avoids the selective use of evidence when forming judgments, thus limiting subjectivity in the process. CINeMA is easy to apply even in large and complicated networks.
Document type :
Journal articles
Complete list of metadatas

Cited literature [43 references]  Display  Hide  Download

https://www.hal.inserm.fr/inserm-02909298
Contributor : Myriam Bodescot <>
Submitted on : Thursday, July 30, 2020 - 11:37:29 AM
Last modification on : Friday, October 23, 2020 - 4:59:13 PM

File

journal.pmed.1003082.pdf
Publication funded by an institution

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Collections

Citation

Adriani Nikolakopoulou, Julian Higgins, Theodoros Papakonstantinou, Anna Chaimani, Cinzia del Giovane, et al.. CINeMA: An approach for assessing confidence in the results of a network meta-analysis. PLoS Medicine, Public Library of Science, 2020, 17 (4), pp.e1003082. ⟨10.1371/journal.pmed.1003082⟩. ⟨inserm-02909298⟩

Share

Metrics

Record views

31

Files downloads

55