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

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 mul...

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Autores principales: Nikolakopoulou, Adriani, Higgins, Julian P. T., Papakonstantinou, Theodoros, Chaimani, Anna, Del Giovane, Cinzia, Egger, Matthias, Salanti, Georgia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122720/
https://www.ncbi.nlm.nih.gov/pubmed/32243458
http://dx.doi.org/10.1371/journal.pmed.1003082
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author Nikolakopoulou, Adriani
Higgins, Julian P. T.
Papakonstantinou, Theodoros
Chaimani, Anna
Del Giovane, Cinzia
Egger, Matthias
Salanti, Georgia
author_facet Nikolakopoulou, Adriani
Higgins, Julian P. T.
Papakonstantinou, Theodoros
Chaimani, Anna
Del Giovane, Cinzia
Egger, Matthias
Salanti, Georgia
author_sort Nikolakopoulou, Adriani
collection PubMed
description 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.
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spelling pubmed-71227202020-04-09 CINeMA: An approach for assessing confidence in the results of a network meta-analysis Nikolakopoulou, Adriani Higgins, Julian P. T. Papakonstantinou, Theodoros Chaimani, Anna Del Giovane, Cinzia Egger, Matthias Salanti, Georgia PLoS Med Guidelines and Guidance 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. Public Library of Science 2020-04-03 /pmc/articles/PMC7122720/ /pubmed/32243458 http://dx.doi.org/10.1371/journal.pmed.1003082 Text en © 2020 Nikolakopoulou et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Guidelines and Guidance
Nikolakopoulou, Adriani
Higgins, Julian P. T.
Papakonstantinou, Theodoros
Chaimani, Anna
Del Giovane, Cinzia
Egger, Matthias
Salanti, Georgia
CINeMA: An approach for assessing confidence in the results of a network meta-analysis
title CINeMA: An approach for assessing confidence in the results of a network meta-analysis
title_full CINeMA: An approach for assessing confidence in the results of a network meta-analysis
title_fullStr CINeMA: An approach for assessing confidence in the results of a network meta-analysis
title_full_unstemmed CINeMA: An approach for assessing confidence in the results of a network meta-analysis
title_short CINeMA: An approach for assessing confidence in the results of a network meta-analysis
title_sort cinema: an approach for assessing confidence in the results of a network meta-analysis
topic Guidelines and Guidance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122720/
https://www.ncbi.nlm.nih.gov/pubmed/32243458
http://dx.doi.org/10.1371/journal.pmed.1003082
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