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

Autoři: Adriani Nikolakopoulou aff001;  Julian P. T. Higgins aff002;  Theodoros Papakonstantinou aff001;  Anna Chaimani aff003;  Cinzia Del Giovane aff005;  Matthias Egger aff001;  Georgia Salanti aff001
Působiště autorů: Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland aff001;  Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom aff002;  Université de Paris, Research Center of Epidemiology and Statistics Sorbonne Paris Cité (CRESS UMR1153), INSERM, INRA, Paris, France aff003;  Cochrane France, Paris, France aff004;  Institute of Primary Health Care (BIHAM), University of Bern, Bern, Switzerland aff005
Vyšlo v časopise: CINeMA: An approach for assessing confidence in the results of a network meta-analysis. PLoS Med 17(4): e32767. doi:10.1371/journal.pmed.1003082
Kategorie: Guidelines and Guidance
doi: 10.1371/journal.pmed.1003082



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.


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.


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.

Klíčová slova:

Adverse events – Coronary heart disease – Diagnostic medicine – Electrocardiography – Magnetic resonance imaging – Metaanalysis – Network analysis – Statins


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