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

• Published in: PLoS medicine 2020-04, Vol.17 (4), p.e1003082-e1003082
• Main Authors: Nikolakopoulou, Adriani, Higgins, Julian P T, Papakonstantinou, Theodoros, Chaimani, Anna, Del Giovane, Cinzia, Egger, Matthias, Salanti, Georgia
• Format: Article
• Language: English
• Subjects: Acute coronary syndromes; Bias; Cardiovascular disease; Computer and Information Sciences; Confidence Intervals; Coronary Artery Disease - diagnostic imaging; Coronary Artery Disease - epidemiology; Coronary vessels; Credibility; Disease prevention; Electrocardiography - methods; Electrocardiography - standards; Exercise Test - methods; Exercise Test - standards; Guidelines and Guidance; Humans; Life Sciences; Magnetic Resonance Imaging, Cine - methods; Magnetic Resonance Imaging, Cine - standards; Medical imaging; Medicine and Health Sciences; Meta-analysis; Network Meta-Analysis; Physical Sciences; Preventive medicine; Randomized Controlled Trials as Topic - methods; Randomized Controlled Trials as Topic - standards; Research and Analysis Methods; Santé publique et épidémiologie; Software
• ISSN: 1549-1676; 1549-1277; 1549-1676
• 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.