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

TLDR

Evaluating the credibility of meta‑analysis results is increasingly essential for evidence synthesis. This study introduces CINeMA, a framework for assessing confidence in network meta‑analysis outcomes across multiple interventions. CINeMA evaluates six domains—within‑study bias, reporting bias, indirectness, imprecision, heterogeneity, and incoherence—using a percentage‑contribution matrix that quantifies each study’s influence and is available via a free web application. Three illustrative examples demonstrate that CINeMA enhances transparency, reduces selective evidence use, and remains easy to apply in large, complex networks.

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.