CINeMA
CINeMA is a method to assess how confident we can be in the results of a network meta-analysis, which compares multiple treatments at once. It looks at six key areas like study quality, missing data, and consistency of results to help decision-makers understand the reliability of findings.
At a glance
Use when
Assessing the credibility of network meta-analysis results for decision-making in health technology assessment, guideline development, or systematic reviews involving multiple interventions.
Avoid when
When a network meta-analysis has not been conducted or when insufficient data are available to evaluate the six domains, especially contribution matrices or risk of bias.
Inputs
Network meta-analysis results, including effect estimates, standard errors, study characteristics, and the network structure; optionally, risk of bias assessments and publication bias analyses.
Outputs
Domain-specific judgments of confidence (low, moderate, high) and an overall confidence rating for each comparison in the network meta-analysis.
How it works
CINeMA (Confidence in Network Meta-Analysis) is a framework for evaluating confidence in network meta-analysis results across six domains: within-study bias, reporting bias, indirectness, imprecision, heterogeneity, and incoherence. It uses a percentage contribution matrix to trace study influence and integrates variability components relevant to clinical decision-making. The method supports transparent, systematic appraisal and is applicable to complex networks.
- HTA domains
- Clinical Effectiveness, Organisational aspects
- Assumptions
- The network meta-analysis model is correctly specified; risk of bias and other domains are assessable from available data; contribution matrices accurately reflect information flow in the network.
- Strengths
- Improves transparency in confidence assessments,Reduces subjectivity through structured domain evaluation,Applicable to large and complex networks,Integrates contribution matrices to trace evidence flow,Supports systematic and reproducible judgments
- Limitations
- Dependent on quality of input data and network meta-analysis model,Requires access to detailed study and statistical data,May be challenging to apply when data on reporting bias are sparse,Does not replace clinical expertise in interpretation
- Also known as
- Confidence in Network Meta-Analysis
Questions this answers
- › How confident can we be in the results of a network meta-analysis?
- › Which studies contribute most to the estimated treatment effects?
- › Are the comparisons in the network affected by bias or inconsistency?
- › Is there sufficient precision and homogeneity in the effect estimates?
- › How does indirect evidence affect the validity of conclusions?
- › What factors reduce confidence in the network meta-analysis results?
References & sources
Related methods
Similar by meaning
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