CINeMA in 60 seconds: confidence rating for NMA. #Shorts

GRADE was built for pairwise meta-analysis. CINeMA (Confidence In Network Meta-Analysis, Nikolakopoulou Higgins Salanti 2020 PLOS Medicine) extends it for networks. Each network estimate - direct, indirect, and mixed - gets its own confidence rating across six domains.

The six CINeMA domains:
- Within-study bias (the RoB of contributing trials)
- Reporting bias (publication and selective outcome reporting)
- Indirectness (PICO mismatch between trial population and target)
- Imprecision (CI width vs decision threshold)
- Heterogeneity (between-study variance for that comparison)
- Incoherence (direct-vs-indirect agreement, AKA inconsistency)

What you get:
- Contribution matrix showing which trials drive each network comparison
- Per-comparison confidence rating (Very low, Low, Moderate, High)
- Network-level summary of where confidence is weakest
- Summary-of-Findings table formatted for paper appendix
- Diagnostic plots: traffic-light per domain x comparison

Workflow position: use AFTER NMA convergence and consistency tests, BEFORE rankograms and SUCRA. If incoherence is high in your network, do not report SUCRA - report direct comparisons only.

Why this matters: PRISMA-NMA 2015 extension explicitly recommends CINeMA-style confidence rating for any NMA submitted to a Cochrane-aligned journal. Most NMA papers skip it. Doing it raises your paper above 90 percent of competition.

Used in Synthesis course Module 11: NMA confidence and reporting.

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Live: https://mahmood726-cyber.github.io/allmeta/cinema/index.html
Catalogue: https://mahmood726-cyber.github.io/allmeta/
Synthesis Courses (free, 26 modules × 12 languages): https://mahmood726-cyber.github.io/synthesis-courses/
Evidence Reversal series: at meta-analysishtml

#MetaAnalysis #NMA #CINeMA #GRADE #PRISMA #Cochrane #EBM #Shorts #YTShorts

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