Abstract
Network meta-analysis (NMA) compares several interventions that are linked in a network of comparative studies and estimates the relative treatment effects between all treatments, using both direct and indirect evidence. NMA is increasingly used for decision making in health care, however, a user-friendly system to evaluate the confidence that can be placed in the results of NMA is currently lacking. This paper is a tutorial describing the Confidence In Network Meta-Analysis (CINeMA) web application, which is based on the framework developed by Salanti et al (2014, PLOS One, 9, e99682) and refined by Nikolakopoulou et al (2019, bioRxiv). Six domains that affect the level of confidence in the NMA results are considered: (a) within-study bias, (b) reporting bias, (c) indirectness, (d) imprecision, (e) heterogeneity, and (f) incoherence. CINeMA is freely available and open-source and no login is required. In the configuration step users upload their data, produce network plots and define the analysis and effect measure. The dataset should include assessments of study-level risk of bias and judgments on indirectness. CINeMA calls the netmeta routine in R to estimate relative effects and heterogeneity. Users are then guided through a systematic evaluation of the six domains. In this way reviewers assess the level of concerns for each relative treatment effect from NMA as giving rise to "no concerns," "some concerns," or "major concerns" in each of the six domains, which are graphically summarized on the report page for all effect estimates. Finally, judgments across the domains are summarized into a single confidence rating ("high," "moderate," "low," or "very low"). In conclusion, the user-friendly web-based CINeMA platform provides a transparent framework to evaluate evidence from systematic reviews with multiple interventions.
Highlights
We describe the functionality of Confidence In Network Meta‐Analysis (CINeMA) and illustrate its use with the example of a Network meta‐analysis (NMA) that compared the incidence of diabetes in patients taking antihypertensive drugs or placebo
Users need to select the types of intervention and outcome and press “View.” Boxes for each relative treatment effect are updated to include between‐study heterogeneity measures based on direct comparisons (I2 and τ2) and reference values for τ2
The comparison of beta blockers with placebo is judged as case 3 Figure 4a
Summary
Binary outcome data on long format should be imported as, providing at least six columns for the study id and the treatment name, the events, the sample size, RoB and indirectness per study arm. Once the procedure is done (or directly after uploading the data, if variable names are exactly as in Tables 1 or 2), information on the file format (long, wide), outcome type (binary, continuous), number of studies, number of interventions, and number of comparisons with direct evidence appears.
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