Background: Policy makers and guideline developers face challenges in evaluating the quality of evidence from systematic reviews with multiple interventions. We previously developed a framework to judge the confidence that can be placed in results obtained from a network meta-analysis (NMA) based on the GRADE domains: study limitations, indirectness, inconsistency, imprecision and publication bias. The framework combines judgments about direct evidence with their statistical contribution to NMA results, enabling evaluation of the credibility of NMA treatment effects and treatment rankings. However, the process is cumbersome and time-consuming for large networks.

Objectives: To present a web application, CINeMA (Confidence In Network Meta-Analysis), that considerably simplifies the evaluation of confidence in the findings from NMA.

Methods: CINeMA provides an interactive, online process to determine the degree of confidence one can place in NMA results. Users upload a dataset (in .csv format) and are guided through the steps of the evaluation process. CINeMA optionally automates several of the methodological steps involved, e.g. by providing heterogeneity and inconsistency metrics and appropriate reference values for their interpretation. Information about study-level risk-of-bias assessments can be included in the uploaded data, and CINeMA evaluates study limitations in each pairwise comparison and in each NMA effect size. Standard NMA outputs (such as the network plot and the NMA effect sizes) are also provided.

Results: Using networks of different size and complexity, we show that CINeMA can greatly simplify the evaluation of credibility of NMA results. We will illustrate the application using data from a network of antihypertensive drugs for incidence diabetes.

Conclusions: Evaluation of the quality of evidence is a particularly important but challenging part of a systematic review with multiple interventions. CINeMA, with semi-automation of methods and via a guided online process, will greatly simplify the evaluation of the quality of NMA results and will improve transparency and reproducibility.