NetMetaEasy: enabling rapid and user-friendly network meta-analysis (NMA) for comparative effectiveness research

Network meta-analysis (NMA) enables the simultaneous comparison of multiple treatments by combining direct and indirect evidence across a network of studies. While its application is rapidly expanding in pharmacological research and clinical guideline development, performing NMA typically requires advanced statistical knowledge and access to specialized software, limiting its broader adoption. Here, we present NetMetaEasy, a user-friendly, web-based platform that allows rapid execution and visualization of network meta-analyses from standard input formats, with minimal technical expertise required.

NetMetaEasy is an R/Shiny-based platform supporting binary, continuous and summary-effect data (e.g. odds ratios, hazard ratios). It offers frequentist and Bayesian network meta-analysis with P-score and SUCRA-based treatment ranking. It generates standard outputs including network diagrams, forest plots, netleague tables, inconsistency assessments and funnel plots using fixed- and random-effects models implemented via the netmeta and gemtc R packages.

We demonstrate the functionality of NetMetaEasy using a real-world pharmacological dataset evaluating the cardiovascular outcomes of sodium/glucose cotransporter 2 (SGLT2) inhibitors. The platform successfully generated all standard NMA outputs, identified treatments with statistically significant benefits over placebo and showed no evidence of small-study bias. The entire workflow, from data upload to interpretation-ready plots,was completed within minutes. The registration-free NetMetaEasy analysis platform is accessible at http://www.metaanalysisonline.com/netmetaeasy.

NetMetaEasy provides an accessible solution for conducting network meta-analyses by streamlining data processing, analysis and visualization into a single online interface, thereby enabling more widespread and rapid evidence synthesis in pharmacology, particularly for researchers without formal statistical training.