MASimplified: A User-Friendly Web-Based Tool for Conducting Pairwise Meta-Analysis and Visualization Using R-Shiny and Metafor R Packages

MASimplified software

Abstract

Background: A meta-analysis is a fundamental method for synthesizing evidence across studies comparing two or more
interventions, playing a pivotal role in healthcare decision-making and evidence-based research. While essential, existing
software tools often require programming expertise, they are not cost-free, limiting accessibility for many researchers and
clinicians.
Purpose: To present a MASimplified, a user-friendly web application designed for conducting a pairwise meta-analysis. The
tool enables researchers with no programming background to conduct analyses via an intuitive point-and-click interface,
generate publication-ready visualizations, and interpret results in real time. Leveraging R’s meta and metafor packages for
statistical computations and the Shiny framework for interface development, the MASimplified was built as a freely accessible
web app. The platform requires only a standard internet browser, eliminating installation barriers. Key features include
conducting a pairwise meta-analysis, subgroup analysis, automated forest plots, funnel plots, risk-of-bias assessments and
conducting a meta-regression analysis.
Conclusions: MASimplified successfully streamlines the entire pairwise meta-analysis workflow, from data input to result
interpretation. An illustrative example (detailed in the current manuscript) demonstrates its functionality, showcasing
outputs such as pooled effect estimates, heterogeneity metrics, meta-regression analysis and visualizations. The app is
publicly available at https://arminparavlic.shinyapps.io/MASimplified/. MASimplified empowers non-specialists to conduct
rigorous pairwise meta-analyses, enhancing the transparency and clinical relevance of evidence synthesis. By bridging the
gap between advanced statistical methods and user-friendly implementation, the tool strengthens capacity for informed
decision-making in research and practice. We hope this initiative inspires further development of accessible tools using
open-source technologies like Shiny, fostering broader engagement with specialized analytic methods.