MetaAnalysisOnline.com: Web-Based Tool for the Rapid Meta-Analysis of Clinical and Epidemiological Studies

A meta-analysis is a quantitative, formal study design in epidemiology and clinical medicine that systematically integrates and quantitatively synthesizes findings from multiple independent studies. This approach not only enhances statistical power but also enables the exploration of effects across diverse populations and helps resolve controversies arising from conflicting studies.

This study aims to develop and implement a user-friendly tool for conducting meta-analyses, addressing the need for an accessible platform that simplifies the complex statistical procedures required for evidence synthesis while maintaining methodological rigor.

The platform available at MetaAnalysisOnline.com enables comprehensive meta-analyses through an intuitive web interface, requiring no programming expertise or command-line operations. The system accommodates diverse data types including binary (total and event numbers), continuous (mean and SD), and time-to-event data (hazard rates with CIs), while implementing both fixed-effect and random-effect models using established statistical approaches such as DerSimonian-Laird, Mantel-Haenszel, and inverse variance methods for effect size estimation and heterogeneity assessment.

In addition to statistical tests, graphical representations including the forest plot, the funnel plot, and the z score plot can be drawn. A forest plot is highly effective in illustrating heterogeneity and pooled results. The risk of publication bias can be revealed by a funnel plot. A z score plot provides a visual assessment of whether more research is needed to establish a reliable conclusion. All the discussed models and visualization options are integrated into the registration-free web-based portal. Leveraging MetaAnalysisOnline.com's capabilities, we examined treatment-related adverse events in patients with cancer receiving perioperative anti-PD-1 immunotherapy through a systematic review encompassing 10 studies with 8099 total participants. Meta-analysis revealed that anti-PD-1 therapy doubled the risk of adverse events (risk ratio 2.15, 95% CI 1.39-3.32), with significant between-study heterogeneity (I²=95%) and publication bias detected through the Egger test (P=.02). While these findings suggest increased toxicity associated with anti-PD-1 treatment, the z score analysis indicated that additional studies are needed for definitive conclusions.

In summary, the web-based tool aims to bridge the void for clinical and life science researchers by offering a user-friendly alternative for the swift and reproducible meta-analysis of clinical and epidemiological trials.