Presentation (CTI)

OBJECTIVES: Indirect Treatment Comparisons (ITC) are essential for Health Technology Assessment (HTA) bodies to evaluate relative treatment effects in absence of head-to-head comparisons between treatments. Numerous software and open-source R packages are available for conducting ITC in HTA submissions, including population adjusted ITC methods. This project aims to develop an "R-shiny" application interface to standardize ITC process, providing a comprehensive solution for both conventional and advanced ITC methodologies.
METHODS: The user-friendly web application, developed with the R Shiny framework, is deployed using Docker containers on Amazon Web Services (AWS) to ensure accessibility and scalability. User security is maintained with SSL certificates and Auth0 authentication. The application accepts the aggregate data from the published evidence and individual patient level data from clinical trials to perform various analysis. The tool assesses heterogeneity and performs feasibility checks to ensure the appropriateness of selected models based on the available aggregate data. Users can select the type of analysis to perform via dropdown menus including frequentist and Bayesian network meta-analysis (NMA), network meta regression, matching adjusted indirect comparison (MAIC), simulated treatment comparison (STC) and multi-level network meta regression (ML-NMR).
RESULTS: The R-Shiny tool offers an interactive dashboard to display ITC results, including treatment effects, confidence intervals, heterogeneity assessments, and diagnostics. It summarizes ITC findings with SUCRA values, treatment rankings, forest plots, and league tables. Feasibility and heterogeneity are assessed using boxplots, I² statistics, and diagnostic plots, while model selection is supported by Deviance Information Criterion (DIC). Additionally, the application generates estimates and CODA values in Excel format for economic models and produces dynamically updated Word reports for documentation.
CONCLUSIONS: The "R-Shiny" interface for ITC enhances accessibility, ensures data security, and provides interactive dashboards with real-time reports, standardizing workflows for researchers and decision-makers. Integration with a generative AI-powered QA chatbot can further deepen insights and improve data interpretation.