Speaker
Description
Accurate diagnosis often requires the integration of multiple biomarkers rather than relying on a single test. However, existing tools for combining diagnostic tests are limited in methodological diversity and usability, especially for clinicians without programming expertise. To address this gap, we present dtComb-Shiny, a user-friendly web-based interface built on the dtComb R package. The dtComb framework integrates a comprehensive set of 142 combination methods, including linear, non-linear, machine learning, and mathematical operator-based approaches. The Shiny application extends this functionality by providing an intuitive, interactive environment where users can upload datasets, select markers, apply combination methods, and evaluate diagnostic performance without requiring coding skills. The application is developed in R using a modular architecture, where shiny handles the interactive interface, dplyr and stringr support data processing, and ggplot2 enables flexible visualization. User interface components are enhanced with shinyBS, and the system leverages reactive programming principles for efficient and scalable analysis workflows. Automated testing is performed using the shinytest framework to ensure reliability and reproducibility. The platform supports full analytical workflows, including preprocessing, model training with resampling strategies, ROC analysis, optimal cut-off selection, and detailed performance metrics such as sensitivity, specificity, PPV, and NPV. Additionally, it enables visualization through ROC curves, density plots, and sensitivity-specificity graphs, and allows prediction on new data using trained models.By bridging advanced statistical methodology with accessibility, dtComb-Shiny facilitates the adoption of sophisticated diagnostic test combination techniques in clinical and research settings. This tool empowers clinicians and researchers to improve diagnostic accuracy and make data-driven decisions more efficiently.
If you used AI tools or services to support the preparation of this submission, please state the name and reason for using each of them.
ChatGPT was used to assist with English grammar and language refinement in this submission. It was not used in the development, implementation, or analysis of the web application.
Additional Material or Paper
The dtComb package has been published in The R Journal (Doi: 10.32614/RJ-2025-036). The initial version of this work was previously presented at the 12th International Conference of the International Biometric Society Eastern Mediterranean Region (EMR 2023). The current submission focuses on the Shiny-based web application developed for dtComb.
| Keywords: Please list up to 5 keywords to help us find the right session for your contribution. | Diagnostic test combination, ROC analysis, Shiny web application, machine learning, dtComb |
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| Virtual Option | This submission is for onsite presentation only |
| Video Recording | Please don't share recordings of my talk |
| The author(s) agree(s) to take responsibility and be accountable for the contents of the submission and is/are authorized to present it. | Confirm |