{"id":30877554,"url":"https://github.com/nassimayad87/evaltest","last_synced_at":"2025-09-08T04:46:47.322Z","repository":{"id":312149400,"uuid":"1046495886","full_name":"NassimAyad87/EvalTest","owner":"NassimAyad87","description":"A Shiny Application to Evaluate Diagnostic Tests Performance. It allows users to compute key performance indicators and visualize ROC curves, determine optimal cut-off thresholds, display confusion matrix, and export publication-ready plots. 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It aims to facilitate the application of statistical methods in diagnostic test evaluation by healthcare professionals.\r\n\r\n## Description\r\n\r\nThe 'EvalTest' package provides a 'Shiny' application for evaluating diagnostic test performance using data from laboratory or diagnostic research. It supports both binary and continuous test variables. It allows users to compute and visualize:\r\n\r\n-   **Confusion matrix**: for binary test results (or threshold categorized quantitative test results) and disease status.\r\n\r\n-   **Key performance indicators**: sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), likelihood ratios (LR+ and LR-), accuracy, and Youden index.\r\n\r\n-   **Receiver Operating Characteristic (ROC) curve**: determine optimal cut-off thresholds of quantitative tests, display ROC plot and area under curve (AUC) with confidence intervals.\r\n\r\n-   **Outputs and plot**: The application provides interactive tables and plots that can be exported for reporting purposes.\r\n\r\n## Installation\r\n\r\nYou can install the development version of 'EvalTest' from GitHub like so:\r\n\r\n``` r\r\ndevtools::install_github(\"NassimAyad87/EvalTest\", dependencies = TRUE)\r\n```\r\nOr from CRAN (after the package is published there):\r\n\r\n``` r\r\ninstall.packages(EvalTest)\r\n```\r\n\r\n## Using the Application\r\n\r\nLaunch the Shiny application using:\r\n\r\n``` r\r\nlibrary(EvalTest)\r\nrun_app()\r\n```\r\n\r\nThis will open the application in your default web browser (or RStudio viewer pane) and follow these steps: \r\n\r\n-   Before uploading your data, you should ensure that: the test variable is in one column (either qualitative or quantitative) and the reference variable (disease status) is in another column (binary: 1/0). There are no missing values in the selected columns.\r\n\r\n-   Upload your data in Excel format (.xlsx). \r\n\r\n-   Choose your variable test type (Qualitative or Quantitative). \r\n\r\n-   Select the appropriate columns for test variable and reference variable (disease status). \r\n\r\n-   Input disease prevalence value (between 0 and 1) of the study population.\r\n\r\n-   Run the analysis and explore the results in the different tabs.\r\n\r\n-   You can download the ROC plot and the results table for your report.\r\n\r\n## License\r\n\r\nThis package is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\r\n\r\n## Citation\r\n\r\nTo cite the 'EvalTest' package in publications, use:\r\n\r\n``` r\r\ncitation(\"EvalTest\")\r\n```\r\n\r\n\r\n\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnassimayad87%2Fevaltest","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnassimayad87%2Fevaltest","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnassimayad87%2Fevaltest/lists"}