https://github.com/nassimayad87/evaltest
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. It supports both binary and continuous test variables.
https://github.com/nassimayad87/evaltest
diagnostic-tests performance-test r receiver-operating-characteristic roc sensitivity shiny-apps specificity youden
Last synced: 11 months ago
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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. It supports both binary and continuous test variables.
- Host: GitHub
- URL: https://github.com/nassimayad87/evaltest
- Owner: NassimAyad87
- License: other
- Created: 2025-08-28T19:15:42.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-08-28T20:16:50.000Z (11 months ago)
- Last Synced: 2025-08-29T01:56:45.607Z (11 months ago)
- Topics: diagnostic-tests, performance-test, r, receiver-operating-characteristic, roc, sensitivity, shiny-apps, specificity, youden
- Language: R
- Homepage:
- Size: 362 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# EvalTest
[](https://doi.org/10.5281/zenodo.16989620)
## Overview
**EvalTest** is an R Shiny application designed for evaluating diagnostic test performance. It aims to facilitate the application of statistical methods in diagnostic test evaluation by healthcare professionals.
## Description
The '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:
- **Confusion matrix**: for binary test results (or threshold categorized quantitative test results) and disease status.
- **Key performance indicators**: sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), likelihood ratios (LR+ and LR-), accuracy, and Youden index.
- **Receiver Operating Characteristic (ROC) curve**: determine optimal cut-off thresholds of quantitative tests, display ROC plot and area under curve (AUC) with confidence intervals.
- **Outputs and plot**: The application provides interactive tables and plots that can be exported for reporting purposes.
## Installation
You can install the development version of 'EvalTest' from GitHub like so:
``` r
devtools::install_github("NassimAyad87/EvalTest", dependencies = TRUE)
```
Or from CRAN (after the package is published there):
``` r
install.packages(EvalTest)
```
## Using the Application
Launch the Shiny application using:
``` r
library(EvalTest)
run_app()
```
This will open the application in your default web browser (or RStudio viewer pane) and follow these steps:
- 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.
- Upload your data in Excel format (.xlsx).
- Choose your variable test type (Qualitative or Quantitative).
- Select the appropriate columns for test variable and reference variable (disease status).
- Input disease prevalence value (between 0 and 1) of the study population.
- Run the analysis and explore the results in the different tabs.
- You can download the ROC plot and the results table for your report.
## License
This package is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Citation
To cite the 'EvalTest' package in publications, use:
``` r
citation("EvalTest")
```