PerfMeasuresOverview

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PerfMeasuresOverview

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R code and predictions for the case study from Van Calster et al (Validation Studies of Predictive AI for Use in Medical Practice: Overview and Guidance for Performance Measures)

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# Performance Measures Overview

**Code and data for the validation of Predictive AI in Medical Practice.**

This repository contains the source code (R and Python) and dataset used in the case study for the paper **"Validation studies of predictive AI for use in medical practice: overview and guidance for performance measures"**, published in *The Lancet Digital Health*.

🔗 **[Read the Full Paper](https://doi.org/10.1016/j.landig.2025.100916)**

## 📄 About the Project

This work was developed under the guidance of the **STRATOS (STRengthening Analytical Thinking for Observational Studies) consortium**.

The paper reviews **32 performance measures** for clinical risk prediction models (broadly referred to as Predictive AI) intended for use in medical practice. It provides critical guidance and recommendations on selecting the most appropriate measures for validation studies.

### The Case Study
The measures discussed in the paper are illustrated using a real-world case study:
* **Context:** External validation of a model to estimate the risk of malignancy in patients with an ovarian tumor selected for surgery.
* **Source:** *Landolfo et al., Br J Cancer 2024*.

## ⚠️ Disclaimer regarding Code Implementation

Please note that the code for this study was **originally produced in R** (`PerfMeasuresOverview.R`). The Python implementations (`PerfMeasuresPython.py` and `PerfMeasures.ipynb`) are replications of the original R code provided for accessibility and convenience.

## 📂 Repository Contents

This repository provides the code to calculate the performance measures and generate the plots shown in the paper. It also includes the anonymized prediction data.

## 📊 Data Description

The file `data_case_study.txt` contains the specific data used for the case study. To ensure patient privacy and anonymity, **no clinical variables or personal identifiers are included**.

The dataset consists of:
* **Risk Estimates:** The predicted probability of the outcome.
* **Outcomes:** Binary classification (1 = malignant, 0 = benign).

## 🚀 Usage

You can reproduce the analysis using either R or Python.

### R Users
Run the `PerfMeasuresOverview.R` script in RStudio or your preferred R environment. Ensure the `data_case_study.txt` file is in your working directory.

### Python Users
You can run the analysis using the provided script or notebook:
* **Script:** Run `PerfMeasuresPython.py`.
* **Notebook:** Open `PerfMeasures.ipynb` in Jupyter Notebook or JupyterLab.

## 📚 References

If you use this code or methodology in your research, please cite the main paper:

> Van Calster B, et al. **Validation studies of predictive AI for use in medical practice: overview and guidance for performance measures.** *The Lancet Digital Health*. 2025.
> [https://doi.org/10.1016/S2589-7500(25)00098-6](https://doi.org/10.1016/j.landig.2025.100916)

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