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<!DOCTYPE html>
<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
<meta charset="utf-8">
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<title>readme</title>
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<section id="multi-metric-comparison-of-machine-learning-imputation-methods-with-application-to-breast-cancer-survival" class="level1">
<h1>Multi-Metric Comparison of Machine Learning Imputation Methods with Application to Breast Cancer Survival</h1>
<section id="about" class="level2">
<h2 class="anchored" data-anchor-id="about">About</h2>
<p>This repository contains all the necessary code and resources to reproduce the results of our study on machine learning imputation methods, using a breast cancer survival dataset as a working example. We evaluated several single and multiple imputation methods to handle missing data, crucial in clinical prognostic studies. Our comprehensive assessment covered various performance metrics each with different perspective, including Gower’s distance, estimation bias, and predictive accuracy, among others.</p>
</section>
<section id="simulation-experiment" class="level2">
<h2 class="anchored" data-anchor-id="simulation-experiment">Simulation Experiment</h2>
<section id="dataset" class="level3">
<h3 class="anchored" data-anchor-id="dataset">Dataset</h3>
<ul>
<li><strong>Simulated Dataset</strong>: Created with 30% Missing At Random (MAR) values to mimic real-world scenarios of missing data in clinical studies.</li>
</ul>
</section>
<section id="imputation-methods-evaluated" class="level3">
<h3 class="anchored" data-anchor-id="imputation-methods-evaluated">Imputation Methods Evaluated</h3>
<ul>
<li><p><strong>Single Imputation (SI) Methods</strong>: KNN, missMDA, CART, missForest, missRanger, missCforest.</p></li>
<li><p><strong>Multiple Imputation (MI) Methods</strong>: miceCART, miceRF.</p></li>
</ul>
</section>
<section id="performance-metrics" class="level3">
<h3 class="anchored" data-anchor-id="performance-metrics">Performance Metrics</h3>
<ul>
<li>Gower’s distance, estimation bias, empirical standard error, coverage rate, length of confidence interval, predictive accuracy, proportion of falsely classified (PFC), normalized root mean squared error (NRMSE), AUC, and C-index scores.</li>
</ul>
</section>
</section>
<section id="repository-structure" class="level2">
<h2 class="anchored" data-anchor-id="repository-structure">Repository Structure</h2>
<pre><code>.
└── R/
├── utils.R # Utility functions for data generation, imputation, and performance evaluation
└── simulation_script.R # Main script to run the simulations and imputation experiments</code></pre>
</section>
<section id="citation" class="level2">
<h2 class="anchored" data-anchor-id="citation">Citation</h2>
<p>If you use the code in this repository in your research, please cite the repository as the manuscript is currently <strong>under consideration</strong>.</p>
</section>
</section>
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