Does this skill support weighted reliability measures?

Absolutely. It includes patterns for unweighted, linear, and quadratic weighted Kappa for categorical data, as well as various ICC models for continuous measurements.

Which R packages are required for these diagnostic analyses?

The skill utilizes several industry-standard packages including pROC for ROC curves, cutpointr for threshold optimization, epiR for 2x2 tables, and mada for diagnostic meta-analysis.

Can I compare the performance of multiple diagnostic markers?

Yes, the skill provides specific code snippets for plotting multiple ROC curves together and performing statistical tests like the DeLong test to compare AUC values.

How does the skill handle optimal cutpoint selection?

It provides multiple methods including maximizing Youden's Index, minimizing misclassification costs, or setting specific sensitivity/specificity constraints using the cutpointr and OptimalCutpoints packages.

R Diagnostic Accuracy Analysis

Name: R Diagnostic Accuracy Analysis
Author: choxos

bychoxos

数据科学与机器学习

Performs comprehensive diagnostic test accuracy analysis in R, including ROC curves, cutpoint optimization, and meta-analysis.

关于

This skill provides a comprehensive toolkit for clinical researchers and data scientists to evaluate diagnostic test performance using R. It implements best practices for calculating fundamental metrics like sensitivity and specificity, generating ROC curves with confidence intervals, and determining optimal thresholds using various statistical criteria. Beyond basic analysis, it includes specialized methods for Decision Curve Analysis (DCA), inter-rater reliability, and advanced diagnostic meta-analysis using bivariate and HSROC models, making it an essential resource for medical validation and biostatistical workflows.

主要功能

Comprehensive ROC curve analysis and AUC comparison using DeLong and Bootstrap methods
Clinical utility evaluation through Decision Curve Analysis (DCA) and net benefit metrics
0 GitHub stars
Inter-rater reliability measures including Cohen's/Fleiss' Kappa and Intraclass Correlation (ICC)
Automated optimal cutpoint selection using Youden’s Index or cost-benefit optimization
Meta-analysis of diagnostic studies using hierarchical bivariate and HSROC models

使用场景

Evaluating the performance of a new medical biomarker against a gold standard reference
Determining the optimal clinical threshold for a diagnostic test to minimize specific misclassification costs
Synthesizing diagnostic accuracy data from multiple studies for systematic reviews and meta-analyses

关于

主要功能

Comprehensive ROC curve analysis and AUC comparison using DeLong and Bootstrap methods
Clinical utility evaluation through Decision Curve Analysis (DCA) and net benefit metrics
0 GitHub stars
Inter-rater reliability measures including Cohen's/Fleiss' Kappa and Intraclass Correlation (ICC)
Automated optimal cutpoint selection using Youden’s Index or cost-benefit optimization
Meta-analysis of diagnostic studies using hierarchical bivariate and HSROC models

使用场景

Evaluating the performance of a new medical biomarker against a gold standard reference
Determining the optimal clinical threshold for a diagnostic test to minimize specific misclassification costs
Synthesizing diagnostic accuracy data from multiple studies for systematic reviews and meta-analyses