Related Experiment Video
Updated: Feb 18, 2026

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
bcROCsurface: an R package for correcting verification bias in estimation of the ROC surface and its volume for
1Department of Statistical Sciences, University of Padova, via C. Battisti, 241, Padova, 35121, Italy. toduc@stat.unipd.it.
Background:
Receiver operating characteristic (ROC) surface analysis is usually employed to assess the accuracy of a medical diagnostic test when there are three ordered disease status (e.g. non-diseased, intermediate, diseased). In practice, verification bias can occur due to missingness of the true disease status and can lead to a distorted conclusion on diagnostic accuracy. In such situations, bias-corrected inference tools are required.
Results:
This paper introduce an R package, named bcROCsurface, which provides utility functions for verification bias-corrected ROC surface analysis. The shiny web application of the correction for verification bias in estimation of the ROC surface analysis is also developed.
Conclusion:
bcROCsurface may become an important tool for the statistical evaluation of three-class diagnostic markers in presence of verification bias. The R package, readme and example data are available on CRAN. The web interface enables users less familiar with R to evaluate the accuracy of diagnostic tests, and can be found at http://khanhtoduc.shinyapps.io/bcROCsurface_shiny/ .
More Related Videos
09:00Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
Published on: August 16, 2024
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
Related Concept Videos
Receiver Operating Characteristic Plot
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Statistical Software for Data Analysis and Clinical Trials
Response Surface Methodology
The process of RSM involves several key steps:
Accuracy and Errors in Hypothesis Testing
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
Quantifying and Rejecting Outliers: The Grubbs Test