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Related Concept Videos

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Related Experiment Video

Updated: Jun 21, 2025

An R-Based Landscape Validation of a Competing Risk Model
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Extending the DeLong algorithm for comparing areas under correlated receiver operating characteristic curves with

Lily Zou1, Yun-Hee Choi2, Leonardo Guizzetti2

  • 1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada.

Statistics in Medicine
|July 16, 2024
PubMed
Summary

The DeLong method for comparing receiver operating characteristic curves can produce invalid results by omitting data with missing values. This study introduces a novel, valid, and efficient approach to handle missing data in these comparisons.

Keywords:
Behrens‐FisherWilcoxon‐Mann‐Whitney testbiomarkersconcordancediagnostic testseffect sizemissing at randommixed modelsnonparametricranksresponsiveness

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Area of Science:

  • Biostatistics
  • Statistical Methods
  • Medical Data Analysis

Background:

  • The DeLong method (1988) is widely used for comparing areas under correlated receiver operating characteristic (ROC) curves.
  • Existing software implementations of the DeLong method may produce invalid or inefficient results by excluding individuals with any missing data.
  • Missing data is a common issue in real-world datasets, necessitating robust statistical approaches.

Purpose of the Study:

  • To address the limitations of the DeLong method concerning missing data.
  • To develop a simplified and more robust algorithm for comparing areas under correlated ROC curves.
  • To provide a valid and efficient method for handling missing data in multivariate ROC analyses.

Main Methods:

  • A simplified version of the DeLong algorithm using ranks was developed.
  • A mixed-model approach was employed to extend the algorithm for multivariate data with missing values.
  • The proposed procedure was validated through simulation studies for data missing at random.

Main Results:

  • Simulation results confirmed the validity and efficiency of the proposed procedure.
  • The new method effectively accommodates missing data, overcoming limitations of the original DeLong approach.
  • The procedure demonstrated superior performance in handling datasets with missing values compared to standard implementations.

Conclusions:

  • The proposed mixed-model approach provides a valid and efficient alternative for comparing areas under correlated ROC curves with missing data.
  • This method enhances the reliability of statistical analyses in the presence of incomplete datasets.
  • The procedure is illustrated and available in popular statistical software (SAS, Stata, R).