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Updated: Aug 27, 2025

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Concordance Measures and Time-Dependent ROC Methods.

Norberto Pantoja-Galicia1, Olivia I Okereke2,3, Deborah Blacker2,3

  • 1U.S. Food and Drug Administration, Center for Devices and Radiological Health, Silver Spring, MD 20993.

Biostatistics & Epidemiology
|October 3, 2022
PubMed
Summary
This summary is machine-generated.

This study explores time-dependent receiver operating characteristic (ROC) methods and the area under the ROC curve (AUC) for assessing prognostic biomarkers. New global concordance measures are introduced to evaluate discrimination ability for clinical decisions.

Keywords:
Alzheimer’s diseaseTime-dependent sensitivity and specificitycensored survival timesdiagnostic testinverse probability weighting

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Clinical Epidemiology

Background:

  • Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) are standard metrics for evaluating diagnostic tests.
  • Time-dependent ROC methods are crucial for analyzing survival data with time-to-event outcomes.
  • Existing global scalar measures may not fully capture the nuances of time-dependent discrimination.

Purpose of the Study:

  • To review definitions of time-dependent sensitivity and specificity.
  • To explore global scalar measures, such as concordance probability, for summarizing time-dependent AUC.
  • To establish new connections between concordance measures and time-dependent AUC.

Main Methods:

  • Review of time-dependent ROC methodology.
  • Exploration of concordance probability as a global measure.
  • Development and analysis of new global concordance measures.
  • Investigation of relationships between concordance measures and time-dependent AUC.

Main Results:

  • Definitions of time-dependent sensitivity and specificity were reviewed.
  • New global concordance measures were established.
  • Connections between these new measures and their corresponding time-dependent AUC were explored.
  • The utility of concordance probability for assessing prognostic biomarkers was highlighted.

Conclusions:

  • Concordance probability and related measures offer valuable global assessments of discrimination for time-dependent events.
  • These methods can support precision medicine by evaluating prognostic biomarkers for overall survival.
  • The findings have implications for clinical decision-making, particularly in complex diseases like Alzheimer's.