Related Experiment Video
Updated: Jul 5, 2026
13:44
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Published on: August 30, 2013
Evaluating the ROC performance of markers for future events
Margaret S Pepe1, Yingye Zheng, Yuying Jin
1Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N., M2-B500, Seattle, WA 98109, USA. mspepe@u.washington.edu
Lifetime Data Analysis
|December 8, 2007
Summary
Time-dependent Receiver Operating Characteristic (ROC) curves are essential for evaluating disease diagnostic biomarkers over time. This study explores various definitions and estimation methods for time-dependent ROC curves, considering censored data and competing risks.
Area of Science:
- Biostatistics
- Medical Informatics
- Epidemiology
Background:
- Receiver operating characteristic (ROC) curves are standard for diagnostic test evaluation.
- Applying ROC curves to time-to-event data requires accounting for the time lag between marker measurement and event occurrence.
Purpose of the Study:
- To discuss various definitions of time-dependent ROC curves.
- To evaluate methods for estimating time-dependent ROC curves in the presence of time-to-event outcomes.
- To compare retrospective and prospective estimation approaches.
Main Methods:
- Review and discussion of different time-dependent ROC curve definitions.
- Comparative analysis of estimation methods, including retrospective and prospective approaches.
- Consideration of extensions for censored data, competing risks, and various sampling schemes.
Main Results:
- Different definitions of time-dependent ROC curves are suitable for distinct applications.
- Retrospective and prospective methods offer varying degrees of flexibility and assumptions.
- Methods can accommodate complexities like censored data and competing risks.
Conclusions:
- Time-dependent ROC curves are crucial for accurate biomarker evaluation in time-to-event settings.
- The choice of definition and estimation method depends on the specific research question and data structure.
- The presented approaches provide a flexible framework for analyzing time-dependent diagnostic accuracy.
Related Concept Videos
01:52Accuracy and Precision
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. Highly accurate measurements...
01:52Accuracy and Precision
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. Highly accurate measurements...
01:20Region of Convergence of Laplace Tarnsform
The Region of Convergence (ROC) is a fundamental concept in signal processing and system analysis, particularly associated with the Laplace transform. The ROC represents an area in the complex plane where the Laplace transform of a given signal converges, determining the transform's applicability and utility.
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This substitution...
Consider a decaying exponential signal that begins at a specific time. When deriving its Laplace transform, the time-domain variable is replaced with a complex variable. This substitution...
01:17Region of Convergence
The z-transform is a powerful mathematical tool used in the analysis of discrete-time signals and systems. It is a crucial tool in the analysis of discrete-time systems, but its convergence is limited to specific values of the complex variable z. This range of values, known as the Region of Convergence (ROC), is fundamental in determining the behavior and stability of a system or signal. The ROC defines the region in the complex plane where the z-transform converges, which can take various...
01:13Sensitivity, Specificity, and Predicted Value
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Sensitivity is the...
01:15Receiver Operating Characteristic Plot
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...