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Receiver Operating Characteristic Plot01:15

Receiver 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...
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Prediction-based structured variable selection through the receiver operating characteristic curves.

Yuanjia Wang1, Huaihou Chen, Runze Li

  • 1Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York 10032, USA. yuanjia.wang@columbia.edu

Biometrics
|December 24, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces novel methods for selecting variables in medical screening tests with hierarchical structures. These techniques optimize predictive accuracy, ensuring reliable early disease detection and improving patient referral for specialized care.

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Last Updated: Jun 5, 2026

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Published on: October 11, 2018

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05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Biostatistics
  • Medical Informatics
  • Clinical Diagnostics

Background:

  • Assessing screening test accuracy is crucial for early disease detection in clinical settings.
  • Variable selection is key in designing effective medical screening tools, especially those with hierarchical question structures.
  • Existing methods may not adequately handle the dependencies in hierarchical screening data, where stem questions depend on root questions.

Purpose of the Study:

  • To develop and evaluate methods for variable selection in diagnostic tests with structured, hierarchical variables.
  • To maximize the predictive performance of a combined diagnostic test, focusing on the area under the receiver operating characteristic curve (AUC of ROC).
  • To address the challenge of optimizing empirical AUC in complex, non-convex problems by leveraging Support Vector Machines (SVM).

Main Methods:

  • Proposed methods for variable selection in structured data, forming a combined test via linear combination of variables.
  • Maximized the AUC of ROC subject to a penalty function for overfitting control, reframing it as a penalized SVM problem.
  • Introduced a penalized logistic regression approach for structured variables and compared it with ROC-based methods.

Main Results:

  • Simulation studies using real data demonstrated the performance of the proposed variable selection methods.
  • The penalized SVM approach effectively imposed the hierarchical structure on the variables.
  • The developed methods were successfully applied to design a structured screener for identifying potentially psychotic patients in primary care.

Conclusions:

  • The proposed methods offer a robust approach to variable selection for diagnostic tests with hierarchical structures.
  • These techniques enhance predictive accuracy, crucial for reliable early disease detection and appropriate patient management.
  • The study successfully designed a practical tool for primary care, aiding in the referral of patients requiring specialized psychiatric evaluation.