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Single reader between-cases AUC estimator with nested data.

Hongfei Du1, Si Wen2, Yufei Guo1

  • 1Statistics Department, 8367The George Washington University, Washington, USA.

Statistical Methods in Medical Research
|July 5, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces new methods for evaluating diagnostic accuracy in complex medical cases. The research focuses on between-cases AUC to better assess patient-level performance, offering improved variance and covariance estimators for diagnostic accuracy.

Keywords:
Area under the receiver operating characteristic curvebetween-cases area under the receiver operating characteristic curvenested datareader performance evaluationwithin-cases area under the receiver operating characteristic curve

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Area of Science:

  • Medical Diagnostics
  • Biostatistics
  • Machine Learning Evaluation

Background:

  • Area under the ROC curve (AUC) is standard for diagnostic performance evaluation.
  • Evaluating diagnostic performance with nested data (multiple regions of interest per case) is challenging.
  • Existing methods struggle with location-level vs. patient-level performance assessment in nested data.

Purpose of the Study:

  • To develop and validate estimators for between-cases AUC in nested diagnostic data.
  • To provide methods for estimating variance of between-cases AUC and covariance for multiple readers.
  • To enhance the assessment of patient-level diagnostic performance.

Main Methods:

  • Identification of within-cases and between-cases AUC estimators.
  • Focus on the between-cases AUC estimator for patient-level analysis.
  • Derivation and theoretical proof of variance and covariance estimators.
  • Monte Carlo simulations to characterize estimator behavior.
  • Application to a real-data example.
  • Connecting distribution-based and linear mixed-effect models for simulation.

Main Results:

  • Novel estimators for the variance of between-cases AUC are provided.
  • Covariance estimators for two readers are developed.
  • Theoretical values of estimators are proven.
  • Simulation results characterize the behavior of the proposed estimators.
  • A real-data example demonstrates practical application.

Conclusions:

  • The proposed between-cases AUC estimators effectively address the challenges of nested diagnostic data.
  • The methods provide reliable variance and covariance estimates for improved diagnostic performance evaluation.
  • This work enhances the ability to assess patient-level diagnostic accuracy, particularly with multiple readers.