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Bootstrap-based procedures for inference in nonparametric receiver-operating characteristic curve regression

María Xosé Rodríguez-Álvarez1,2,3, Javier Roca-Pardiñas1, Carmen Cadarso-Suárez4

  • 11 Department of Statistics and Operations Research and Biomedical Research Centre, University of Vigo, Vigo, Spain.

Statistical Methods in Medical Research
|December 14, 2017
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Summary
This summary is machine-generated.

Evaluating diagnostic test accuracy is crucial. This study introduces methods to assess how patient information affects diagnostic accuracy using covariate-specific receiver-operating characteristic curves and provides an R-package for practical application.

Keywords:
Receiver-operating characteristic curvebootstrapcomputer-aided diagnosisgeneralised additive models

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Machine Learning

Background:

  • Rigorous evaluation of diagnostic test accuracy is essential before clinical implementation.
  • Receiver-operating characteristic (ROC) curves are standard for continuous diagnostic tests.
  • Assessing the impact of patient-specific covariates on diagnostic accuracy requires advanced methods.

Purpose of the Study:

  • To develop and present novel inferential procedures for analyzing covariate-specific receiver-operating characteristic (ROC) curves.
  • To investigate the influence of continuous covariates and factor-by-curve interactions on diagnostic accuracy.
  • To provide practical tools for applying these advanced statistical methods in research and clinical settings.

Main Methods:

  • Utilized direct regression modeling and nonparametric smoothing techniques to estimate covariate-specific ROC curves.
  • Developed generalized additive models for ROC curve analysis.
  • Implemented two bootstrap-based tests to assess covariate effects and interaction terms.

Main Results:

  • The proposed bootstrap-based inferential procedures demonstrated validity through simulation studies.
  • The methods effectively test the impact of continuous covariates on the ROC curve.
  • The study confirmed the ability to detect factor-by-curve interactions.

Conclusions:

  • The developed methods offer robust statistical tools for evaluating diagnostic accuracy in the presence of covariates.
  • The R-package 'npROCRegression' facilitates the application of these advanced techniques.
  • These advancements are applicable to areas like computer-aided diagnosis, exemplified by breast cancer tumor detection.