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Statistical Inference for Box-Cox based Receiver Operating Characteristic Curves.

Leonidas E Bantis1, Benjamin Brewer1, Christos T Nakas2,3

  • 1Department of Biostatistics and Data Science, University of Kansas Medical Center, Kansas City, KS, USA.

Statistics in Medicine
|November 17, 2024
PubMed
Summary
This summary is machine-generated.

This study addresses challenges in Receiver Operating Characteristic (ROC) curve analysis by accounting for variability in the Box-Cox transformation parameter. A new R package,

Keywords:
Box–CoxROCcorrelated biomarkersdelta methodsensitivitysmooth ROCspecificity

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Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Statistical Modeling

Background:

  • Receiver Operating Characteristic (ROC) curve analysis is crucial for evaluating diagnostic tests and biomarkers.
  • Parametric statistical inference for ROC curves often uses the Box-Cox transformation to normality.
  • A key challenge is accurately accounting for the variability of the estimated Box-Cox transformation parameter, which is frequently ignored.

Purpose of the Study:

  • To review literature on Box-Cox transformations in ROC analysis.
  • To present a methodology for incorporating the estimation variability of the Box-Cox parameter.
  • To develop a general framework for statistical inference on ROC curve functionals.

Main Methods:

  • Literature review of Box-Cox transformation applications in ROC analysis.
  • Development of a statistical framework to account for the estimation of the Box-Cox transformation parameter.
  • Application of the framework to various diagnostic accuracy measures (AUC, Youden index, etc.).

Main Results:

  • A comprehensive review of existing literature and methodologies.
  • A general inferential framework for ROC analysis incorporating Box-Cox parameter variability.
  • Development and release of the 'rocbc' R package for practical implementation.

Conclusions:

  • Accurate statistical inference in ROC analysis requires accounting for Box-Cox transformation parameter estimation.
  • The proposed framework and 'rocbc' package provide robust tools for ROC curve analysis.
  • This work enhances the reliability of diagnostic test and biomarker evaluations.