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Receiver operating characteristic estimation and threshold selection criteria in three-class classification problems

Duc-Khanh To1, Gianfranco Adimari1, Monica Chiogna2

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|April 1, 2022
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Summary
This summary is machine-generated.

This study introduces a linear-mixed model approach to accurately evaluate diagnostic tests using clustered data. It addresses bias from omitted variables and proposes methods for threshold selection in biomarker analysis.

Keywords:
Box–Cox transformationReceiver operating characteristic analysisclustered datacovariate adjustmentlinear-mixed models

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Area of Science:

  • Biostatistics
  • Biomarker Discovery
  • Statistical Modeling

Background:

  • Diagnostic test and biomarker evaluation is complex, especially with clustered data.
  • Omission of cluster-level effects or individual covariates can introduce bias.
  • Clustered data is prevalent in many research designs.

Purpose of the Study:

  • To develop a statistical method for evaluating continuous diagnostic tests in clustered data.
  • To address bias arising from cluster-level effects and individual covariates.
  • To propose methods for optimal threshold selection in three-class classification problems.

Main Methods:

  • Utilized a linear-mixed model approach to handle clustered data.
  • Developed methods for estimating covariate-specific receiver operating characteristic surfaces.
  • Proposed and analyzed three estimators for optimal threshold selection, proving their consistency and asymptotic normality.

Main Results:

  • The proposed linear-mixed model approach effectively exploits clustered data structure.
  • Methods for estimating covariate-specific ROC surfaces were successfully developed.
  • The consistency and asymptotic normality of proposed threshold estimators were mathematically demonstrated.

Conclusions:

  • The linear-mixed model approach provides a robust framework for evaluating diagnostic tests with clustered data.
  • The study offers practical solutions for threshold selection in complex biomarker studies.
  • Applied the methodology to gene expression data for distinguishing neuronal subtypes, demonstrating biomarker utility.