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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Flexible diagnostic measures and new cut-point selection methods under multiple ordered classes.

Yingdong Feng1, Lili Tian1

  • 1Department of Biostatistics, University at Buffalo, Buffalo, New York, USA.

Pharmaceutical Statistics
|August 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces new methods to accurately evaluate diagnostic markers for diseases with multiple ordered stages, like cancer. These novel approaches improve biomarker assessment and aid in clinical decision-making for conditions such as ovarian cancer.

Keywords:
ROC analysisYouden indexarea under curveclassificationmedical diagnosistree or umbrella ordering

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Oncology

Background:

  • Medical diagnosis often involves classifying patients into multiple ordered categories (e.g., cancer staging).
  • Existing accuracy measures for diagnostic markers are insufficient for ordered classes, failing to distinguish between different biomarkers effectively.
  • Accurate assessment of diagnostic markers is crucial for reliable medical diagnosis and treatment planning.

Purpose of the Study:

  • To develop novel methods for evaluating biomarker accuracy in the context of multiple ordered diagnostic classes.
  • To introduce flexible overall measures and computationally simple cut-point selection methods for enhanced diagnostic marker assessment.
  • To address the limitations of current measures in differentiating between biomarkers with ordered classifications.

Main Methods:

  • A multi-step procedure was developed for evaluating biomarker accuracy under multiple ordered classes.
  • Two new flexible overall accuracy measures were proposed.
  • Three new cut-point selection methods were introduced, emphasizing computational ease.
  • Numerical exploration via a simulation study assessed the performance of the proposed methods.

Main Results:

  • The proposed multi-step procedure and new measures effectively evaluate biomarker accuracy for ordered diagnostic classes.
  • The new cut-point selection methods demonstrated computational ease and effectiveness.
  • Simulation studies confirmed the superior performance of the proposed measures and methods compared to existing approaches.
  • Application to an ovarian cancer dataset (Prostate, Lung, Colorectal, and Ovarian cancer study) demonstrated practical utility.

Conclusions:

  • The developed methods provide a more accurate and flexible framework for assessing diagnostic marker accuracy in ordered classification scenarios.
  • The proposed cut-point selection methods offer practical advantages for clinical application.
  • These advancements can lead to improved diagnostic marker selection and risk stratification, particularly in complex diseases like ovarian cancer.