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ROC curves with multiple marker measurements

P A Murtaugh1

  • 1Department of Statistics, Oregon State University, Corvallis 97331, USA.

Biometrics
|December 1, 1995
PubMed
Summary

Repeatedly measured markers can alter receiver operating characteristic (ROC) curves. The number of measurements and within-subject correlation significantly influence ROC curve shape, impacting diagnostic accuracy assessment.

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Area of Science:

  • Biostatistics
  • Medical Diagnostics

Background:

  • Receiver operating characteristic (ROC) curves are crucial for evaluating diagnostic tests.
  • Assessing markers measured repeatedly requires understanding how measurement frequency affects ROC curve properties.

Purpose of the Study:

  • To investigate the impact of repeated marker measurements on ROC curve characteristics.
  • To analyze how within-subject measurement variability and correlation influence ROC curve shape.

Main Methods:

  • Exploration of ROC curve properties for markers with multiple measurements per subject.
  • Modeling scenarios where the number of measurements differs between positive and negative response groups.
  • Analysis of the influence of within-subject measurement correlation on ROC curve behavior.

Main Results:

  • The number of measurements per subject can cause non-informative marker ROC curves to deviate from the diagonal.
  • Even informative markers may exhibit ROC curves below the single-measurement baseline if negative responders have more measurements.
  • Within-subject measurement correlation strongly influences the observed ROC curve shape.

Conclusions:

  • Repeated marker measurements introduce complexities in ROC analysis.
  • The number of measurements and their correlation are critical factors affecting diagnostic test evaluation using ROC curves.
  • Careful consideration of measurement properties is essential for accurate interpretation of ROC curves in repeated-measurement settings.

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