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Related Experiment Video

Updated: Nov 6, 2025

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Tutorial on Biostatistics: Receiver-Operating Characteristic (ROC) Analysis for Correlated Eye Data.

Gui-Shuang Ying1, Maureen G Maguire1, Robert J Glynn2

  • 1Center for Preventive Ophthalmology and Biostatistics, Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Ophthalmic Epidemiology
|May 12, 2021
PubMed
Summary

Ignoring inter-eye correlation in receiver-operating characteristic (ROC) analysis leads to narrower confidence intervals. Nonparametric and cluster bootstrap methods accurately account for correlated eye data in ROC analysis.

Keywords:
Ocular testROC analysisarea under ROC curvecluster bootstrapcorrelated eye data

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Area of Science:

  • Ophthalmology
  • Biostatistics
  • Medical Imaging

Background:

  • Correlated eye data is common in ophthalmological studies.
  • Accurate statistical analysis is crucial for reliable diagnostic and prognostic models.
  • Receiver-operating characteristic (ROC) analysis is a standard method for evaluating diagnostic tests.

Purpose of the Study:

  • To demonstrate methods for receiver-operating characteristic (ROC) analysis of correlated eye data.
  • To compare the performance of different statistical approaches in the presence of inter-eye correlation.
  • To highlight the impact of ignoring inter-eye correlation on confidence intervals for the area under the ROC curve (AUC).

Main Methods:

  • Applied Obuchowski's nonparametric approach and cluster bootstrap for estimating and comparing AUC.
  • Utilized three datasets with varying degrees of inter-eye correlation.
  • Evaluated diagnostic performance in optic neuritis, age-related macular degeneration incidence, and retinopathy of prematurity.

Main Results:

  • The naive approach (ignoring inter-eye correlation) resulted in narrower 95% confidence intervals (CI) for AUC compared to nonparametric and cluster bootstrap methods across all datasets.
  • The degree of CI narrowing was dependent on the magnitude of inter-eye correlation (kappa values).
  • Differences in AUC between models were also subject to narrower CIs with the naive approach.

Conclusions:

  • Ignoring inter-eye correlation in ROC analysis leads to narrower 95% CIs and potential underestimation of uncertainty.
  • Nonparametric and cluster bootstrap approaches effectively account for inter-eye correlation in ROC analysis.
  • These methods provide more reliable confidence intervals for AUC, crucial for accurate clinical interpretation.