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Related Experiment Videos

Nonparametric analysis of clustered ROC curve data

N A Obuchowski1

  • 1Department of Biostatistics and Epidemiology, Cleveland Clinic Foundation, Ohio 44195-5196, USA.

Biometrics
|June 1, 1997
PubMed
Summary

Estimating diagnostic test accuracy with clustered data requires accounting for intracluster correlation. This study extends existing methods to accurately estimate Receiver Operating Characteristics (ROC) curve areas for clustered diagnostic test results.

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Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Epidemiology

Background:

  • Standard methods for diagnostic test accuracy estimation assume independent results, which is often violated in practice.
  • Multiple test results from the same patient (clustered data) are common, necessitating methods that account for intracluster correlation.
  • Ignoring intracluster correlation can lead to inflated statistical test sizes and inaccurate accuracy estimates.

Purpose of the Study:

  • To extend the structural components method for estimating Receiver Operating Characteristics (ROC) curve area in the presence of clustered data.
  • To incorporate concepts of design effect and effective sample size for robust estimation with clustered binary data.
  • To provide a method applicable to both continuous and ordinal diagnostic test results.

Main Methods:

  • Extension of the DeLong, DeLong, and Clarke-Pearson structural components method.
  • Incorporation of Rao and Scott's concepts of design effect and effective sample size for clustered data.
  • Monte Carlo simulation study to evaluate the performance of the proposed method under various correlation structures.

Main Results:

  • Statistical tests assuming independence show inflated sizes when intracluster correlation is present.
  • The proposed method effectively handles various intracluster correlations, including those between true disease statuses and test results.
  • The method demonstrates applicability to both continuous and ordinal diagnostic test results.

Conclusions:

  • The proposed extension provides a statistically sound approach for estimating diagnostic test accuracy with clustered data.
  • Accounting for intracluster correlation is crucial for accurate inference in studies with multiple test results per patient.
  • The method offers a valuable tool for researchers conducting diagnostic accuracy studies with clustered designs.

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