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Computing sample size for receiver operating characteristic studies

N A Obuchowski1

  • 1Department of Biostatistics, Cleveland Clinic Foundation, Ohio.

Investigative Radiology
|February 1, 1994
PubMed
Summary
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Hanley and McNeil's method for receiver operating characteristic (ROC) curve sample size estimation may underestimate needs. A new binormal distribution method offers more accurate sample size calculations for ROC studies using rating data.

Area of Science:

  • Medical diagnostics
  • Statistical modeling
  • Biostatistics

Background:

  • The Hanley and McNeil (1982) nonparametric method is crucial for planning receiver operating characteristic (ROC) study sample sizes.
  • Its validity for rating data with varying standard deviation ratios remains unexamined.

Purpose of the Study:

  • To investigate the accuracy of the Hanley and McNeil method for estimating the standard error of the area under the ROC curve (AUC) with rating data.
  • To propose and evaluate an alternative method for AUC standard error estimation.

Main Methods:

  • A simulation study compared empirical AUC standard errors against Hanley and McNeil's estimates across diverse parameters.
  • An alternative standard error computation method, based on a binormal distribution, was developed.

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Main Results:

  • Hanley and McNeil's method can result in underestimation of the required minimum sample size for ROC studies.
  • The proposed binormal distribution-based method yields more appropriate sample size estimates.

Conclusions:

  • For ROC studies employing rating data, the standard error estimator derived from the binormal distribution is recommended for accurate sample size determination.
  • This approach ensures more reliable planning for ROC research.