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Profile-likelihood inference for highly accurate diagnostic tests.

John V Tsimikas1, Ronald J Bosch, Brent A Coull

  • 1Department of Mathematics and Statistics, University of Massachusetts at Amherst, 1442 LGRT, Amherst, Massachusetts 01002, USA. tsimikas@math.umass.edu

Biometrics
|December 24, 2002
PubMed
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This study introduces profile-likelihood methods for diagnostic test accuracy using the area under the receiver operating characteristic (ROC) curve with ordinal data. The methods provide reliable confidence intervals, even for small sample sizes and highly accurate tests.

Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Statistical Inference

Background:

  • Assessing diagnostic test accuracy is crucial in clinical practice.
  • Ordinal rating data are common in diagnostic evaluations.
  • Existing methods for accuracy assessment may have limitations with small sample sizes or high accuracy.

Purpose of the Study:

  • To develop and evaluate profile-likelihood inference methods for diagnostic test accuracy.
  • To apply these methods to ordinal rating data using the area under the receiver operating characteristic (ROC) curve.
  • To assess the performance of confidence intervals derived from these methods.

Main Methods:

  • Profile-likelihood inference based on the multinomial distribution.
  • Application to ordinal rating data for area under the ROC curve calculation.

Related Experiment Videos

  • Simulation studies to evaluate confidence interval coverage probabilities.

    • Main Results:

    • The proposed methods provide confidence intervals with acceptable coverage probabilities.
    • Performance remains robust even with small sample sizes and high diagnostic test accuracies.
    • The methods are adaptable to stratified data and correlated ratings.

    Conclusions:

    • Profile-likelihood inference offers a reliable approach for assessing diagnostic test accuracy with ordinal data.
    • The methods are suitable for various clinical scenarios, including those with limited data or high accuracy.
    • The approach is extendable to more complex data structures like stratified or correlated ratings.