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Comparing diagnostic tests on benefit-risk.

Gene Pennello1, Norberto Pantoja-Galicia1, Scott Evans2

  • 1a Center for Devices and Radiological Health , Food and Drug Administration , Silver Spring , Maryland , USA.

Journal of Biopharmaceutical Statistics
|August 23, 2016
PubMed
Summary
This summary is machine-generated.

Evaluating diagnostic tests using benefit-risk analysis is more conclusive than accuracy alone. Diagnostic yield and decision theory offer a comprehensive approach to assessing test performance and clinical utility.

Keywords:
Clinical utilitycost/benefit ratiodecision theorydiagnostic yieldrelative net benefitrisk thresholdweighted accuracy

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Area of Science:

  • Medical Diagnostics
  • Decision Theory
  • Health Economics

Background:

  • Diagnostic test accuracy metrics like sensitivity and specificity can be insufficient for comparison.
  • Clinical consequences of diagnostic errors, including false positives and negatives, are critical for comprehensive evaluation.

Purpose of the Study:

  • To propose a benefit-risk framework for evaluating diagnostic tests.
  • To introduce diagnostic yield and decision theory for a more conclusive test comparison.

Main Methods:

  • Constructing a diagnostic yield table detailing true/false positives/negatives and adverse consequences.
  • Developing a decision theory to interpret diagnostic yield quantities.
  • Calculating weighted accuracy as a measure of expected utility relative to a perfect test.

Main Results:

  • Diagnostic yield provides a distribution of test outcomes, including adverse events from false positives.
  • Weighted accuracy, considering prevalence and error importance, offers a robust evaluation metric.
  • Plots of diagnostic yield, weighted accuracy, and net benefit can visualize test performance across different scenarios.

Conclusions:

  • Benefit-risk evaluation, incorporating clinical consequences, is superior to accuracy-based comparisons for diagnostic tests.
  • Diagnostic yield and decision theory provide a framework for understanding and comparing the clinical utility of diagnostic strategies.
  • The proposed methods, illustrated with colorectal cancer screening, can guide optimal test selection in practice.