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Related Experiment Videos

Focusing on Diagnostic Accuracy Jeopardises Therapeutic Optimality.

Jack Dowie1,2, Mette Kjer Kaltoft2, Vije Kumar Rajput1,3

  • 1London School of Hygiene and Tropical Medicine, London, UK.

Studies in Health Technology and Informatics
|May 17, 2025
PubMed
Summary

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Choosing a cut-off for ordinal tests requires evaluating the trade-off between false alarms and false reassurances. Our FARN metric transparently reveals this error rate, aiding optimal test interpretation for better patient care.

Area of Science:

  • Medical Diagnostics
  • Biostatistics
  • Psychometrics

Background:

  • Ordinal tests are commonly used for diagnosis and screening.
  • Cut-off selection often relies on maximizing sensitivity and specificity, treating errors equally.
  • This approach overlooks the differential costs of false positives and false negatives.

Purpose of the Study:

  • To introduce the False Alarms per False Reassurance Number (FARN) metric.
  • To demonstrate how FARN transparently quantifies error trade-offs at different cut-offs.
  • To advocate for the routine reporting of FARN to inform optimal cut-off selection.

Main Methods:

  • Defined FARN as the ratio of false alarms to false reassurances.
  • Calculated FARN for all possible cut-offs on ordinal tests.
Keywords:
Generalised Anxiety Disordercut-offdiagnostic accuracyfalse alarm ratefalse reassurance ratetest evaluationtest validation

Related Experiment Videos

  • Illustrated the method using the GAD-7 for Generalized Anxiety Disorder detection.

    • Main Results:

    • Existing methods implicitly embed error trade-offs without transparency.
    • FARN provides a clear quantitative measure of the balance between false alarms and false reassurances.
    • The GAD-7 example demonstrates the practical application of FARN in understanding cut-off implications.

    Conclusions:

    • Focusing solely on diagnostic accuracy metrics like sensitivity and specificity can be misleading.
    • Routine reporting of FARN is essential for transparent cut-off selection in diagnostic and screening tests.
    • Considering the relative disutility of different error types leads to more appropriate clinical decision-making and therapeutic outcomes.