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The economics of diagnosis

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  • George Laking
  • Joanne Lord
  • Alastair Fischer

Abstract

Any population can be divided into two groups, one with the presence of a given disease or condition, and the other without. Diagnosis consists of using tests to sort the population into these groups. Diagnostic tests use a threshold value of a diagnostic variable to distinguish between disease‐positive and disease‐negative individuals. The analysis of error in diagnostic tests has typically been undertaken using receiver‐operator characteristic (ROC) curves. More recently, economic value of information (VOI) methods have characterised the costs and consequences of testing. This paper develops a new method for economic test evaluation, which we call ROTS analysis. The ROTS curve plots the costs and effects of changing test thresholds, in cost‐effectiveness space. We illustrate the use of our method with a worked example, and show how it can answer three key questions: (1) Is there any test that is worth doing? (2) What is a test's optimum operating point in terms of sensitivity and specificity? (3) If two tests are available, which is best? We contrast the merits of our method with those of established ROC and VOI analysis. We argue that ROTS analysis more clearly reveals the link between changing test thresholds and the cost‐effectiveness of different treatments. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • George Laking & Joanne Lord & Alastair Fischer, 2006. "The economics of diagnosis," Health Economics, John Wiley & Sons, Ltd., vol. 15(10), pages 1109-1120, October.
    • Handle: RePEc:wly:hlthec:v:15:y:2006:i:10:p:1109-1120
    DOI: 10.1002/hec.1114
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    1. Gregory S. Zaric, 2008. "Optimal drug pricing, limited use conditions and stratified net benefits for Markov models of disease progression," Health Economics, John Wiley & Sons, Ltd., vol. 17(11), pages 1277-1294, November.
    2. De Donder, Philippe & Bardey, David & Zaporozhets, Vera, 2024. "The Health Technology Assessment Approach of the Economic Value of Diagnostic Tests - A Literature Review," TSE Working Papers 24-1508, Toulouse School of Economics (TSE).
    3. Ariel L Rivas & Mark D Jankowski & Renata Piccinini & Gabriel Leitner & Daniel Schwarz & Kevin L Anderson & Jeanne M Fair & Almira L Hoogesteijn & Wilfried Wolter & Marcelo Chaffer & Shlomo Blum & Tom, 2013. "Feedback-Based, System-Level Properties of Vertebrate-Microbial Interactions," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-16, February.
    4. Markus Eyting, 2020. "A Random Forest a Day Keeps the Doctor Away," Working Papers 2026, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    5. Oke Gerke & Antonia Zapf, 2022. "Convergence Behavior of Optimal Cut-Off Points Derived from Receiver Operating Characteristics Curve Analysis: A Simulation Study," Mathematics, MDPI, vol. 10(22), pages 1-14, November.
    6. Alexander J. Sutton & Nicola J. Cooper & Steve Goodacre & Matthew Stevenson, 2008. "Integration of Meta-analysis and Economic Decision Modeling for Evaluating Diagnostic Tests," Medical Decision Making, , vol. 28(5), pages 650-667, September.
    7. Gregory S. Zaric, 2008. "Optimal drug pricing, limited use conditions and stratified net benefits for Markov models of disease progression," Health Economics, John Wiley & Sons, Ltd., vol. 17(11), pages 1277-1294.

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