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A comparison of C/B ratios from studies using receiver operating characteristic curve analysis
S B Cantor1, C C Sun, G Tortolero-Luna
1Department of Internal Medicine Specialties, The University of Texas M.D. Anderson Cancer Center, Houston 77030-4095, USA.
Journal of Clinical Epidemiology
|October 21, 1999
Summary
This study reviews cost-benefit (C/B) ratios in receiver operating characteristic (ROC) curve analysis for diagnostic tests. Findings can help establish optimal cutpoints for new tests by providing a framework for C/B ratio estimation.
Area of Science:
- Biostatistics
- Medical Decision Making
- Health Economics
Background:
- Receiver Operating Characteristic (ROC) curve analysis is crucial for evaluating diagnostic test performance.
- Determining the optimal cutoff value on an ROC curve requires considering the ratio of costs to benefits (C/B).
- Disease prevalence (p[D]) and the C/B ratio are key components in calculating optimal diagnostic test cutpoints.
Purpose of the Study:
- To conduct a structured review of the medical literature to examine the reported C/B ratios used in ROC curve analysis.
- To identify the range and application of C/B ratios in establishing optimal cutpoints for diagnostic tests.
- To develop a framework for approximating C/B ratios in new clinical situations.
Main Methods:
- A systematic review of the medical literature was performed to identify studies reporting C/B ratios in ROC analysis.
- Studies were analyzed for explicitly calculated C/B ratios or those based on holistic estimates.
- The range of C/B ratios across different clinical scenarios was documented.
Main Results:
- Only two studies explicitly calculated C/B ratios; 11 others used holistic estimates.
- C/B ratios varied widely, from 0.0025 for tuberculosis screening to 2.7 for teeth restoration.
- Life-threatening but curable conditions had C/B ratios below 0.05.
Conclusions:
- The study highlights the limited explicit reporting of C/B ratios in ROC analysis.
- A table of ordered C/B ratios was constructed to aid researchers in approximating values for new diagnostic tests.
- This framework can assist in establishing optimal cutpoints for diagnostic tests across various clinical contexts.
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