Peer-reviewed veterinary case report
Receiver-operating characteristic curves and likelihood ratios: improvements over traditional methods for the evaluation and application of veterinary clinical pathology tests.
- Journal:
- Veterinary clinical pathology
- Year:
- 2006
- Authors:
- Gardner, Ian A & Greiner, Matthias
- Affiliation:
- Department of Medicine and Epidemiology · United States
Plain-English summary
This study discusses a method called receiver-operating characteristic (ROC) curves, which helps evaluate the accuracy of tests used in veterinary labs without relying on specific cutoff points. The area under the ROC curve gives a good overall measure of how well a test works and allows for comparisons between different tests or equipment. Although this method hasn't been widely used in veterinary medicine yet, it could be very helpful alongside traditional measures of test accuracy. The researchers also looked at likelihood ratios, which show how the likelihood of a condition changes based on test results. Overall, the study suggests that using ROC analysis and likelihood ratios can improve how we assess veterinary tests.
Abstract
Receiver-operating characteristic (ROC) curves provide a cutoff-independent method for the evaluation of continuous or ordinal tests used in clinical pathology laboratories. The area under the curve is a useful overall measure of test accuracy and can be used to compare different tests (or different equipment) used by the same tester, as well as the accuracy of different diagnosticians that use the same test material. To date, ROC analysis has not been widely used in veterinary clinical pathology studies, although it should be considered a useful complement to estimates of sensitivity and specificity in test evaluation studies. In addition, calculation of likelihood ratios can potentially improve the clinical utility of such studies because likelihood ratios provide an indication of how the post-test probability changes as a function of the magnitude of the test results. For ordinal test results, likelihood ratios can be calculated on a category-specific basis from the empirical data or by using the slope of the line joining adjacent category limits on the ROC curve. For continuous test results, data need to be categorized into intervals for estimation of likelihood ratios, or they can be calculated as the slope (tangent) to the ROC curve at a unique test value. We use ROC analysis and calculate likelihood ratios to evaluate the performance of tests reported in 2 articles previously published in this journal.
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Search related cases →Original publication: https://pubmed.ncbi.nlm.nih.gov/16511785/