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Estimating cutoff values for diagnostic tests to achieve target specificity using extreme value theory
Sierra Pugh1, Bailey K Fosdick2, Mary Nehring3
1Department of Statistics, Colorado State University, 102 Statistics Building, Fort Collins, 80523, Colorado, USA.
BMC Medical Research Methodology
|February 8, 2024
Summary
Selecting optimal diagnostic test cutoffs is crucial for emerging diseases. Extreme value theory offers a robust method for high specificity testing with limited data, outperforming traditional approaches.
Area of Science:
- Biostatistics
- Epidemiology
- Diagnostic Test Development
Background:
- Accurate diagnostic tests are vital for monitoring emerging diseases, especially in early epidemic stages with low prevalence.
- High specificity is crucial to minimize false positives, but selecting appropriate test cutoffs with limited validation data is challenging.
- Existing statistical methods for estimating quantiles may not be optimal for extreme values in the context of new diagnostic tests.
Purpose of the Study:
- To propose and evaluate a novel method using extreme value theory for selecting diagnostic test cutoffs with predetermined specificity.
- To compare the performance of the extreme value theory-based method against existing cutoff selection techniques.
- To assess the impact of different specificity targets on test accuracy and prevalence estimation.
Main Methods:
- Utilized extreme value theory by fitting a Pareto distribution to the upper tail of negative control data to determine optimal cutoffs.
- Compared the proposed method with five other previously suggested cutoff selection methods.
- Conducted a data analysis and simulation study using COVID-19 enzyme-linked immunosorbent assay antibody test results.
Main Results:
- The extreme value approach demonstrated minimal bias when targeting a high specificity of 0.995.
- The empirical quantile method performed well for a specificity target of 0.95.
- Higher target specificity improved overall test accuracy in low prevalence scenarios, while lower specificity reduced prevalence estimation variability in higher prevalence settings.
Conclusions:
- Extreme value theory and empirical quantile methods are superior to normal-based methods for determining disease testing cutoffs with limited training data.
- Recommend extreme value-based methods for high specificity targets and empirical quantiles for lower specificity targets in diagnostic test development.
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