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Interval Estimation for the Youden Index and Optimal Cut-Off Point in AUC-Based Optimal Combinations of Multivariate
1Department of Statistics, Yazd University, Yazd, Iran.
Pharmaceutical Statistics
|March 19, 2025
Summary
This study introduces new methods for estimating the Youden index and optimal cut-off points for biomarker combinations, accounting for covariates. These methods provide reliable interval estimations for improved diagnostic accuracy in clinical settings.
Area of Science:
- Biostatistics
- Medical Informatics
- Diagnostic Accuracy Research
Background:
- Accurate estimation of diagnostic test performance is crucial in clinical practice.
- The Youden index and optimal cut-off point are key metrics for evaluating binary diagnostic tests.
- Existing methods may not adequately address multivariate biomarkers or the influence of covariates.
Purpose of the Study:
- To develop and evaluate interval estimation methods for the Youden index and optimal cut-off point.
- To specifically address AUC-based optimal combinations of multivariate normally distributed biomarkers.
- To incorporate the impact of covariates into these estimations.
Main Methods:
- Development of a generalized pivotal confidence interval.
- Implementation of a Bayesian credible interval.
- Application of various bootstrap confidence intervals.
- Monte Carlo simulation study for performance evaluation.
Main Results:
- The proposed methods provide robust interval estimations for the Youden index and optimal cut-off point.
- Performance evaluation through simulation demonstrates the reliability of the developed intervals.
- The methods effectively handle multivariate biomarkers and covariate adjustments.
Conclusions:
- The presented interval estimation techniques offer valuable tools for assessing diagnostic accuracy with complex biomarker combinations.
- These methods enhance the reliability of Youden index and optimal cut-off point estimations in the presence of covariates.
- Application to a diabetic dataset illustrates practical utility in real-world scenarios.
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