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Interval estimation in three-class receiver operating characteristic analysis: A fairly general approach based on the
Duc-Khanh To1,2, Gianfranco Adimari3, Monica Chiogna4
1Faculty of Mathematics and Computer Science, University of Science, Ho Chi Minh City, Vietnam.
Statistical Methods in Medical Research
|March 19, 2024
Summary
This study introduces novel empirical likelihood methods for evaluating three-class diagnostic tests. These flexible, nonparametric approaches improve interval estimation in receiver operating characteristic analysis.
Area of Science:
- Statistics
- Biostatistics
- Nonparametric Inference
Background:
- Empirical likelihood is a nonparametric tool that preserves large-sample properties of parametric likelihood.
- Assessing discriminatory power of diagnostic tests is crucial in medical research.
- Three-class receiver operating characteristic (ROC) analysis presents unique inferential challenges.
Purpose of the Study:
- To develop and evaluate novel empirical likelihood-based methods for assessing the discriminatory power of three-class diagnostic tests.
- To focus on interval estimation within the three-class ROC analysis framework.
- To provide efficient techniques for various inferential tasks in this setting.
Main Methods:
- Utilizing empirical likelihood as a nonparametric framework.
- Developing novel theoretical results and tailored techniques for interval estimation.
- Conducting extensive simulation experiments to compare performance against existing methods.
Main Results:
- The proposed empirical likelihood methods are highly flexible and adaptable to various distributions, including mixtures.
- New proposals demonstrate competitive performance compared to existing methods.
- The techniques are suitable for accommodating complex target population distributions.
Conclusions:
- The novel empirical likelihood approach offers a powerful and flexible tool for analyzing three-class diagnostic tests.
- The methods provide robust interval estimation in three-class ROC analysis.
- The study illustrates practical application with a real data example and suggests future research directions.
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