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Nonparametric Bayesian estimation of the three-way receiver operating characteristic surface.
Vanda Inácio1, Antónia A Turkman, Christos T Nakas
1Department of Statistics and Operations Research and Center of Statistics and Applications, Faculty of Sciences, Lisbon University, Building C6, 1749-016 Lisbon, Portugal. vanda.kinets@gmail.com
Biometrical Journal. Biometrische Zeitschrift
|November 10, 2011
Summary
This study introduces a robust Bayesian method using finite Polya trees for accurate ROC surface estimation, particularly for complex diagnostic data. The approach provides reliable results for the ROC surface and its volume, outperforming standard methods.
Area of Science:
- Statistics
- Biostatistics
- Machine Learning
Background:
- Estimating three-way ROC surfaces is crucial for evaluating diagnostic tests, especially with continuous data.
- Parametric models can yield misleading results for nonstandard continuous diagnostic data (e.g., skewed or multimodal).
Purpose of the Study:
- To develop a robust, nonparametric Bayesian method for estimating three-way ROC surfaces.
- To address challenges in modeling continuous diagnostic data with nonstandard features.
- To provide reliable inference for the ROC surface and its volume.
Main Methods:
- Utilized mixtures of finite Polya trees (MFPT) priors, a robust Bayesian nonparametric approach.
- Developed data-driven inference techniques for ROC surface estimation.
- Conducted a simulation study to evaluate the method's performance.
Main Results:
- The proposed MFPT approach provides robust and data-driven estimation of the three-way ROC surface.
- The method effectively handles continuous diagnostic data with skewness, multimodality, and other nonstandard features.
- Demonstrated reliable inference for the volume under the ROC surface.
Conclusions:
- The nonparametric Bayesian approach using MFPT priors offers a robust alternative for ROC surface estimation with complex diagnostic data.
- This method improves upon parametric approaches that may falter with nonstandard data distributions.
- The approach was successfully applied to real-world data from a human immunodeficiency virus study.
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