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An R-Based Landscape Validation of a Competing Risk Model

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On estimating the area under the ROC curve in ranked set sampling.

M Mahdizadeh¹, Ehsan Zamanzade^2,3

¹Department of Statistics, 185150Hakim Sabzevari University, Sabzevar, Iran.

Statistical Methods in Medical Research

|May 13, 2022

Summary

New methods improve biomarker accuracy assessment using ranked set sampling. These novel estimators offer advantages for evaluating biomarker performance in medical research, enhancing diagnostic accuracy.

Keywords:

Judgment ranking power transformation receiver operating characteristic curve

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Area of Science:

Biostatistics
Medical Informatics
Diagnostic Accuracy

Background:

Receiver operating characteristic (ROC) curves are standard for evaluating continuous biomarkers in medical research.
The area under the ROC curve (AUC) quantifies overall biomarker performance.
Ranked set sampling (RSS) offers potential efficiency gains over simple random sampling.

Purpose of the Study:

To develop and evaluate novel estimators for the area under the ROC curve (AUC) specifically designed for ranked set sampling (RSS).
To compare the performance of the proposed AUC estimators against existing methods.

Main Methods:

Development of three new AUC estimators for RSS data.
The first estimator assumes data normality.
The other two estimators utilize a Box-Cox transformation followed by parametric or kernel-density-based approaches.

Main Results:

A simulation study demonstrated that the new RSS-based AUC estimators provide advantages in specific scenarios.
The proposed methods showed competitive or superior performance compared to existing literature estimators under certain conditions.
The utility of the new estimators was validated using real medical data.

Conclusions:

The developed AUC estimators offer valuable alternatives for biomarker performance evaluation in RSS settings.
These methods enhance the accuracy and efficiency of biomarker assessment in medical research.
The findings support the application of these novel estimators in clinical practice and further research.