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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Estimating diagnostic accuracy of multiple binary tests with an imperfect reference standard.

Paul S Albert¹

¹Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD 20892, U.S.A. albertp@ctep.nci.nih.gov

Statistics in Medicine

|December 23, 2008

Summary

Estimating diagnostic accuracy with imperfect reference standards is crucial. This study presents a robust methodology using external data for accurate test performance evaluation, even without a perfect gold standard.

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Area of Science:

Diagnostic medicine
Biostatistics
Medical testing evaluation

Background:

Estimating diagnostic accuracy often relies on a gold standard reference, which is not always available.
Imperfect reference standards are commonly used, introducing potential biases in accuracy estimation.

Purpose of the Study:

To propose a methodology for estimating diagnostic accuracy of multiple binary tests using an imperfect reference standard.
To incorporate external data on the imperfect test's accuracy into the estimation process.
To provide a framework for analyzing diagnostic test performance when a gold standard is absent.

Main Methods:

Development of alternative joint models to characterize dependence between experimental tests.
Utilizing external data sources for the imperfect reference standard's accuracy.
Analytical and simulation techniques to assess the proposed methodology's robustness.

Main Results:

The proposed methodology enables estimation of individual test sensitivity and specificity.
Multivariate post-test probabilities (predictive values) can be accurately estimated.
Inferences on diagnostic accuracy remain robust even with joint model misspecification, provided the imperfect test has high sensitivity and specificity.

Conclusions:

The study offers a valuable statistical approach for diagnostic accuracy assessment in the frequent scenario of imperfect reference standards.
The methodology is applicable to various diagnostic tests, including HIV-antibody tests.
Accurate estimation of diagnostic accuracy is achievable by leveraging external data and appropriate modeling techniques.