Biomarker combination based on the Youden index with and without gold standard

TL;DR

This paper introduces a two-stage method to optimize biomarker combination and cutoff values for disease diagnosis using the Youden index, accommodating imperfect reference standards and improving diagnostic accuracy.

Contribution

It proposes a novel two-stage approach for biomarker combination and cutoff selection based on the Youden index, applicable even with imperfect gold standards.

Findings

Method achieves consistent estimators under regularity conditions.

Simulation studies demonstrate the effectiveness of the approach.

Application to Chinese medicine diagnostics shows practical utility.

Abstract

In clinical practice, multiple biomarkers are often measured on the same subject for disease diagnosis, and combining them can improve diagnostic accuracy. Existing studies typically combine multiple biomarkers by maximizing the Area Under the ROC Curve (AUC), assuming a gold standard exists or that biomarkers follow a multivariate normal distribution. However, practical diagnostic settings require both optimal combination coefficients and an effective cutoff value, and the reference test may be imperfect. In this paper, we propose a two-stage method for identifying the optimal linear combination and cutoff value based on the Youden index. First, it maximizes an approximation of the empirical AUC to estimate the optimal linear coefficients for combining multiple biomarkers. Then, it maximizes the empirical Youden index to determine the optimal cutoff point for disease classification. Under the semiparametric single index model and regularity conditions, the estimators for the linear coefficients, cutoff point, and Youden index are consistent. This method is also applicable when the reference standard is imperfect. We demonstrate the performance of our method through simulations and apply it to construct a diagnostic scale for Chinese medicine.