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Combining Biomarkers to Improve Diagnostic Accuracy in Detecting Diseases With Group-Tested Data.

Jin Yang¹, Wei Zhang², Paul S Albert³

¹Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland, USA.

Statistics in Medicine

|October 8, 2024

Summary

This study introduces a new method to improve disease detection accuracy using multiple biomarkers from group-tested data. The pairwise model fitting approach enhances diagnostic performance, even with complex group testing challenges.

Keywords:

AUC differential misclassification joint model multiple biomarkers

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Area of Science:

Biostatistics
Medical Diagnostics
Epidemiology

Background:

Accurate disease detection is crucial, often relying on biomarker combinations.
Group-tested data presents challenges like unknown individual statuses and misclassification.
Combining multiple biomarkers requires robust statistical methods for optimal diagnostic accuracy.

Purpose of the Study:

To develop a method for combining multiple biomarkers to enhance disease detection accuracy using group-tested data.
To address challenges including unavailable individual disease statuses and differential misclassification.
To estimate the optimal linear combination of biomarkers and its diagnostic accuracy.

Main Methods:

A pairwise model fitting approach is proposed.
Assumes a multivariate normal distribution for biomarker combinations.
Estimates the distribution of the optimal linear combination and its diagnostic accuracy.

Main Results:

The pairwise model fitting approach effectively estimates diagnostic accuracy from group-tested data.
Simulation studies validated the method's performance.
The approach was successfully applied to chlamydia and COVID-19 detection data.

Conclusions:

The proposed pairwise model fitting approach offers a viable solution for improving diagnostic accuracy with group-tested biomarker data.
This method overcomes key challenges associated with group testing and complex biomarker combinations.
The approach has practical applications in infectious disease diagnosis.