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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
PubMed

Insights

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.

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.

Keywords:
AUCdifferential misclassificationjoint modelmultiple biomarkers

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