Combining Biomarkers to Improve Diagnostic Accuracy in Detecting Diseases With Group‐Tested Data
We consider the problem of combining multiple biomarkers to improve the diagnostic accuracy of detecting a disease when only group-tested data on the disease status are available. There are several challenges in addressing this problem, including unavailable individual disease statuses, differential misclassification depending on group size and number of diseased individuals in the group, and extensive computation due to a large number of possible combinations of multiple biomarkers. To tackle these issues, we propose a pairwise model fitting approach to estimating the distribution of the optimal linear combination of biomarkers and its diagnostic accuracy under the assumption of a multivariate normal distribution. The approach is evaluated in simulation studies and applied to data on chlamydia detection and COVID-19 diagnosis.
Authors
- Zhen Chen (ORCID: https://orcid.org/0000-0002-9946-1362)
- Jin Yang (ORCID: https://orcid.org/0000-0003-0625-5189)
- Paul S. Albert (ORCID: https://orcid.org/0000-0003-1658-1068)
- Aiyi Liu (ORCID: https://orcid.org/0000-0002-6618-5082)
- Wei Zhang (ORCID: https://orcid.org/0009-0007-0725-5021)
Institutions
- National Institutes of Health (US)
- Chinese Academy of Sciences (CN)
- Academy of Mathematics and Systems Science (CN)
- National Cancer Institute (US)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (US)
Publication Details
- Journal
- Statistics in Medicine
- Published
- 2024-10-07
- DOI
- https://doi.org/10.1002/sim.10230
- Citations
- 6
- Primary Topic
- Statistical Methods and Bayesian Inference
- Type
- article
- Field-Weighted Citation Impact
- 4.25
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- National Cancer Institute
- National Institute of Child Health and Human Development
- Eunice Kennedy Shriver National Institute of Child Health and Human Development