Combining Biomarkers to Improve Diagnostic Accuracy in Detecting Diseases With Group‐Tested Data
Year: 2024
Type: article
Abstract: 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.
Source: Statistics in Medicine
DOI: 10.1002/sim.10230
Authors: Jin Yang, Wei Zhang, Paul S. Albert, Aiyi Liu, Zhen Chen
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,190
Funders: National Natural Science Foundation of China, National Institutes of Health, 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
OpenAlex ID: W4403236902