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A serial risk score approach to disease classification that accounts for accuracy and cost
Author(s) -
Huynh Dat,
Laeyendecker Oliver,
Brookmeyer Ron
Publication year - 2014
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12217
Subject(s) - logistic regression , diagnostic accuracy , diagnostic test , medicine , test (biology) , computer science , disease , statistics , cohort , machine learning , pathology , mathematics , pediatrics , biology , paleontology
Summary The performance of diagnostic tests for disease classification is often measured by accuracy (e.g., sensitivity or specificity); however, costs of the diagnostic test are a concern as well. Combinations of multiple diagnostic tests may improve accuracy, but incur additional costs. Here, we consider serial testing approaches that maintain accuracy while controlling costs of the diagnostic tests. We present a serial risk score classification approach. The basic idea is to sequentially test with additional diagnostic tests just until persons are classified. In this way, it is not necessary to test all persons with all tests. The methods are studied in simulations and compared with logistic regression. We applied the methods to data from HIV cohort studies to identify HIV infected individuals who are recently infected ( < 1 year) by testing with assays for multiple biomarkers. We find that the serial risk score classification approach can maintain accuracy while achieving a reduction in cost compared to testing all individuals with all assays.