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Comparison of Group Testing Algorithms for Case Identification in the Presence of Test Error
Author(s) -
Kim HaeYoung,
Hudgens Michael G.,
Dreyfuss Jonathan M.,
Westreich Daniel J.,
Pilcher Christopher D.
Publication year - 2007
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/j.1541-0420.2007.00817.x
Subject(s) - pooling , group testing , word error rate , identification (biology) , statistics , type i and type ii errors , false discovery rate , computer science , algorithm , mathematics , artificial intelligence , biology , combinatorics , biochemistry , botany , gene
Summary We derive and compare the operating characteristics of hierarchical and square array‐based testing algorithms for case identification in the presence of testing error. The operating characteristics investigated include efficiency (i.e., expected number of tests per specimen) and error rates (i.e., sensitivity, specificity, positive and negative predictive values, per‐family error rate, and per‐comparison error rate). The methodology is illustrated by comparing different pooling algorithms for the detection of individuals recently infected with HIV in North Carolina and Malawi.