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Comparison of group testing algorithms for case identification in the presence of test error
Hae-Young Kim1, Michael G Hudgens, Jonathan M Dreyfuss
1Department of Biostatistics, School of Public Health, University of North Carolina at Chapel Hill, 3107-E McGavran-Greenberg Hall, Chapel Hill, North Carolina 27599, USA.
Abstract:
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.
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