Evaluation of Group Testing Algorithms in the Presence of Misclassification

• Activity Number: 367
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Yaakov Malinovsky* and Paul S. Albert and Anindya Roy
• Companies: University of Maryland, Baltimore County and Eunice Kennedy Shriver National Institute of Child Health and Human Development and University of Maryland, Baltimore County
• Keywords: Dorfman two-stage procedure ; Group testing ; Sensitivity ; Specificity ; Optimum group size

Abstract

In the context of group testing screening there is recent work that proposes group testing algorithms for homogeneous and heterogeneous populations in the presence of misclassification. In both cases, the evaluation of the algorithms' performance was based on the expected number of tests per person, with the optimal design being defined by minimizing this quantity. The purpose of this work is to show that although the expected number of tests per person is an appropriate evaluation criteria for group testing when there is no misclassification, it may be problematic when there is misclassification. Specifically, a valid criterion needs to take into account the amount of correct classification and not just the number of tests. We propose a more suitable objective function that accounts for not only the expected number of tests, but also the expected number of correct classifications. We then show how using this objective function that accounts for correct classification is important for design when considering group testing under misclassification. We also present novel analytical results which characterize the optimal Dorfman (1943) design under the misclassification.