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Activity Number: 169
Type: Contributed
Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #311901 View Presentation
Title: Hierarchical Group Testing for Multiple Infections
Author(s): Peijie Hou*+ and Joshua Tebbs
Companies: University of South Carolina and University of South Carolina
Keywords: Correlated binary response ; Efficiency ; Group testing ; Identification ; Markov chain ; EM algorithm
Abstract:

Group testing is widely used to screen a large number of individuals for a rare infection. However, screening practices have historically involved testing for single infection only. Recently, Tebbs et al. (2013, Biometrics) have evaluated the performance of a two-stage algorithm used for chlamydia and gonorrhea testing as part of the infertility prevention project (IPP). In this article, we extend this two-stage algorithm to include a larger number of stages where pools of individuals are tested for multiple infections simultaneously. In order to investigate case identification performance in higher-stage algorithm, we use a Markov chain framework to derive closed-form expressions for the expected number of tests and misclassification probabilities. We then identify the optimal number of stages and pool sizes that minimize the expected number of tests. Probabilities of infections are estimated by implementing an EM algorithm using all available test responses. Overall, we offer compelling evidence that a higher-stage algorithm can provide large cost savings while providing prevalence estimates that are as efficient as those from the two-stage algorithm in Tebbs et al. (2013).


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