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Two‐Stage Hierarchical Group Testing for Multiple Infections with Application to the Infertility Prevention Project

Two‐Stage Hierarchical Group Testing for Multiple Infections with Application to the Infertility... Summary Screening for sexually transmitted diseases (STDs) has benefited greatly from the use of group testing (pooled testing) to lower costs. With the development of assays that detect multiple infections, screening practices now involve testing pools of individuals for multiple infections simultaneously. Building on the research for single infection group testing procedures, we examine the performance of group testing for multiple infections. Our work is motivated by chlamydia and gonorrhea testing for the infertility prevention project (IPP), a national program in the United States. We consider a two‐stage pooling algorithm currently used to perform testing for the IPP. We first derive the operating characteristics of this algorithm for classification purposes (e.g., expected number of tests, misclassification probabilities, etc.) and identify pool sizes that minimize the expected number of tests. We then develop an expectation–maximization (EM) algorithm to estimate probabilities of infection using both group and individual retest responses. Our research shows that group testing can offer large cost savings when classifying individuals for multiple infections and can provide prevalence estimates that are actually more efficient than those from individual testing. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrics Oxford University Press

Two‐Stage Hierarchical Group Testing for Multiple Infections with Application to the Infertility Prevention Project

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References (26)

Publisher
Oxford University Press
Copyright
© 2013, The International Biometric Society
ISSN
0006-341X
eISSN
1541-0420
DOI
10.1111/biom.12080
pmid
24117173
Publisher site
See Article on Publisher Site

Abstract

Summary Screening for sexually transmitted diseases (STDs) has benefited greatly from the use of group testing (pooled testing) to lower costs. With the development of assays that detect multiple infections, screening practices now involve testing pools of individuals for multiple infections simultaneously. Building on the research for single infection group testing procedures, we examine the performance of group testing for multiple infections. Our work is motivated by chlamydia and gonorrhea testing for the infertility prevention project (IPP), a national program in the United States. We consider a two‐stage pooling algorithm currently used to perform testing for the IPP. We first derive the operating characteristics of this algorithm for classification purposes (e.g., expected number of tests, misclassification probabilities, etc.) and identify pool sizes that minimize the expected number of tests. We then develop an expectation–maximization (EM) algorithm to estimate probabilities of infection using both group and individual retest responses. Our research shows that group testing can offer large cost savings when classifying individuals for multiple infections and can provide prevalence estimates that are actually more efficient than those from individual testing.

Journal

Biometrics – Oxford University Press

Published: Dec 1, 2013

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