TY - JOUR
T1 - Array testing for multiplex assays
AU - Hou, Peijie
AU - Tebbs, Joshua M.
AU - Wang, Dewei
AU - McMahan, Christopher S.
AU - Bilder, Christopher R.
N1 - Funding Information:
This research was supported by Grant R01 AI121351 from the National Institutes of Health.
Publisher Copyright:
© The Author 2018. Published by Oxford University Press. All rights reserved.
PY - 2020
Y1 - 2020
N2 - Group testing involves pooling individual specimens (e.g., blood, urine, swabs, etc.) and testing the pools for the presence of disease. When the proportion of diseased individuals is small, group testing can greatly reduce the number of tests needed to screen a population. Statistical research in group testing has traditionally focused on applications for a single disease. However, blood service organizations and large-scale disease surveillance programs are increasingly moving towards the use of multiplex assays, which measure multiple disease biomarkers at once. Tebbs and others (2013, Two-stage hierarchical group testing for multiple infections with application to the Infertility Prevention Project. Biometrics 69, 1064–1073) and Hou and others (2017, Hierarchical group testing for multiple infections. Biometrics 73, 656–665) were the first to examine hierarchical group testing case identification procedures for multiple diseases. In this article, we propose new non-hierarchical procedures which utilize two-dimensional arrays. We derive closed-form expressions for the expected number of tests per individual and classification accuracy probabilities and show that array testing can be more efficient than hierarchical procedures when screening individuals for multiple diseases at once. We illustrate the potential of using array testing in the detection of chlamydia and gonorrhea for a statewide screening program in Iowa. Finally, we describe an R/Shiny application that will help practitioners identify the best multiple-disease case identification algorithm.
AB - Group testing involves pooling individual specimens (e.g., blood, urine, swabs, etc.) and testing the pools for the presence of disease. When the proportion of diseased individuals is small, group testing can greatly reduce the number of tests needed to screen a population. Statistical research in group testing has traditionally focused on applications for a single disease. However, blood service organizations and large-scale disease surveillance programs are increasingly moving towards the use of multiplex assays, which measure multiple disease biomarkers at once. Tebbs and others (2013, Two-stage hierarchical group testing for multiple infections with application to the Infertility Prevention Project. Biometrics 69, 1064–1073) and Hou and others (2017, Hierarchical group testing for multiple infections. Biometrics 73, 656–665) were the first to examine hierarchical group testing case identification procedures for multiple diseases. In this article, we propose new non-hierarchical procedures which utilize two-dimensional arrays. We derive closed-form expressions for the expected number of tests per individual and classification accuracy probabilities and show that array testing can be more efficient than hierarchical procedures when screening individuals for multiple diseases at once. We illustrate the potential of using array testing in the detection of chlamydia and gonorrhea for a statewide screening program in Iowa. Finally, we describe an R/Shiny application that will help practitioners identify the best multiple-disease case identification algorithm.
KW - Case identification
KW - Group testing
KW - Infertility prevention project
KW - Matrix pooling
KW - Pooled testing
KW - Screening
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U2 - 10.1093/BIOSTATISTICS/KXY058
DO - 10.1093/BIOSTATISTICS/KXY058
M3 - Article
C2 - 30371749
AN - SCOPUS:85086847420
SN - 1465-4644
VL - 21
SP - 417
EP - 431
JO - Biostatistics
JF - Biostatistics
IS - 3
ER -