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Array testing for multiplex assays.

Authors
  • Hou, Peijie1
  • Tebbs, Joshua M2
  • Wang, Dewei2
  • McMahan, Christopher S3
  • Bilder, Christopher R4
  • 1 Statistical and Quantitative Sciences, Takeda Pharmaceutical Inc., 300 Massachusetts Avenue, Cambridge, MA, USA.
  • 2 Department of Statistics, University of South Carolina, 1523 Greene St, Columbia, SC, USA.
  • 3 School of Mathematical and Statistical Sciences, Clemson University, O-110 Martin Hall, Clemson, SC, USA.
  • 4 Department of Statistics, University of Nebraska-Lincoln, 340 Hardin Hall North, Lincoln, NE, USA.
Type
Published Article
Journal
Biostatistics (Oxford, England)
Publication Date
Jul 01, 2020
Volume
21
Issue
3
Pages
417–431
Identifiers
DOI: 10.1093/biostatistics/kxy058
PMID: 30371749
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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. Biometrics69, 1064-1073) and Hou and others (2017, Hierarchical group testing for multiple infections. Biometrics73, 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. © The Authors 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected]

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