Volume 75, Issue 1
BIOMETRIC METHODOLOGY

Informative group testing for multiplex assays

Christopher R. Bilder

Corresponding Author

E-mail address: chris@chrisbilder.com

Department of Statistics, University of Nebraska‐Lincoln, Lincoln, Nebraska 68583

Correspondence

Christopher R. Bilder, Department of Statistics, University of Nebraska‐Lincoln, Lincoln, NE 68583

Email: chris@chrisbilder.com

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Joshua M. Tebbs

Department of Statistics, University of South Carolina, Columbia, South Carolina 29208

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Christopher S. McMahan

School of Mathematical and Statistical Sciences, Clemson University, Clemson, South Carolina 29634

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First published: 24 October 2018
Citations: 2

Abstract

Infectious disease testing frequently takes advantage of two tools—group testing and multiplex assays—to make testing timely and cost effective. Until the work of Tebbs et al. (2013) and Hou et al. (2017), there was no research available to understand how best to apply these tools simultaneously. This recent work focused on applications where each individual is considered to be identical in terms of the probability of disease. However, risk‐factor information, such as past behavior and presence of symptoms, is very often available on each individual to allow one to estimate individual‐specific probabilities. The purpose of our paper is to propose the first group testing algorithms for multiplex assays that take advantage of individual risk‐factor information as expressed by these probabilities. We show that our methods significantly reduce the number of tests required while preserving accuracy. Throughout this paper, we focus on applying our methods with the Aptima Combo 2 Assay that is used worldwide for chlamydia and gonorrhea screening.

Number of times cited according to CrossRef: 2

  • Nonparametric estimation of distributions and diagnostic accuracy based on group‐tested results with differential misclassification, Biometrics, 10.1111/biom.13236, 0, 0, (2020).
  • The objective function controversy for group testing: Much ado about nothing?, Statistics in Medicine, 10.1002/sim.8341, 38, 24, (4912-4923), (2019).

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