Array testing for multiplex assays

Peijie Hou, Joshua M. Tebbs, Dewei Wang, Christopher S. McMahan, Christopher R. Bilder

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)417-431
Number of pages15
Issue number3
StatePublished - 2020


  • Case identification
  • Group testing
  • Infertility prevention project
  • Matrix pooling
  • Pooled testing
  • Screening

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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