Two-Dimensional Informative Array Testing

Christopher S. McMahan, Joshua M. Tebbs, Christopher R. Bilder

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


Array-based group-testing algorithms for case identification are widely used in infectious disease testing, drug discovery, and genetics. In this article, we generalize previous statistical work in array testing to account for heterogeneity among individuals being tested. We first derive closed-form expressions for the expected number of tests (efficiency) and misclassification probabilities (sensitivity, specificity, predictive values) for two-dimensional array testing in a heterogeneous population. We then propose two "informative" array construction techniques which exploit population heterogeneity in ways that can substantially improve testing efficiency when compared to classical approaches that regard the population as homogeneous. Furthermore, a useful byproduct of our methodology is that misclassification probabilities can be estimated on a per-individual basis. We illustrate our new procedures using chlamydia and gonorrhea testing data collected in Nebraska as part of the Infertility Prevention Project.

Original languageEnglish (US)
Pages (from-to)793-804
Number of pages12
Issue number3
StatePublished - Sep 2012


  • Disease screening
  • Efficiency
  • Group testing
  • Infertility Prevention Project
  • Matrix pooling
  • Pooled testing

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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