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Biometrics. 2012 Sep;68(3):793-804. doi: 10.1111/j.1541-0420.2011.01726.x. Epub 2011 Dec 29.

Two-dimensional informative array testing.

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1
Department of Statistics, University of South Carolina, Columbia, South Carolina 29208, USA.

Abstract

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.

PMID:
22212007
PMCID:
PMC4371870
DOI:
10.1111/j.1541-0420.2011.01726.x
[Indexed for MEDLINE]
Free PMC Article
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