Regression analysis for multiple-disease group testing data

Boan Zhang, Christopher R. Bilder, Joshua M. Tebbs

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Group testing, where individual specimens are composited into groups to test for the presence of a disease (or other binary characteristic), is a procedure commonly used to reduce the costs of screening a large number of individuals. Group testing data are unique in that only group responses may be available, but inferences are needed at the individual level. A further methodological challenge arises when individuals are tested in groups for multiple diseases simultaneously, because unobserved individual disease statuses are likely correlated. In this paper, we propose new regression techniques for multiple-disease group testing data. We develop an expectation-solution based algorithm that provides consistent parameter estimates and natural large-sample inference procedures. We apply our proposed methodology to chlamydia and gonorrhea screening data collected in Nebraska as part of the Infertility Prevention Project and to prenatal infectious disease screening data from Kenya.

Original languageEnglish (US)
Pages (from-to)4954-4966
Number of pages13
JournalStatistics in Medicine
Volume32
Issue number28
DOIs
StatePublished - Dec 10 2013

Keywords

  • Correlated binary data
  • Expectation-solution algorithm
  • Generalized estimating equations
  • Infertility Prevention Project
  • Pooled testing
  • Specimen pooling

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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