Group testing regression models with fixed and random effects

Peng Chen, Joshua M. Tebbs, Christopher R. Bilder

Research output: Contribution to journalArticle

40 Scopus citations

Abstract

Group testing, where subjects are tested in pools rather than individually, has a long history of successful application in infectious disease screening. In this article, we develop group testing regression models to include covariate effects that are best regarded as random. We present approaches to fit mixed effects models using maximum likelihood, investigate likelihood ratio and score tests for variance components, and evaluate small sample performance using simulation. We illustrate our methods using chlamydia and gonorrhea data collected by the state of Nebraska as part of the Infertility Prevention Project.

Original languageEnglish (US)
Pages (from-to)1270-1278
Number of pages9
JournalBiometrics
Volume65
Issue number4
DOIs
StatePublished - Dec 2009

Keywords

  • Generalized linear mixed model
  • Latent binary response
  • Likelihood ratio test
  • Monte Carlo EM algorithm
  • Pooled testing
  • Score test

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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