Group testing regression model estimation when case identification is a goal

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

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Group testing is frequently used to reduce the costs of screening a large number of individuals for infectious diseases or other binary characteristics in small prevalence situations. In many applications, the goals include both identifying individuals as positive or negative and estimating the probability of positivity. The identification aspect leads to additional tests being performed, known as "retests", beyond those performed for initial groups of individuals. In this paper, we investigate how regression models can be fit to estimate the probability of positivity while also incorporating the extra information from these retests. We present simulation evidence showing that significant gains in efficiency occur by incorporating retesting information, and we further examine which testing protocols are the most efficient to use. Our investigations also demonstrate that some group testing protocols can actually lead to more efficient estimates than individual testing when diagnostic tests are imperfect. The proposed methods are applied retrospectively to chlamydia screening data from the Infertility Prevention Project. We demonstrate that significant cost savings could occur through the use of particular group testing protocols.

Original languageEnglish (US)
Pages (from-to)173-189
Number of pages17
JournalBiometrical Journal
Issue number2
StatePublished - Mar 2013


  • Binary response
  • EM algorithm
  • Generalized linear model
  • Latent response
  • Pooled testing
  • Prevalence estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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