Inverse sampling regression for pooled data

Osval A. Montesinos-López, Abelardo Montesinos-López, Kent Eskridge, José Crossa

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Because pools are tested instead of individuals in group testing, this technique is helpful for estimating prevalence in a population or for classifying a large number of individuals into two groups at a low cost. For this reason, group testing is a well-known means of saving costs and producing precise estimates. In this paper, we developed a mixed-effect group testing regression that is useful when the data-collecting process is performed using inverse sampling. This model allows including covariate information at the individual level to incorporate heterogeneity among individuals and identify which covariates are associated with positive individuals. We present an approach to fit this model using maximum likelihood and we performed a simulation study to evaluate the quality of the estimates. Based on the simulation study, we found that the proposed regression method for inverse sampling with group testing produces parameter estimates with low bias when the pre-specified number of positive pools (r) to stop the sampling process is at least 10 and the number of clusters in the sample is also at least 10. We performed an application with real data and we provide an NLMIXED code that researchers can use to implement this method.

Original languageEnglish (US)
Pages (from-to)1093-1109
Number of pages17
JournalStatistical Methods in Medical Research
Issue number3
StatePublished - Jun 1 2017


  • Group testing
  • classification
  • inverse sampling
  • precision
  • prevalence

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management


Dive into the research topics of 'Inverse sampling regression for pooled data'. Together they form a unique fingerprint.

Cite this