From mixed effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data

Chase N. Joyner, Christopher S. McMahan, Joshua M. Tebbs, Christopher R. Bilder

Research output: Contribution to journalArticle

Abstract

Due to reductions in both time and cost, group testing is a popular alternative to individual-level testing for disease screening. These reductions are obtained by testing pooled biospecimens (eg, blood, urine, swabs, etc.) for the presence of an infectious agent. However, these reductions come at the expense of data complexity, making the task of conducting disease surveillance more tenuous when compared to using individual-level data. This is because an individual's disease status may be obscured by a group testing protocol and the effect of imperfect testing. Furthermore, unlike individual-level testing, a given participant could be involved in multiple testing outcomes and/or may never be tested individually. To circumvent these complexities and to incorporate all available information, we propose a Bayesian generalized linear mixed model that accommodates data arising from any group testing protocol, estimates unknown assay accuracy probabilities and accounts for potential heterogeneity in the covariate effects across population subgroups (eg, clinic sites, etc.); this latter feature is of key interest to practitioners tasked with conducting disease surveillance. To achieve model selection, our proposal uses spike and slab priors for both fixed and random effects. The methodology is illustrated through numerical studies and is applied to chlamydia surveillance data collected in Iowa.

Original languageEnglish (US)
Pages (from-to)913-923
Number of pages11
JournalBiometrics
Volume76
Issue number3
DOIs
StatePublished - Sep 1 2020

Keywords

  • binary regression
  • generalized linear mixed model
  • latent variable modeling
  • pooled testing
  • random effects
  • spike and slab prior

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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