title: Bayesian regression for group testing data.
abstract: Group testing involves pooling individual specimens (e.g., blood, urine, swabs, etc.) and testing the pools for the presence of a disease. When individual covariate information is available (e.g., age, gender, number of sexual partners, etc.), a common goal is to relate an individual's true disease status to the covariates in a regression model. Estimating this relationship is a nonstandard problem in group testing because true individual statuses are not observed and all testing responses (on pools and on individuals) are subject to misclassification arising from assay error. Previous regression methods for group testing data can be inefficient because they are restricted to using only initial pool responses and/or they make potentially unrealistic assumptions regarding the assay accuracy probabilities. To overcome these limitations, we propose a general Bayesian regression framework for modeling group testing data. The novelty of our approach is that it can be easily implemented with data from any group testing protocol. Furthermore, our approach will simultaneously estimate assay accuracy probabilities (along with the covariate effects) and can even be applied in screening situations where multiple assays are used. We apply our methods to group testing data collected in Iowa as part of statewide screening efforts for chlamydia, and we make user-friendly R code available to practitioners.
authors.affiliation: Department of Mathematical Sciences, Clemson University, Clemson, South Carolina 29634, U.S.A.
authors.forename: Christopher S
authors.initials: CS
authors.lastname: McMahan
authors.affiliation: Department of Statistics, University of South Carolina, Columbia, South Carolina 29208, U.S.A.
authors.forename: Joshua M
authors.identifier: 0000-0002-6762-7241
authors.initials: JM
authors.lastname: Tebbs
authors.affiliation: Department of Statistics, University of South Carolina, Columbia, South Carolina 29208, U.S.A.
authors.forename: Timothy E
authors.initials: TE
authors.lastname: Hanson
authors.affiliation: Department of Statistics, University of Nebraska-Lincoln, Lincoln, Nebraska 68583, U.S.A.
authors.forename: Christopher R
authors.initials: CR
authors.lastname: Bilder
mesh_terms: D001499:Bayes Theorem; D002690:Chlamydia Infections / Q000175:diagnosis; D006801:Humans; D007484:Iowa; D008403:Mass Screening / Q000706:statistics & numerical data*; D012044:Regression Analysis
keywords: Binary regression; Latent response; Pooled testing; Specimen pooling