Bayesian Regression for Group Testing Data

Dr. Joshua Tebbs, University of South Carolina
Monday, November 16, 2020 - 4:00am to 5:00pm
Online via Zoom

Abstract: Group testing involves pooling individual specimens and testing the pools for the presence of a disease. When individual covariate information is available, 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 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 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.