【摘要】 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 infection.

【报告人简介】Dr. Tebbs is Professor and Chair in the Department of Statistics at University of South Carolina. He obtained his BS in Mathematics and MS in Statistics from University of Iowa and his PhD in Statistics from North Carolina State University. His primary research interests are in the development of statistical methods for categorical data, especially aggregated or group tested data and their application in infectious disease screening, as well as in general biostatistical methods and problems involving ordering or shape restrictions. He is an elected member of the International Statistical Institute, a fellow of the American Statistical Association, and his research program is funded by the National Institutes of Health (NIH). He is currently the editor of American Statistician and is an associate editor at Statistics in Medicine. He is a former member of the Biostatistical Methods and Research Design (BMRD) Study Section of the NIH.