A New Semiparametric Framework for Modeling Group Testing Data

• Activity Number: 143
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Dewei Wang*+ and Karunarathna B. Kulasekera and Colin M. Gallagher and Christopher S. McMahan
• Companies: Clemson University and University of Louisville and Clemson University and Clemson University
• Keywords: Semiparametric binary regression ; Pooled data ; Prevalence ; Missing data ; Testing error ; Local polynomial estimator

Abstract

Group testing through the use of pooling has proven to be an efficient method of reducing the time and cost associated with screening for a binary characteristic of interest such as infection status. A topic of key interest in this area involves the development of regression models that relate the individual level covariates to the binary pool testing responses. The research in this area has primarily focused on parametric regression models. In this article, we propose a general semiparametric framework which can handle multi-dimensional covariates, imperfect testing, and missing covariates. The asymptotic properties of our estimators are also presented. We investigate the performance of our method by applying it to a hepatitis data set obtained from the National Health and Nutrition Examination Survey.