Semiparametric group testing regression models

• 成果类型: Article
• 署名作者: Wang, D.; McMahan, C. S.; Gallagher, C. M.; Kulasekera, K. B.
• 署名单位: Clemson University; University of Louisville
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu007
• 发表日期: 2014
• 页码: 587598
• 关键词: Nonparametric regression; case identification; EFFICIENCY; estimator

摘要

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 the statistical literature involves the development of regression models that relate individual-level covariates to testing responses observed from pooled specimens. In this article, we propose a general semiparametric framework that allows for the inclusion of multi-dimensional covariates, decoding information, and imperfect testing. The asymptotic properties of our estimators are presented and guidance on finite sample implementation is provided. We illustrate the performance of our methods through simulation and by applying them to chlamydia and gonorrhea data collected by the Nebraska Public Health Laboratory as a part of the Infertility Prevention Project.