Nonparametric and Parametric Estimators of Prevalence From Group Testing Data With Aggregated Covariates

• 成果类型: Article
• 署名作者: Delaigle, Aurore; Zhou, Wen-Xin
• 署名单位: University of Melbourne; Princeton University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1054491
• 发表日期: 2015
• 页码: 1785-1796
• 关键词: in-variables problem; regression-models; pooled assessments; samples; probability; populations; PROPORTION; biomarkers; EFFICIENCY; disease

摘要

Group testing is a technique employed in large screening studies involving infectious disease, where individuals in the study are grouped before being observed. Parametric and nonparametric estimators of conditional prevalence have been developed in the group testing literature, in the case where the binary variable indicating the disease status is available only for the group, but the explanatory variable is observed for each individual. However, for reasons such as the high cost of assays, the confidentiality of the patients, or the impossibility of measuring a concentration under a detection limit, the explanatory variable is observable only in an aggregated form and the existing techniques are no longer valid. We develop consistent parametric and nonparametric estimators of the conditional prevalence in this complex problem. We establish theoretical properties of our estimators and illustrate their practical performance on simulated and real data. We extend our techniques to the case where the group status is measured imperfectly, and to the setting where the covariate is aggregated and the individual status is available. Supplementary materials for this article are available online.