Journal article
Nonparametric and Parametric Estimators of Prevalence From Group Testing Data With Aggregated Covariates
A Delaigle, WX Zhou
Journal of the American Statistical Association | AMER STATISTICAL ASSOC | Published : 2015
Abstract
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 techniqu..
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Funding Acknowledgements
Aurore Delaigle, School of Mathematics and Statistics, University of Melbourne, VIC, 3010, Australia, and Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers of Big Data, Big Models, New Insights, Parkville, VIC, 3010, Australia (E-mail: A. Delaigle@ms.unimelb.edu.au). Wen-Xin Zhou, School of Mathematics and Statistics, University of Melbourne, VIC, 3010, Australia, and Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08544 (E-mail: wenxinz@princeton.edu). This research was supported by a grant and a fellowship from the Australian Research Council. The NHANES data are available from the NHANES website of the Centers for Disease Control and Prevention, National Center for Health Statistics, Hyattsville, MD: http://wwwn.cdc.gov/nchs/nhanes/search/nhanes99_00.aspx.