Group Testing with Missing Values

• Activity Number: 425 - Modern Statistical Learning of Complex Data
• Type: Invited
• Date/Time: Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Aurore Delaigle* and Ruoxu Tan
• Companies: University of Melbourne and University of Melbourne
• Keywords: cost-effective detection; limited resources; disease detection; pooling strategies; time saving; disease monitoring

Abstract

Estimating the prevalence of an infectious disease in a big population typically requires testing individuals for the disease using a specimen test. When a new disease such as covid-19 spreads quickly, testing each individual is often not possible because of time constraints and limited resources. The group testing procedure was introduced in the 1940's to handle such situations. Instead of testing all individuals for a disease, it tests the pooled specimens of groups of individuals. This approach permits to estimate fast and accurately a prevalence which is not too high. Often it is the prevalence conditional on important variables which is of interest, and techniques have been developed in the literature for estimating it from group testing data. However, these fail if the specimen is missing for some of the individuals, a situation which is often encountered in practice. We construct consistent nonparametric estimators of conditional prevalence for group testing data, designed for this case. We also consider the more difficult situation where the explanatory variable is also missing for some individuals, where we derive parametric procedures.