Nested Group Testing Procedure

Abstract

We investigated the false-negative, true-negative, false-positive, and true-positive predictive values from a general group testing procedure for a heterogeneous population. We show that its false (true)-negative predictive value of a specimen is larger (smaller), and the false (true)-positive predictive value is smaller (larger) than that from individual testing procedure, where the former is in aversion. Then we propose a nested group testing procedure, and show that it can keep the sterling characteristics and also improve the false-negative predictive values for a specimen, not larger than that from individual testing. These characteristics are studied from both theoretical and numerical points of view. The nested group testing procedure is better than individual testing on both false-positive and false-negative predictive values, while retains the efficiency as a basic characteristic of a group testing procedure. Applications to Dorfman’s, Halving and Sterrett procedures are discussed. Results from extensive simulation studies and an application to malaria infection in microscopy-negative Malawian women exemplify the findings.

Nonparametric Additive Regression for High-Dimensional Group Testing Data

“…In group testing, the true status of an individual is not directly observable; instead, tests are performed at the group level. Moreover, for groups flagged as high-risk due to positive results, additional tests, including individual tests, may be conducted, as individuals within these groups are more likely to be infected [34]. A typical group testing algorithm usually contains two steps.…”