An Exploration into Grouped Current Status Data

• Activity Number: 80
• Type: Contributed
• Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
• Sponsor: Biometrics Section
• Author(s): Lucia Petito* and Nicholas P. Jewell
• Companies: UC Berkeley and UC Berkeley
• Keywords: current status data ; expectation-maximization algorithm ; pool-adjacent-violators algorithm ; grouped testing

Abstract

Grouped testing has long been used as a method to reduce costs when estimating the prevalence of a binary characteristic based on a screening test of k groups that include n independent individuals in total. In some applications, the individual binary response corresponds to whether an underlying "time to incidence" variable T is less than an observed screening time C. This data structure at the individual level is known as current status data. Given sufficient variation in the observed Cs, it is possible to estimate the distribution function F of T non-parametrically using the pool-adjacent-violators algorithm (Ayer et al., 1955). Here, we consider similar nonparametric estimation of F based on group tested current status data for k groups where the group tests "positive" if and only if any individual unobserved T is less than its corresponding observed C. We consider potential cost savings and its relationship to precision over the support of F, and investigate the effect of misclassification of the pooled tests. We consider potential applications to testing for the presence of various diseases from pooled samples where interest focuses on the age at incidence distribution.