Misclassified group-tested current status data


    loading  Checking for direct PDF access through Ovid

Abstract

SummaryGroup testing, introduced by Dorfman (1943), has been used to reduce costs when estimating the prevalence of a binary characteristic based on a screening test of Symbol groups that include Symbol independent individuals in total. If the unknown prevalence is low and the screening test suffers from misclassification, it is also possible to obtain more precise prevalence estimates than those obtained from testing all Symbol samples separately (Tu et al., 1994). In some applications, the individual binary response corresponds to whether an underlying time-to-event variable Symbol is less than an observed screening time Symbol, a data structure known as current status data. Given sufficient variation in the observed Symbol values, it is possible to estimate the distribution function Symbol of Symbol nonparametrically, at least at some points in its support, using the pool-adjacent-violators algorithm (Ayer et al., 1955). Here, we consider nonparametric estimation of Symbol based on group-tested current status data for groups of size Symbol where the group tests positive if and only if any individual's unobserved Symbol is less than the corresponding observed Symbol. We investigate the performance of the group-based estimator as compared to the individual test nonparametric maximum likelihood estimator, and show that the former can be more precise in the presence of misclassification for low values of Symbol. Potential applications include testing for the presence of various diseases in pooled samples where interest focuses on the age-at-incidence distribution rather than overall prevalence. We apply this estimator to the age-at-incidence curve for hepatitis C infection in a sample of U.S. women who gave birth to a child in 2014, where group assignment is done at random and based on maternal age. We discuss connections to other work in the literature, as well as potential extensions.

    loading  Loading Related Articles