Misclassified group-tested current status data
研究了基于群检测的当前状态数据中，非参数估计分布函数的方法，发现当误分类存在且患病率低时，群检测估计比个体检测更精确，并应用于丙型肝炎感染年龄分布估计。
Group 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 [Formula: see text] groups that include [Formula: see text] 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 [Formula: see text] samples separately (Tu et al., 1994). In some applications, the individual binary response corresponds to whether an underlying time-to-event variable [Formula: see text] is less than an observed screening time [Formula: see text], a data structure known as current status data. Given sufficient variation in the observed [Formula: see text] values, it is possible to estimate the distribution function [Formula: see text] of [Formula: see text] 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 [Formula: see text] based on group-tested current status data for groups of size [Formula: see text] where the group tests positive if and only if any individual's unobserved [Formula: see text] is less than the corresponding observed [Formula: see text]. 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 [Formula: see text]. 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.