Inferring Bivariate Association from Respondent-driven Sampling Data. Issue 2 (8th March 2021) - Kim, Dongah; Gile, Krista J.; Guarino, Honoria; Mateu-Gelabert, Pedro
Abstract: Respondent-driven sampling (RDS) is an effective method of collecting data from many hard-to-reach populations. Valid statistical inference for these data relies on many strong assumptions. In standard samples, we assume observations from pairs of individuals are independent. In RDS, this assumption is violated by the sampling dependence between individuals. We propose a method to semi-parametrically estimate the null distributions of standard test statistics in the presence of sampling dependence, allowing for more valid statistical testing for dependence between pairs of variables within the sample. We apply our method to study characteristics of young adult illicit opioid users in New York City.