Inferring Bivariate Association from Respondent-driven Sampling Data. Issue 2 (8th March 2021)

• Record Type: Journal Article
• Authors: Kim, Dongah; Gile, Krista J.; Guarino, Honoria; Mateu-Gelabert, Pedro
• Is Part Of: Journal of the Royal Statistical Society. Volume 70:Issue 2(2021)
• Page Start: 415
• Page End: 433
• Publication Date: 2021-03-08
• Subjects: bivariate association -- homophily -- network analysis -- randomization test -- respondent-driven sampling -- semi-parametric; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12465
• Languages: English
• ISSNs: 0035-9254

Abstract

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.