General regression methods for respondent-driven sampling data. (October 2021)

• Record Type: Journal Article
• Authors: Yauck, Mamadou; Moodie, Erica EM; Apelian, Herak; Fourmigue, Alain; Grace, Daniel; Hart, Trevor; Lambert, Gilles; Cox, Joseph
• Is Part Of: Statistical methods in medical research. Volume 30:Number 9(2021)
• Page Start: 2105
• Page End: 2118
• Publication Date: 2021-10
• Subjects: Design weights -- hidden population sampling -- homophily -- identifiation -- peer effects -- simultaneous autoregressive models -- social networks; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/09622802211032713
• Languages: English
• ISSNs: 0962-2802

Abstract

Respondent-driven sampling is a variant of link-tracing sampling techniques that aim to recruit hard-to-reach populations by leveraging individuals' social relationships. As such, a respondent-driven sample has a graphical component which represents a partially observed network of unknown structure. Moreover, it is common to observe homophily, or the tendency to form connections with individuals who share similar traits. Currently, there is a lack of principled guidance on multivariate modelling strategies for respondent-driven sampling to address peer effects driven by homophily and the dependence between observations within the network. In this work, we propose a methodology for general regression techniques using respondent-driven sampling data. This is used to study the socio-demographic predictors of HIV treatment optimism (about the value of antiretroviral therapy) among gay, bisexual and other men who have sex with men, recruited into a respondent-driven sampling study in Montreal, Canada.