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.
Highlights
Respondent-driven sampling (RDS) is a network-based sampling technique that leverages social relationships to recruit individuals of hard-to-reach populations into research studies.[1]
Our results show that ignoring homophily-driven effects, if present, induces a negligible to small bias for linear and Poisson models while, for logistic regression, this strategy induces a substantial bias in the estimates when clustering is assumed at the seed level, and less bias but increased variability when clustering is assumed at the recruiter level
Fitting mixed models in which clustering is assumed at the recruiter level yields estimators with less bias than models in which clustering is assumed at the seed level
Summary
Respondent-driven sampling (RDS) is a network-based sampling technique that leverages social relationships to recruit individuals of hard-to-reach populations into research studies.[1] The RDS process, which proceeds through recruitment waves, starts with the selection of initial seed participants who, after being interviewed, receive a fixed number of coupons to distribute among their peers. Through many waves of recruitment, the process samples farther from the initial recruits, which should ensure greater representativeness and generalizability of the sample. This is because seeds typically represent a convenience sample, even if thoughtfully chosen with the view to optimizing representation of their social spheres. RDS reduces the privacy concerns that are associated with the identification of participants’ social networks or the community population that could occur in a more traditional study that would aim to enumerate the members of the target population by relying on members to recruit their peers into the study
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