  Respondent-Driven Sampling (RDS) is a variant of link-tracing sampling
techniques that aim to recruit hard-to-reach populations by leveraging
individuals' social relationships. As such, an RDS 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 modeling strategies for RDS to
address homophilic covariates and the dependence between observations within
the network. In this work, we propose a methodology for general regression
techniques using RDS 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 an RDS study in Montreal, Canada.
