The graphical structure of respondent-driven sampling

Abstract: Respondent-driven sampling (RDS) is a chain-referral method for sampling members of a hidden or hard-to-reach population such as sex workers, homeless people, or drug users via their social network. Most methodological work on RDS has focused on inference of population means under the assumption that subjects' network degree determines their probability of being sampled. Criticism of existing estimators is usually focused on missing data: the underlying network is only partially observed, so it is difficult to determine correct sampling probabilities. In this paper, we show that data collected in ordinary RDS studies contain information about the structure of the respondents' social network. We construct a continuous-time model of RDS recruitment that incorporates the time series of recruitment events, the pattern of coupon use, and the network degrees of sampled subjects. Together, the observed data and the recruitment model place a well-defined probability distribution on the recruitment-induced subgraph of respondents. We show that this distribution can be interpreted as an exponential random graph model and develop a computationally efficient method for estimating the hidden graph. We validate the method using simulated data and apply the technique to an RDS study of injection drug users in St. Petersburg, Russia.