Extending respondent-driven sampling to allow modeling of social networks with application to people who inject drugs

Music City Center 

Respondent-driven Sampling (RDS) is often used to sample hard-to-reach human populations, especially those at risk for transmissible disease such as HIV and HCV. RDS is conducted by collecting samples over the social network, leaving a tantalizing trace of the social network in the dataset, and begging the question of whether this incidental network information can be used to make inference about the underlying social network that might relate to the transmission of infection. A key limitation of this pursuit is that the RDS network information is structurally limited to tree-structured data – there are no cross-ties and no way to infer endogenous clustering, a key component of disease transmission. In this study we introduce the augmentation of RDS data with the distribution of tokens to provide a sample of cross-ties and introduce a method to use these data to make inference to the underlying social network.