Trevor Hart | Searcharxiv

arXiv2020

General Regression Methods for Respondent-Driven Sampling Data

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.

stat.ME↗

arXiv2020

Sampling from Networks: Respondent-Driven Sampling

Respondent-Driven Sampling (RDS) is a variant of link-tracing, a sampling technique for surveying hard-to-reach communities that takes advantage of community members' social networks to reach potential participants. As a network-based sampling method, RDS is faced with the fundamental problem of sampling from population networks where features such as homophily (the tendency for individuals with similar traits to share social ties) and differential activity (the ratio of the average number of connections by attribute) are sensitive to the choice of a sampling method. Though not clearly described in the RDS literature, many simple methods exist to generate simulated RDS data, with specific levels of network features, where the focus is on estimating simple estimands. However, the accuracy of these methods in their abilities to consistently recover those targeted network features remains unclear. This is also motivated by recent findings that some population network parameters (e.g.~homophily) cannot be consistently estimated from the RDS data alone \citep{Crawford17}. In this paper, we conduct a simulation study to assess the accuracy of existing RDS simulation methods, in terms of their abilities to generate RDS samples with the desired levels of two network parameters: homophily and differential activity. The results show that (1) homophily cannot be consistently estimated from simulated RDS samples and (2) differential activity estimates are more precise when groups, defined by traits, are equally active and equally represented in the population. We use this approach to mimic features of the Engage Study, an RDS sample of gay, bisexual and other men who have sex with men in Montreal.

stat.AP↗