  Sampling hidden populations is particularly challenging using standard
sampling methods mainly because of the lack of a sampling frame.
Respondent-driven sampling (RDS) is an alternative methodology that exploits
the social contacts between peers to reach and weight individuals in these
hard-to-reach populations. It is a snowball sampling procedure where the weight
of the respondents is adjusted for the likelihood of being sampled due to
differences in the number of contacts. In RDS, the structure of the social
contacts thus defines the sampling process and affects its coverage, for
instance by constraining the sampling within a sub-region of the network. In
this paper we study the bias induced by network structures such as social
triangles, community structure, and heterogeneities in the number of contacts,
in the recruitment trees and in the RDS estimator. We simulate different
scenarios of network structures and response-rates to study the potential
biases one may expect in real settings. We find that the prevalence of the
estimated variable is associated with the size of the network community to
which the individual belongs. Furthermore, we observe that low-degree nodes may
be under-sampled in certain situations if the sample and the network are of
similar size. Finally, we also show that low response-rates lead to reasonably
accurate average estimates of the prevalence but generate relatively large
biases.
