  Respondent-driven sampling is a widely-used network sampling technique,
designed to sample from hard-to-reach populations. Estimation from the
resulting samples is an area of active research, with software available to
compute at least four estimators of a population proportion. Each estimator is
claimed to address deficiencies in previous estimators, however those claims
are often unsubstantiated. In this study we provide a simulation-based
comparison of five existing estimators, focussing on sampling conditions which
a recent estimator is designed to address. We find no estimator consistently
out-performs all others, and highlight sampling conditions in which each is to
be preferred.
