Estimating Hard-to-Reach Population Size Using Respondent-Driven Sampling Data

Mark S. Handcock, University of California, Los Angeles
Krista Gile, University of Massachusetts at Amherst
Corinne Mar, University of Washington

Respondent-Driven Sampling (RDS) is an approach to sampling design and inference in hard-to-reach human populations. Typically, a sampling frame is not available, and population members are difficult to identify or recruit from broader sampling frames. Common examples include unregulated workers and those most at risk for STI. RDS is often conducted in settings where the population size is unknown and of great independent interest. This paper presents an approach to estimating the size of a target population based on data collected through RDS. The proposed approach uses a Bayesian framework. We present the results of an extensive simulation study, show the approach also improves estimation of aggregate characteristics, and produces interval estimates with good frequentist properties. Finally, the method demonstrates sensible results when used to estimate the numbers of sub-populations most at risk for HIV in two cities in El Salvador.

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Presented in Session 11: Statistical, Spatial and Network Methods