Estimating hidden population size using Respondent-Driven Sampling data
Year: 2014
Type: article
Abstract: Respondent-Driven Sampling (RDS) is n approach to sampling design and inference in hard-to-reach human populations. It is often used in situations where the target population is rare and/or stigmatized in the larger population, so that it is prohibitively expensive to contact them through the available frames. Common examples include injecting drug users, men who have sex with men, and female sex workers. Most analysis of RDS data has focused on estimating aggregate characteristics, such as disease prevalence. However, 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 successive sampling approximation to RDS to leverage information in the ordered sequence of observed personal network sizes. The inference uses the Bayesian framework, allowing for the incorporation of prior knowledge. A flexible class of priors for the population size is used that aids elicitation. An extensive simulation study provides insight into the performance of the method for estimating population size under a broad range of conditions. A further study shows the approach also improves estimation of aggregate characteristics. Finally, the method demonstrates sensible results when used to estimate the size of known networked populations from the National Longitudinal Study of Adolescent Health, and when used to estimate the size of a hard-to-reach population at high risk for HIV.
Source: Electronic Journal of Statistics
DOI: 10.1214/14-ejs923
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $675
Funders: National Science Foundation, University of Washington, National Agricultural Statistics Service, Office of Naval Research, National Institute of Child Health and Human Development, California Center for Population Research, University of California, Los Angeles, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: G2507980080, G2998923865, G4149154236, G4154686922, Innovations in Network Modeling for HIV Prevention Studies, The National Longitudinal Study of Adolescent to Adult Health, G425736891, Scalable Model-Based Inference for Social Networks from Complex Sampling Designs, Diagnosis and Reduction of Bias in Respondent-driven Sampling, California Center for Population Research (CCPR) at UCLA, Statistical Methods for Respondent Driven Sampling Data, G6904400553, Center for Studies in Demography and Ecology, G7810591162, G8876996369, Valid Inference for Respondent Driven Sampling of Hidden Networked Populations
OpenAlex ID: W2061521986