Estimating the size of populations at high risk for HIV using respondent-driven sampling data

Estimating the size of populations at high risk for HIV using respondent-driven sampling data.

The study of hard-to-reach populations presents significant challenges. Typically, a sampling frame is not available, and population members are difficult to identify or recruit from broader sampling frames. This is especially true of populations at high risk for HIV/AIDS. Respondent-driven sampling (RDS) is often used in such settings with the primary goal of estimating the prevalence of infec...

Estimating hidden population size using Respondent-Driven Sampling data.

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 s...

Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling

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Respondent-Driven Sampling of Heterosexuals at Increased Risk of HIV Infection.

Diagnostics for Respondent-driven Sampling.

Respondent-driven sampling (RDS) is a widely used method for sampling from hard-to-reach human populations, especially populations at higher risk for HIV. Data are collected through peer-referral over social networks. RDS has proven practical for data collection in many difficult settings and is widely used. Inference from RDS data requires many strong assumptions because the sampling design is...