BACKGROUND AND OBJECTIVES Respondent-driven sampling (RDS) is widely used to sample hidden populations and RDS data are analyzed using specially designed RDS analysis tool (RDSAT). RDSAT estimates parameters such as proportions. Analysis with RDSAT requires separate weight assignment for individual variables even in a single individual; hence, regression analysis is a problem. RDS-analyst is an...

Respondent-driven sampling (RDS) is a recently introduced, and now widely used, technique for estimating disease prevalence in hidden populations. RDS data are collected through a snowball mechanism, in which current sample members recruit future sample members. In this paper we present RDS as Markov chain Monte Carlo importance sampling, and we examine the effects of community structure and th...

Respondent-Driven Sampling (RDS) is used throughout the world to estimate prevalences and population sizes for hard-to-reach populations. Although RDS is an effective method for enrolling people from key populations (KPs) in studies, it relies on an unknown sampling mechanism and thus each individual’s inclusion probability is unknown. Current estimators rely on a participant’s network size (de...

Respondent-driven sampling (RDS) is a link-tracing sampling method that is especially suitable for sampling hidden populations. RDS combines an efficient snowball-type sampling scheme with inferential procedures that yield unbiased population estimates under some assumptions about the sampling procedure and population structure. Several seed individuals are typically used to initiate RDS recrui...

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...