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Evaluation of Respondent-driven Sampling

Bibliographic Data

ID23324899
AuthorsNicky McCreesh (0000-0003-1409-8531, London School of Hygiene & Tropical Medicine), Simon D W Frost (0000-0002-5207-9879, University of Cambridge), Janet Seeley (0000-0002-0583-5272, University of East Anglia), Joseph Katongole (0009-0009-0734-8804, Uganda Virus Research Institute), Matilda Nadagire Tarsh (Uganda Virus Research Institute), Richard Ndunguse (Uganda Virus Research Institute), Fatima Jichi, Natasha L Tilston‐Lunel (0000-0002-5290-9350, London School of Hygiene & Tropical Medicine), Natasha L Lunel, Dermot Maher (0000-0003-0466-1976, Uganda Virus Research Institute), Lisa G Johnston (0000-0002-3828-3638, Tulane University), Pam Sonnenberg (0000-0002-1067-1583, UCL Australia), Andrew Copas (0000-0001-8968-5963, UCL Australia), Andrew J Copas, Renée Hayes (0000-0002-1729-9892, London School of Hygiene & Tropical Medicine), Richard J Hayes, Richard G White (0000-0003-4410-6635, London School of Hygiene & Tropical Medicine, corresponding author)
Year2012
Volume23
Issue1
Pages138-147
Publication date2012-01-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueEpidemiology (JOURNAL)
Journal identifiersISSN: 1044-3983 • E-ISSN: 1531-5487
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/ede.0b013e31823ac17c
PMID22157309
PMCIDPMC3277908
OpenAlexW2123338297
LanguageEN
Citations received91
References cited25

BACKGROUND: Respondent-driven sampling is a novel variant of link-tracing sampling for estimating the characteristics of hard-to-reach groups, such as HIV prevalence in sex workers. Despite its use by leading health organizations, the performance of this method in realistic situations is still largely unknown. We evaluated respondent-driven sampling by comparing estimates from a respondent-driven sampling survey with total population data. METHODS: Total population data on age, tribe, religion, socioeconomic status, sexual activity, and HIV status were available on a population of 2402 male household heads from an open cohort in rural Uganda. A respondent-driven sampling (RDS) survey was carried out in this population, using current methods of sampling (RDS sample) and statistical inference (RDS estimates). Analyses were carried out for the full RDS sample and then repeated for the first 250 recruits (small sample). RESULTS: We recruited 927 household heads. Full and small RDS samples were largely representative of the total population, but both samples underrepresented men who were younger, of higher socioeconomic status, and with unknown sexual activity and HIV status. Respondent-driven sampling statistical inference methods failed to reduce these biases. Only 31%-37% (depending on method and sample size) of RDS estimates were closer to the true population proportions than the RDS sample proportions. Only 50%-74% of respondent-driven sampling bootstrap 95% confidence intervals included the population proportion. CONCLUSIONS: Respondent-driven sampling produced a generally representative sample of this well-connected nonhidden population. However, current respondent-driven sampling inference methods failed to reduce bias when it occurred. Whether the data required to remove bias and measure precision can be collected in a respondent-driven sampling survey is unresolved. Respondent-driven sampling should be regarded as a (potentially superior) form of convenience sampling method, and caution is required when interpreting findings based on the sampling method.

Population · Respondent · Sample (material) · Sampling (signal processing) · Socioeconomic status · Statistics · Stratified sampling · Survey sampling · Adolescent Sexual and Reproductive Health · Computer Science · Demography · HIV, Drug Use, Sexual Risk · HIV/AIDS Research and Interventions · Mathematics

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Citation span2012 - 2026 (15)
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