title: Evaluation of respondent-driven sampling.
abstract: 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.
authors.affiliation: Department of Infectious Disease Epidemiology, Faculty of Epidemiology & Population Health, London School of Hygiene and Tropical Medicine, UK.
authors.forename: Nicky
authors.initials: N
authors.lastname: McCreesh
authors.forename: Simon D W
authors.initials: SD
authors.lastname: Frost
authors.forename: Janet
authors.initials: J
authors.lastname: Seeley
authors.forename: Joseph
authors.initials: J
authors.lastname: Katongole
authors.forename: Matilda N
authors.initials: MN
authors.lastname: Tarsh
authors.forename: Richard
authors.initials: R
authors.lastname: Ndunguse
authors.forename: Fatima
authors.initials: F
authors.lastname: Jichi
authors.forename: Natasha L
authors.initials: NL
authors.lastname: Lunel
authors.forename: Dermot
authors.initials: D
authors.lastname: Maher
authors.forename: Lisa G
authors.initials: LG
authors.lastname: Johnston
authors.forename: Pam
authors.initials: P
authors.lastname: Sonnenberg
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Copas
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Hayes
authors.forename: Richard G
authors.initials: RG
authors.lastname: White
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D015982:Bias; D002648:Child; D002675:Child, Preschool; D015658:HIV Infections / Q000453:epidemiology; D006801:Humans; D007223:Infant; D008297:Male; D008875:Middle Aged; D018579:Patient Selection; D012494:Sampling Studies; D012959:Socioeconomic Factors; D014454:Uganda / Q000453:epidemiology; D055815:Young Adult