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Diagnostics for Respondent-driven Sampling.

Krista J Gile1, Lisa G Johnston2, Matthew J Salganik3

  • 1University of Massachusetts, Amherst, MA, USA.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)
|May 27, 2016
PubMed
Summary
This summary is machine-generated.

Respondent-driven sampling (RDS) is a practical method for reaching populations at high risk for HIV. New diagnostic tools help researchers validate RDS data assumptions and improve statistical analysis for these hard-to-reach groups.

Keywords:
HIV/AIDSdiagnosticsexploratory data analysishard-to-reach populationslink-tracing samplingnon-ignorable designrespondent-driven samplingsocial networkssurvey sampling

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Social Sciences

Background:

  • Respondent-driven sampling (RDS) is a key method for sampling hard-to-reach populations, particularly those at elevated risk for HIV.
  • Data collection via peer-referral in social networks is a core component of RDS.
  • While practical, RDS inference relies on strong assumptions due to partially uncontrolled and unobserved sampling designs.

Purpose of the Study:

  • To introduce and evaluate diagnostic tools for validating assumptions in Respondent-driven sampling (RDS).
  • To enhance the reliability of statistical inference from RDS data.
  • To foster further statistical research in the application of RDS.

Main Methods:

  • Development of novel diagnostic tools to assess RDS assumptions.
  • Application of these diagnostics across 12 distinct high-risk populations.
  • Empirical evaluation of the utility of the diagnostic tools.

Main Results:

  • The introduced diagnostic tools provide a means to scrutinize critical assumptions in RDS.
  • Application in 12 high-risk populations demonstrated the practical utility of these diagnostics.
  • The diagnostics empower researchers with a better understanding of their RDS data.

Conclusions:

  • Diagnostic tools are essential for robust inference from Respondent-driven sampling data.
  • These tools improve the transparency and validity of research involving hard-to-reach populations.
  • Further statistical research on RDS is encouraged by these advancements.