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A Bayesian model for estimating population means using a link-tracing sampling design.

Katherine St Clair1, Daniel O'Connell

  • 1Department of Mathematics, Carleton College, Northfield, Minnesota 55057, USA. kstclair@carleton.edu

Biometrics
|June 15, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced Bayesian model for link-tracing sampling, improving estimates for hidden populations. The model accurately estimates population and domain means for quantitative responses in hidden groups.

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Area of Science:

  • Statistics
  • Social Sciences
  • Epidemiology

Background:

  • Link-tracing sampling designs are effective for studying populations with "hidden" groups.
  • These designs leverage social links to increase sampling intensity within specific domains.
  • Existing Bayesian models (Chow and Thompson, 2003) estimate hidden population sizes but not quantitative responses.

Purpose of the Study:

  • To extend the Bayesian model for link-tracing designs to accommodate quantitative response variables.
  • To enable the modeling of quantitative outcomes within hidden and nonhidden populations.
  • To assess the performance of the proposed model extension.

Main Methods:

  • Developed an additive Bayesian model to the existing framework for link-tracing sampling.
  • Incorporated the modeling of a quantitative response variable into the Bayesian analysis.
  • Evaluated the model using both a constructed population and a real-world population of at-risk individuals.

Main Results:

  • The proposed model extension provides accurate point and interval estimates for population and domain means.
  • The model's performance was validated in populations with both hidden and nonhidden individuals.
  • Successful estimation of quantitative responses was demonstrated in both simulated and real-world data.

Conclusions:

  • The enhanced Bayesian model effectively models quantitative responses in hidden populations studied via link-tracing sampling.
  • The model offers reliable estimation of population and domain means under specified assumptions.
  • This approach advances the analysis of hidden populations in various research fields.