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Inferring bivariate associations with continuous data from studies using respondent-driven sampling.

Samantha Malatesta1, Karen R Jacobson2, Tara Carney3,4

  • 1Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.

Journal of the Royal Statistical Society. Series C, Applied Statistics
|March 17, 2025
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Summary
This summary is machine-generated.

This study introduces a new statistical test for analyzing relationships between variables in hidden populations sampled using respondent-driven sampling (RDS). The method addresses inflated Type 1 errors common with conventional statistics in RDS data.

Keywords:
bivariate associationcontinuous datahomophilynetworkrandomization testrespondent-driven sampling

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Social Sciences

Background:

  • Respondent-driven sampling (RDS) is crucial for studying hidden populations.
  • Inference on variable relationships in RDS data is statistically underdeveloped.
  • Homophily in RDS link-tracing designs can inflate Type 1 errors in conventional analyses.

Purpose of the Study:

  • To extend semiparametric randomization tests for analyzing associations between two variables in RDS data.
  • To accommodate situations where one or both variables are continuous.
  • To provide a robust statistical method for epidemiological research on hidden populations.

Main Methods:

  • Development of a semiparametric randomization test for bivariate associations.
  • Extension of the test to include continuous variables.
  • Application to tuberculosis epidemiology data from people who smoke illicit drugs in South Africa.

Main Results:

  • The proposed semiparametric randomization test offers a statistically sound approach for RDS data.
  • The method corrects for the dependence induced by link-tracing and homophily.
  • The analysis of tuberculosis epidemiology in illicit drug users in South Africa demonstrates the method's utility.

Conclusions:

  • The developed semiparametric randomization test is a valuable tool for analyzing complex relationships in RDS studies.
  • Accurate statistical inference is essential for understanding disease epidemiology in hidden populations.
  • This method enhances the reliability of findings from studies using respondent-driven sampling.