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A single weighting approach to analyze respondent-driven sampling data.

Vadivoo Selvaraj1, Kangusamy Boopathi1, Ramesh Paranjape2

  • 1National Institute of Epidemiology, Indian Council of Medical Research, TNHB, Ayapakkam, Chennai, India.

The Indian Journal of Medical Research
|February 1, 2017
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Summary
This summary is machine-generated.

We introduce RDS-MOD, a simplified method for analyzing respondent-driven sampling (RDS) data. This approach offers efficient estimation and regression analysis, making complex statistical analysis more accessible.

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Area of Science:

  • Social Sciences
  • Statistics
  • Epidemiology

Background:

  • Respondent-driven sampling (RDS) is crucial for studying hidden populations.
  • Current analysis tools like RDSAT and RDS-analyst have limitations, particularly for regression analysis.
  • Specialized skills are often required for existing RDS data analysis methods.

Purpose of the Study:

  • To propose a simplified approach for comprehensive statistical analysis of RDS data.
  • To enable the use of standard statistical software for RDS data analysis.
  • To address the challenges in regression analysis with RDS data.

Main Methods:

  • Developed RDS-MOD (respondent-driven sampling-modified) to assign a single normalized weight per participant.
  • Transformed RDS data into clustered data to account for recruit-recruiter relationships.
  • Employed Taylor's linearization for confidence intervals and generalized estimating equations for regression analysis.

Main Results:

  • Parameter estimates, including proportions, from RDS-MOD were consistent with existing specialized RDS software.
  • The proposed weighting method showed comparability with weights generated by RDSAT.
  • Estimates from RDS-MOD were comparable to those obtained using the RDS II approach.

Conclusions:

  • RDS-MOD offers an efficient and user-friendly method for estimation and regression analysis.
  • The approach effectively accounts for inter-individual dependence between recruits and recruiters.
  • This method simplifies complex RDS data analysis, making it more accessible.