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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Modeling and analyzing respondent-driven sampling as a counting process.

Yakir Berchenko1, Jonathan D Rosenblatt1, Simon D W Frost2

  • 1Department of Industrial Engineering and Management, Ben Gurion University of the Negev, Beersheba, Israel.

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Summary
This summary is machine-generated.

This study introduces a new method using recruitment timing to estimate population size in Respondent-Driven Sampling (RDS). This improves HIV prevalence estimates by addressing biases from highly connected individuals.

Keywords:
Counting processHIVHidden populationsRespondent driven sampling

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Area of Science:

  • Social Sciences
  • Statistics
  • Epidemiology

Background:

  • Respondent-Driven Sampling (RDS) uses social networks for chain-referral sampling.
  • RDS can oversample highly connected individuals, biasing results.
  • Current RDS methods cannot estimate population size and struggle with accurate prevalence estimation.

Purpose of the Study:

  • To develop a novel method for estimating population size using RDS recruitment timing.
  • To improve the accuracy of population parameter estimation, such as HIV prevalence, in RDS studies.
  • To provide a robust statistical framework for RDS analysis with explicit assumptions.

Main Methods:

  • Utilizing recruitment timing data with a counting process model to estimate population size.
  • Adapting methods from epidemiology and software reliability for parameter estimation.
  • Developing large-sample theory for consistency and asymptotic normality of estimators.
  • Implementing estimators in the R package 'chords'.

Main Results:

  • The proposed method successfully estimates population size and degree frequencies from RDS data.
  • Debiased prevalence estimates are achieved through post-stratification using estimated population size.
  • Simulations and real-world data show the new estimators outperform existing RDS methods.
  • The likelihood problem is separable and efficiently solvable.

Conclusions:

  • The novel approach enhances RDS by enabling population size estimation and improving parameter accuracy.
  • Explicit assumptions and robust statistical theory make the method verifiable and extensible.
  • The 'chords' R package provides accessible implementation for researchers.