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General regression methods for respondent-driven sampling data.

Mamadou Yauck1, Erica Em Moodie1, Herak Apelian1

  • 1Department of Epidemiology, Biostatistics an Occupational Health, McGill University, Montreal, Quéebec, Canada.

Statistical Methods in Medical Research
|July 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method for analyzing data from respondent-driven sampling, which helps understand social connections in hard-to-reach populations. The method was applied to study HIV treatment optimism among men who have sex with men.

Keywords:
Design weightshidden population samplinghomophilyidentifiationpeer effectssimultaneous autoregressive modelssocial networks

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Area of Science:

  • Social Sciences
  • Epidemiology
  • Network Analysis

Background:

  • Respondent-driven sampling (RDS) is crucial for studying hard-to-reach populations by using social networks.
  • Homophily, or connecting with similar individuals, complicates RDS data analysis due to network dependence.
  • Existing multivariate modeling strategies lack guidance for RDS data, especially concerning peer effects.

Purpose of the Study:

  • To propose a novel methodology for general regression techniques tailored to respondent-driven sampling data.
  • To address the challenges of homophily and network dependence in RDS analysis.
  • To investigate socio-demographic predictors of HIV treatment optimism in a specific population.

Main Methods:

  • Developed a new multivariate modeling strategy for respondent-driven sampling data.
  • Applied regression techniques to account for network structures and homophily.
  • Utilized data from a respondent-driven sampling study of gay, bisexual, and other men who have sex with men in Montreal.

Main Results:

  • The proposed methodology effectively models peer effects and network dependence in RDS data.
  • Identified key socio-demographic predictors associated with HIV treatment optimism.
  • Demonstrated the utility of the new approach in a real-world epidemiological context.

Conclusions:

  • The developed methodology provides principled guidance for analyzing complex respondent-driven sampling data.
  • This approach enhances understanding of factors influencing health behaviors in network-based studies.
  • The findings contribute to public health strategies for HIV treatment adherence and optimism.