Inference from multivariate differential recruitment in respondent-driven sampling data
Vanesa Reinoso, Danilo Alvares, Jonathan Acosta, Isabelle S. Beaudry

TL;DR
This paper introduces Multivariate Differential Recruitment (MDR) for respondent-driven sampling, modeling recruitment as a Markov process with multiple covariates, and extends prevalence estimators accordingly.
Contribution
It develops a multivariate framework for RDS that accounts for multiple covariates, improving inference accuracy over univariate models.
Findings
MDR effectively incorporates multiple covariates into RDS inference.
Simulation studies demonstrate improved estimator performance.
Application to Venezuelan population data illustrates practical utility.
Abstract
Respondent-Driven Sampling (RDS) is a chain-referral design used for collecting data from hidden or hard-to-reach populations through their social networks. In RDS, respondents recruit their peers from the population of interest. As such, inference with RDS data commonly relies on estimated sampling probabilities derived from specific recruitment assumptions. Early literature assumes random recruitment, which is often unrealistic because individuals may recruit based on their personal preferences. This behavior is known as Differential Recruitment (DR). Recent works have incorporated univariate categorical DR in the estimation procedures. The main objective of this paper is to introduce Multivariate Differential Recruitment (MDR), a framework that incorporates multiple simultaneous covariates, both categorical and continuous, into the sampling representation. We model RDS as a Markov…
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