Open AccessGeneral regression methods for respondent-driven sampling data
Author(s) -
Mamadou Yauck,
Erica E. M. Moodie,
Herak Apelian,
Alain Fourmigue,
Daniel Grace,
Trevor Hart,
Gilles Lambert,
Joseph Cox
Publication year - 2021
Publication title -
statistical methods in medical research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.952
H-Index - 85
eISSN - 1477-0334
pISSN - 0962-2802
DOI - 10.1177/09622802211032713
Subject(s) - homophily , respondent , sampling (signal processing) , sample (material) , psychology , statistics , computer science , econometrics , social psychology , mathematics , political science , chemistry , filter (signal processing) , chromatography , law , computer vision
Respondent-driven sampling is a variant of link-tracing sampling techniques that aim to recruit hard-to-reach populations by leveraging individuals' social relationships. As such, a respondent-driven sample has a graphical component which represents a partially observed network of unknown structure. Moreover, it is common to observe homophily , or the tendency to form connections with individuals who share similar traits. Currently, there is a lack of principled guidance on multivariate modelling strategies for respondent-driven sampling to address peer effects driven by homophily and the dependence between observations within the network. In this work, we propose a methodology for general regression techniques using respondent-driven sampling data. This is used to study the socio-demographic predictors of HIV treatment optimism (about the value of antiretroviral therapy) among gay, bisexual and other men who have sex with men, recruited into a respondent-driven sampling study in Montreal, Canada.