Respondent-driven Sampling As Markov Chain Monte Carlo

Overview

Journal Stat Med

Publisher Wiley

Specialty Public Health

Date 2009 Jul 3

PMID 19572381

Citations 62

Authors

Sharad Goel

Matthew J Salganik

Affiliations

Soon will be listed here.

Abstract

Respondent-driven sampling (RDS) is a recently introduced, and now widely used, technique for estimating disease prevalence in hidden populations. RDS data are collected through a snowball mechanism, in which current sample members recruit future sample members. In this paper we present RDS as Markov chain Monte Carlo importance sampling, and we examine the effects of community structure and the recruitment procedure on the variance of RDS estimates. Past work has assumed that the variance of RDS estimates is primarily affected by segregation between healthy and infected individuals. We examine an illustrative model to show that this is not necessarily the case, and that bottlenecks anywhere in the networks can substantially affect estimates. We also show that variance is inflated by a common design feature in which the sample members are encouraged to recruit multiple future sample members. The paper concludes with suggestions for implementing and evaluating RDS studies.

Citing Articles

Using Respondent-Driven Sampling (RDS) to Identify the Healthcare Needs among Women of Reproductive Age Who Migrated from Venezuela to Brazil, 2018-2021.

Szwarcwald C, Souza Junior P, de Carvalho T, Queiroz R, Castilho E, Leal M Int J Environ Res Public Health. 2024; 21(6).

PMID: 38929057 PMC: 11203649. DOI: 10.3390/ijerph21060811.

Evaluation of respondent-driven sampling in seven studies of people who use drugs from rural populations: findings from the Rural Opioid Initiative.

Rudolph A, Nance R, Bobashev G, Brook D, Akhtar W, Cook R BMC Med Res Methodol. 2024; 24(1):94.

PMID: 38654219 PMC: 11036624. DOI: 10.1186/s12874-024-02206-5.

Preferences and access to community-based HIV testing sites among men who have sex with men (MSM) in Côte d'Ivoire.

Inghels M, Kouassi A, Niangoran S, Bekelynck A, Carilon S, Sika L BMJ Open. 2022; 12(6):e052536.

PMID: 35760538 PMC: 9237902. DOI: 10.1136/bmjopen-2021-052536.

Hoeffding's inequality for general Markov chains with its applications to statistical learning.

Fan J, Jiang B, Sun Q J Mach Learn Res. 2021; 22.

PMID: 34566520 PMC: 8457514.

Predictors of Police Reporting Among Hispanic Immigrant Victims of Violence.

Hautala D, Dombrowski K, Marcus A Race Justice. 2021; 5(3):235-258.

PMID: 34295568 PMC: 8293699. DOI: 10.1177/2153368714554717.

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