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Respondent‐driven sampling as Markov chain Monte Carlo
Author(s) -
Goel Sharad,
Salganik Matthew J.
Publication year - 2009
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3613
Subject(s) - markov chain monte carlo , snowball sampling , variance (accounting) , sampling (signal processing) , sample (material) , respondent , statistics , markov chain , monte carlo method , computer science , econometrics , sample size determination , mathematics , chemistry , accounting , filter (signal processing) , chromatography , law , political science , business , computer vision
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. Copyright © 2009 John Wiley & Sons, Ltd.