Literature DB >> 28605424

Identification of Homophily and Preferential Recruitment in Respondent-Driven Sampling.

Forrest W Crawford1,2,3, Peter M Aronow1,4,3, Li Zeng1, Jianghong Li5.   

Abstract

Respondent-driven sampling (RDS) is a link-tracing procedure used in epidemiologic research on hidden or hard-to-reach populations in which subjects recruit others via their social networks. Estimates from RDS studies may have poor statistical properties due to statistical dependence in sampled subjects' traits. Two distinct mechanisms account for dependence in an RDS study: homophily, the tendency for individuals to share social ties with others exhibiting similar characteristics, and preferential recruitment, in which recruiters do not recruit uniformly at random from their network alters. The different effects of network homophily and preferential recruitment in RDS studies have been a source of confusion and controversy in methodological and empirical research in epidemiology. In this work, we gave formal definitions of homophily and preferential recruitment and showed that neither is identified in typical RDS studies. We derived nonparametric identification regions for homophily and preferential recruitment and showed that these parameters were not identified unless the network took a degenerate form. The results indicated that claims of homophily or recruitment bias measured from empirical RDS studies may not be credible. We applied our identification results to a study involving both a network census and RDS on a population of injection drug users in Hartford, Connecticut (2012-2013).
© The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Keywords:  hidden population; link-tracing; network sampling; nonparametric bounds; social network

Mesh:

Year:  2018        PMID: 28605424      PMCID: PMC5860647          DOI: 10.1093/aje/kwx208

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  37 in total

1.  SOCIAL NETWORK ANALYSIS WITH RESPONDENT-DRIVEN SAMPLING DATA: A STUDY OF RACIAL INTEGRATION ON CAMPUS.

Authors:  Cyprian Wejnert
Journal:  Soc Networks       Date:  2010-05-01

2.  Subpopulations of illicit drug users reached by targeted street outreach and respondent-driven sampling strategies: implications for research and public health practice.

Authors:  Abby E Rudolph; Natalie D Crawford; Carl Latkin; Robert Heimer; Ebele O Benjamin; Kandice C Jones; Crystal M Fuller
Journal:  Ann Epidemiol       Date:  2011-04       Impact factor: 3.797

3.  Evaluation of respondent-driven sampling.

Authors:  Nicky McCreesh; Simon D W Frost; Janet Seeley; Joseph Katongole; Matilda N Tarsh; Richard Ndunguse; Fatima Jichi; Natasha L Lunel; Dermot Maher; Lisa G Johnston; Pam Sonnenberg; Andrew J Copas; Richard J Hayes; Richard G White
Journal:  Epidemiology       Date:  2012-01       Impact factor: 4.822

4.  Homophily and Contagion Are Generically Confounded in Observational Social Network Studies.

Authors:  Cosma Rohilla Shalizi; Andrew C Thomas
Journal:  Sociol Methods Res       Date:  2011-05

5.  Estimating design effect and calculating sample size for respondent-driven sampling studies of injection drug users in the United States.

Authors:  Cyprian Wejnert; Huong Pham; Nevin Krishna; Binh Le; Elizabeth DiNenno
Journal:  AIDS Behav       Date:  2012-05

6.  Harnessing peer networks as an instrument for AIDS prevention: results from a peer-driven intervention.

Authors:  R S Broadhead; D D Heckathorn; D L Weakliem; D L Anthony; H Madray; R J Mills; J Hughes
Journal:  Public Health Rep       Date:  1998-06       Impact factor: 2.792

7.  Challenges to recruiting population representative samples of female sex workers in China using Respondent Driven Sampling.

Authors:  M Giovanna Merli; James Moody; Jeffrey Smith; Jing Li; Sharon Weir; Xiangsheng Chen
Journal:  Soc Sci Med       Date:  2014-04-30       Impact factor: 4.634

Review 8.  Strengthening the Reporting of Observational Studies in Epidemiology for respondent-driven sampling studies: "STROBE-RDS" statement.

Authors:  Richard G White; Avi J Hakim; Matthew J Salganik; Michael W Spiller; Lisa G Johnston; Ligia Kerr; Carl Kendall; Amy Drake; David Wilson; Kate Orroth; Matthias Egger; Wolfgang Hladik
Journal:  J Clin Epidemiol       Date:  2015-05-01       Impact factor: 6.437

9.  Comparison of contact patterns relevant for transmission of respiratory pathogens in Thailand and The Netherlands using respondent-driven sampling.

Authors:  Mart L Stein; Jim E van Steenbergen; Vincent Buskens; Peter G M van der Heijden; Charnchudhi Chanyasanha; Mathuros Tipayamongkholgul; Anna E Thorson; Linus Bengtsson; Xin Lu; Mirjam E E Kretzschmar
Journal:  PLoS One       Date:  2014-11-25       Impact factor: 3.240

10.  Network Structure and Biased Variance Estimation in Respondent Driven Sampling.

Authors:  Ashton M Verdery; Ted Mouw; Shawn Bauldry; Peter J Mucha
Journal:  PLoS One       Date:  2015-12-17       Impact factor: 3.240

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  9 in total

1.  Overlooked Threats to Respondent Driven Sampling Estimators: Peer Recruitment Reality, Degree Measures, and Random Selection Assumption.

Authors:  Jianghong Li; Thomas W Valente; Hee-Sung Shin; Margaret Weeks; Alexei Zelenev; Gayatri Moothi; Heather Mosher; Robert Heimer; Eduardo Robles; Greg Palmer; Chinekwu Obidoa
Journal:  AIDS Behav       Date:  2018-07

2.  A SIMULATION-BASED FRAMEWORK FOR ASSESSING THE FEASIBILITY OF RESPONDENT-DRIVEN SAMPLING FOR ESTIMATING CHARACTERISTICS IN POPULATIONS OF LESBIAN, GAY AND BISEXUAL OLDER ADULTS.

Authors:  Maryclare Griffin; Krista J Gile; Karen I Fredricksen-Goldsen; Mark S Handcock; Elena A Erosheva
Journal:  Ann Appl Stat       Date:  2018-11-13       Impact factor: 2.083

3.  Divergent estimates of HIV incidence among people who inject drugs in Ukraine.

Authors:  Olga Morozova; Robert E Booth; Sergii Dvoriak; Kostyantyn Dumchev; Yana Sazonova; Tetiana Saliuk; Forrest W Crawford
Journal:  Int J Drug Policy       Date:  2019-08-10

4.  Social Network Clustering and the Spread of HIV/AIDS Among Persons Who Inject Drugs in 2 Cities in the Philippines.

Authors:  Ashton M Verdery; Nalyn Siripong; Brian W Pence
Journal:  J Acquir Immune Defic Syndr       Date:  2017-09-01       Impact factor: 3.731

5.  Causal Inference Under Interference And Network Uncertainty.

Authors:  Rohit Bhattacharya; Daniel Malinsky; Ilya Shpitser
Journal:  Uncertain Artif Intell       Date:  2019-07

6.  NEW SURVEY QUESTIONS AND ESTIMATORS FOR NETWORK CLUSTERING WITH RESPONDENT-DRIVEN SAMPLING DATA.

Authors:  Ashton M Verdery; Jacob C Fisher; Nalyn Siripong; Kahina Abdesselam; Shawn Bauldry
Journal:  Sociol Methodol       Date:  2017-07-06

7.  A comparison of the effectiveness of respondent-driven and venue-based sampling for identifying undiagnosed HIV infection among cisgender men who have sex with men and transgender women in Tijuana, Mexico.

Authors:  Heather A Pines; Shirley J Semple; Carlos Magis-Rodríguez; Alicia Harvey-Vera; Steffanie A Strathdee; Rudy Patrick; Gudelia Rangel; Thomas L Patterson
Journal:  J Int AIDS Soc       Date:  2021-03       Impact factor: 5.396

8.  Social network methods for HIV case-finding among people who inject drugs in Tajikistan.

Authors:  Maxim Kan; Danielle B Garfinkel; Olga Samoylova; Robert P Gray; Kristen M Little
Journal:  J Int AIDS Soc       Date:  2018-07       Impact factor: 5.396

9.  General regression methods for respondent-driven sampling data.

Authors:  Mamadou Yauck; Erica Em Moodie; Herak Apelian; Alain Fourmigue; Daniel Grace; Trevor Hart; Gilles Lambert; Joseph Cox
Journal:  Stat Methods Med Res       Date:  2021-07-28       Impact factor: 3.021

  9 in total

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