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The Importance of Measuring and Accounting for Potential Biases in Respondent-Driven Samples

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Abstract

Respondent-driven sampling (RDS) is often viewed as a superior method for recruiting hard-to-reach populations disproportionately burdened with poor health outcomes. As an analytic approach, it has been praised for its ability to generate unbiased population estimates via post-stratified weights which account for non-random recruitment. However, population estimates generated with RDSAT (RDS Analysis Tool) are sensitive to variations in degree weights. Several assumptions are implicit in the degree weight and are not routinely assessed. Failure to meet these assumptions could result in inaccurate degree measures and consequently result in biased population estimates. We highlight potential biases associated with violating the assumptions implicit in degree weights for the RDSAT estimator and propose strategies to measure and possibly correct for biases in the analysis.

Resumen

Respondent-driven sampling (RDS) suele ser considerado como uno de los mejores métodos para el reclutamiento de poblaciones de difícil acceso y con riesgos de salud desproporcionados con respecto al resto de la población. Analíticamente, RDS ha sido elogiado gracias al uso de ponderaciones post-estratificadas para compensar la falta de aleatoriedad en el muestreo, logrando obtener así estimadores poblacionales insesgados. A pesar de ello, los estimadores poblacionales que se obtienen con RDSAT (RDS Analysis Tool) han mostrado ser sensibles a variaciones en las ponderaciones por tamaño de la red. Varios supuestos están implícitos cuando usando los ponderaciones por tamaño de red y la validez de estos supuestos raramente son evaluados. Una violación de esos supuestos podría llevar a cálculo de ponderaciones erróneas y por lo tanto, a estimaciones poblacionales sesgadas. Nosotros discutimos los diferentes tipos de sesgos en los estimadores RDSAT que pueden llegar a surgir debido a violaciones en los supuestos necesarios para el cálculo de las ponderaciones por tamaño de red, y proponemos estrategias para medir y corregir ese sesgo.

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References

  1. Heckathorn D. Respondent-driven sampling: a new approach to the study of hidden populations. Soc Probl. 1997;44(2):174–99.

    Article  Google Scholar 

  2. Heckathorn D. Respondent-driven sampling II. Deriving valid population estimates from chain-referral samples of hidden populations. Soc Probl. 2002;49(1):11–34.

    Article  Google Scholar 

  3. Iguchi M, Ober A, Berry S, Fain R, Heckathorn D, Gorbach P, et al. Simultaneous recruitment of drug users and men who have sex with men in the United States and Russia using respondent-driven sampling: sampling methods and implications. J Urban Health. 2009;86(1):S5–31.

    Article  Google Scholar 

  4. Wang J, Carlson RG, Falck RS, Siegal HA, Rahman A, Li L. Respondent-driven sampling to recruit MDMA users: a methodological assessment. Drug Alcohol Depend. 2005;78(2):147–57.

    Article  PubMed  Google Scholar 

  5. Liu H, Li J, Ha T. Assessment of random recruitment assumption in respondent-driven sampling in egocentric network data. Social Netw. 2012;1(2):13–21.

    Article  Google Scholar 

  6. McCreesh N, Frost SDW, Seeley J, Katongole J, Tarsh MN, Ndunguse R, et al. Evaluation of respondent-driven sampling. Epidemiology. 2012;23(1):138.

    Article  PubMed  Google Scholar 

  7. de Mello M, de Araujo PA, Chinaglia M, Tun W, J′unior A, Il′ario M, et al. Assessment of risk factors for HIV infection among men who have sex with men in the metropolitan area of Campinas City, Brazil, using respondent-driven sampling: Population Council 2008.

  8. Johnston LG, Khanam R, Reza M, Khan SI, Banu S, Alam MS, et al. The effectiveness of respondent driven sampling for recruiting males who have sex with males in Dhaka, Bangladesh. AIDS Behav. 2008;12(2):294–304.

    Article  PubMed  Google Scholar 

  9. Johnston LG, Malekinejad M, Kendall C, Iuppa IM, Rutherford GW. Implementation challenges to using respondent-driven sampling methodology for HIV biological and behavioral surveillance: field experiences in international settings. AIDS Behav. 2008;12(4 Suppl):S131–41.

    Article  PubMed  Google Scholar 

  10. Scott G. “They got their program, and I got mine”: a cautionary tale concerning the ethical implications of using respondent-driven sampling to study injection drug users. Int J Drug Policy. 2008;19(1):42–51.

    Article  PubMed  Google Scholar 

  11. Heckathorn D, Semann S, Broadhead R, Hughes J. Extensions of respondent-driven sampling: a new approach to the study of injection drug users aged 18–25. AIDS Behav. 2002;6(1):55–67.

    Article  Google Scholar 

  12. Wang J, Falck RS, Li L, Rahman A, Carlson RG. Respondent-driven sampling in the recruitment of illicit stimulant drug users in a rural setting: findings and technical issues. Addict Behav. 2007;32(5):924–37.

    Article  PubMed  Google Scholar 

  13. Gile KJ, Handcock MS. Respondent-driven sampling: an assessment of current methodology. Sociol Methodol. 2010;40(1):285–327.

    Article  PubMed  Google Scholar 

  14. Lu X, Bengtsson L, Britton T, Camitz M, Kim BJ, Thorson A, et al. The sensitivity of respondent-driven sampling. J R Stat Soc: Series A (Statistics in Society) 2011;175(1):191–216.

    Google Scholar 

  15. Goel S, Salganik MJ. Assessing respondent-driven sampling. Proc Natl Acad Sci USA. 2010;107(15):6743–7.

    Article  PubMed  CAS  Google Scholar 

  16. Gile KJ, Handcock MS. Respondent-driven sampling: an assessment of current methodology. Arxiv preprint arXiv: 09041855. 2009.

  17. Tomas A, Gile KJ. The effect of differential recruitment, non-response and non-recruitment on estimators for respondent-driven sampling. Electron J Stat. 2011;5:899–934.

    Google Scholar 

  18. Gile KJ. Improved inference for respondent-driven sampling data with application to HIV prevalence estimation. J Am Stat Assoc. 2011;106(493):135–46.

    Article  CAS  Google Scholar 

  19. Salganik MJ, Heckathorn D. Sampling and estimation in hidden populations using respondent-driven sampling. Sociol Methodol. 2004;34:183–239.

    Article  Google Scholar 

  20. Heckathorn D. Extensions of respondent-driven sampling: analyzing continuous variables and controlling for differential recruitment. Sociol Method. 2007;37:151–208.

    Article  Google Scholar 

  21. Volz E, Heckathorn DD. Probability based estimation theory for respondent driven sampling. J Official Stat. 2008;24(1):79.

    Google Scholar 

  22. Volz E, Wejnert C, Degani I, Heckathorn DD. Respondent-driven sampling analysis tool (RDSAT) version 5.6. Ithaca: Cornell University. 2007.

  23. Goel S, Salganik MJ. Assessing respondent-driven sampling. Proc Natl Acad Sci USA. 2010;107(15):6743–7.

    Article  PubMed  CAS  Google Scholar 

  24. Frost SD, Brouwer KC, Firestone Cruz MA, Ramos R, Ramos ME, Lozada RM, et al. Respondent-driven sampling of injection drug users in two U.S.-Mexico border cities: recruitment dynamics and impact on estimates of HIV and syphilis prevalence. J Urban Health. 2006;83(6 Suppl):i83–97.

    Article  PubMed  Google Scholar 

  25. Paz-Bailey G, Jacobson JO, Guardado ME, Hernandez FM, Nieto AI, Estrada M, et al. How many men who have sex with men and female sex workers live in El Salvador? Using respondent-driven sampling and capture–recapture to estimate population sizes. Sex Transm Infect. 2011;87:267–8.

    Article  Google Scholar 

  26. Yeka W, Maibani-Michie G, Prybylski D, Colby D. Application of respondent driven sampling to collect baseline data on FSWs and MSM for HIV risk reduction interventions in two urban centres in Papua New Guinea. J Urban Health. 2006;83(6 Suppl):i60–72.

    Article  PubMed  Google Scholar 

  27. Ma X, Zhang Q, He X, Sun W, Yue H, Chen S, et al. Trends in prevalence of HIV, syphilis, hepatitis C, hepatitis B, and sexual risk behavior among men who have sex with men. Results of 3 consecutive respondent-driven sampling surveys in Beijing, 2004 through 2006. J Acquir Immune Defic Syndr. 2007;45(5):581–7.

    Article  PubMed  Google Scholar 

  28. DesJarlais D, Arasteh K, Perlis T, Hagan H, Abdul-Quader A, Heckathorn D, et al. Convergence of HIV seroprevalence among injecting and non-injecting drug users in New York City. AIDS. 2007;21:231–5.

    Article  Google Scholar 

  29. Johnston LG, Whitehead S, Simic-Lawson M, Kendall C. Formative research to optimize respondent-driven sampling surveys among hard-to-reach populations in HIV behavioral and biological surveillance: lessons learned from four case studies. AIDS Care. 2010;22(6):784–92.

    Article  PubMed  Google Scholar 

  30. Uuskula A, Kals M, Rajaleid K, Abel K, Talu A, Ruutel K, et al. High-prevalence and high-estimated incidence of HIV infection among new injecting drug users in Estonia: need for large scale prevention programs. J Public health (Oxford, England). 2008;30(2):119–25.

    Article  Google Scholar 

  31. Kajubi P, Kamya MR, Raymond HF, Chen S, Rutherford GW, Mandel JS, et al. Gay and bisexual men in Kampala, Uganda. AIDS Behav. 2008;12(3):492–504.

    Article  PubMed  Google Scholar 

  32. He Q, Wang Y, Lin P, Raymond HF, Li Y, Yang F, et al. High prevalence of risk behaviour concurrent with links to other high-risk populations: a potentially explosive HIV epidemic among men who have sex with men in Guangzhou, China. Sex Transm Infect. 2009;85(5):383.

    Article  PubMed  CAS  Google Scholar 

  33. Wellman B. Network analysis: some basic principles. JSTOR. 1983;1:155–200.

    Google Scholar 

  34. Emerson RM. Power-dependence relations. Am Sociol Rev. 1962;27(1):31–41.

    Article  Google Scholar 

  35. Shulman N. Network analysis: a new addition to an old bag of tricks. Acta Sociologica. 1976;19(4):307–23.

    Article  Google Scholar 

  36. Shulman N. Urban social networks. Unpublished doctoral dissertation, Department of Sociology, University of Toronto. 1972.

  37. Coleman J, Katz E, Menzel H. Medical innovation. Indianapolis: The Bobbs-Merrill; 1966.

    Google Scholar 

  38. Laumann EO. Friends of urban men: an assessment of accuracy in reporting their socioeconomic attributes, mutual choice, and attitude agreement. Sociometry. 1969;32:54–69.

    Article  Google Scholar 

  39. Rudolph AE, Crawford ND, Latkin C, White K, Benjamin EO, Jones KC, et al. Individual, study, and neighborhood level characteristics associated with peer recruitment of young illicit drug users: optimizing respondent driven sampling. Soc Sci Med. 2011;73(7):1097–104.

    Article  PubMed  Google Scholar 

  40. Wejnert C. An empirical test of respondent-driven sampling: point estimates, variance, degree measures, and out-of-equilibrium data. Sociol Methodol. 2009;39(1):73–116.

    Article  PubMed  Google Scholar 

  41. Rudolph AE, Crawford ND, Latkin C, Heimer R, Benjamin EO, Jones KC, et al. Subpopulations of illicit drug users reached by targeted street outreach and respondent-driven sampling strategies: implications for research and public health practice. Ann Epidemiol. 2011;21(4):280–9.

    Article  PubMed  Google Scholar 

  42. Ramirez-Valles J, Heckathorn DD, Vazquez R, Diaz RM, Campbell RT. From networks to populations: the development and application of respondent-driven sampling among IDUs and Latino gay men. AIDS Behav. 2005;9(4):387–402.

    Article  PubMed  Google Scholar 

  43. Brewer DD. Forgetting in the recall-based elicitation of personal and social networks. Soc Netw. 2000;22:29–43.

    Article  Google Scholar 

  44. Marsden PV. Network data and measurement. Annu Rev Sociol. 1990;16:435–63.

    Article  Google Scholar 

  45. Bell DC, Belli-McQueen B, Haider A. Partner naming and forgetting: recall of network members. Social Netw. 2007;29(2):279–99.

    Article  Google Scholar 

  46. Gwadz MV, Leonard NR, Cleland CM, Riedel M, Banfield A, Mildvan D. The effect of peer-driven intervention on rates of screening for AIDS clinical trials among African Americans and Hispanics. Am J Public Health. 2011;101(6):1096–102.

    Article  PubMed  Google Scholar 

  47. Abramovitz D, Volz EM, Strathdee SA, Patterson TL, Vera A, Frost SDW, et al. Using respondent-driven sampling in a hidden population at risk of HIV infection: who do HIV-positive recruiters recruit? Sex Transm Dis. 2009;36(12):750–6.

    Article  PubMed  Google Scholar 

  48. Broadhead RS, Heckathorn DD, Weakliem DL, Anthony DL, Madray H, Mills RJ, et al. Harnessing peer networks as an instrument for AIDS prevention: results from a peer-driven intervention. Public Health. 1999;113:42–57.

    Google Scholar 

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Acknowledgments

This work was supported by the National Institute on Drug Abuse at the National Institutes of Health (Grant Number K01 DA033879-01A1). All authors made substantial contributions to the (a) conception and design of the study, or acquisition of data or analysis and interpretation of data, (b) drafting the article or revising it critically for important intellectual content and (c) final approval of the version to be published.

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Authors and Affiliations

  1. Alcohol, Policy, and Safety Research Center, Pacific Institute for Research and Evaluation, 11720 Beltsville Drive Suite 900, Calverton, MD, 20705, USA

    Abby E. Rudolph

  2. Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA

    Crystal M. Fuller

  3. Department of Health, Behavior & Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA

    Carl Latkin

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  1. Abby E. Rudolph
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  2. Crystal M. Fuller
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  3. Carl Latkin
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Correspondence to Abby E. Rudolph.

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Rudolph, A.E., Fuller, C.M. & Latkin, C. The Importance of Measuring and Accounting for Potential Biases in Respondent-Driven Samples. AIDS Behav 17, 2244–2252 (2013). https://doi.org/10.1007/s10461-013-0451-y

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