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In most countries, HIV surveillance systems do not generate good quality samples of most-at-risk populations. Respondent-driven sampling (RDS) is being widely used as an effective method to sample most-at-risk populations for HIV bio-behavioural surveys.

RDS is a chain-referral sampling used worldwide to recruit “hidden” populations that are less likely to be recruited from public venues. Since the first RDS study was conducted in 1994 in the United States, this method has been used in more than 150 studies in more than 50 countries to sample injecting drug users, sex workers, men who have sex with men, migrant/mobile populations, high risk heterosexual men and women and youth at risk. However, many of the countries have been unable to analyze their data due to their inability to use the specialized RDS analysis tool (RDSAT) and difficulties in understanding the complexities of social network theory and statistical adjustments inherent in using this programme.

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The course provides participants with knowledge and understanding of RDS data analysis, and the theoretical and statistical concepts upon which it is based. The course addresses all steps in RDS data management and analysis, including the best practices for bivariate and multivariate analysis. The training course will address new issues in RDS and RDSAT, and parameters that need to be taken into account when planning 2nd and any subsequent rounds of RDS. Importantly, the course addresses interpretation of RDS data, particularly the external validity.
Each participant will work on a computer with the following software:

• RDSAT 6.0.1
• Excel
• NETDRAW (freeware for recruitment chains)
• STATA or SPSS (for logistic regression)

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• Latest information about implementing RDS
• RDS software that is currently available, its capacity and limitations
• Theoretical parameters of RDS and the meaning of technical concepts such as homophily, social network sizes, differential recruitment and equilibrium
• Preparation of data files that can be used in RDS software and analysis of data using RDSAT
• Graphical presentation of recruitment chains using NetDraw and Gephi
• Analysis of trend data from RDS surveys
• Export of individualized weights to conduct regression analyses
• Regression analyses using either SPSS or STATA
• Data interpretation and analysis

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The course consists of lectures and individual work on computers so that participants can get hands-on experience in RDS data management and analysis. Participants are encouraged to bring their own RDS data sets to work on, particularly if they need to write up the results from RDS surveys for surveillance reports or scientific manuscripts. Those without actual data will be provided with datasets. Participants should be familiar in basic statistics and Excel software. Participants are expected to bring their own laptop computers at the course. They will be given statistical programmes at the course to work with.

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This training invites principal investigators, statisticians, data managers and other researchers who have experience using RDS.

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cialis pill, PhD, Independent Consultant; Senior analyst for University of California, San Francisco, Global Health Sciences; Assistant/adjunct professor, Tulane University, School of International Public Health and Tropical Medicine. Website: www.lisagjohnston.com.

cialis pill, MD, DrPH, WHO Collaborating Centre for HIV Surveillance, School of Medicine, University of Zagreb, Croatia

Aleksandar Stulhofer, PhD, Faculty of Arts and Humanities, University of Zagreb, Croatia

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Danijela Leso, WHO Collaborating Centre for Capacity Building in HIV Surveillance

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The course takes place in five days, from 13-17 February 2012 in Zagreb, Croatia.

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Web site of Respondent-Driven Sampling, includes downloads of RDSAT and the references
Cornell University
www.respondentdrivensampling.org

Heckathorn D. Respondent-Driven Sampling: A new approach to the study of hidden populations. Social Problems. 1997;44:174–199.

Heckathorn D, Semaan S, Broadhead RS, et al. Extensions of respondent-
driven sampling: a new approach to the study of injection drug users aged 18-25. AIDS
and Behavior 2002;6:55-67.

Townsend L, Johnston LG, Flisher AJ, Mathews C, Zembe Y. Effectiveness of respondent-driven sampling to recruit high risk heterosexual men who have multiple female sexual partners: differences in HIV prevalence and sexual risk behaviours measured at two time points. AIDS Behav. 2010;14(6):1330-9.

Bozicevic I, Rode OD, Lepej SZ, Johnston LG, Stulhofer A, Dominkovic Z, Bacak V, Lukas D, Begovac J.Prevalence of sexually transmitted infections among men who have sex with men in Zagreb, Croatia. AIDS Behav 2009;13:303-9.

Johnston LG, Malekinejad M, Kendall C, Iuppa IM, Rutherford GW.I mplementation 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 2008.

Malekinejad M, Johnston LG, Kendall C, Kerr LR, Rifkin MR, Rutherford GW.
Using respondent-driven sampling methodology for HIV biological and behavioral surveillance in international settings: a systematic review. AIDS Behav 2008;12(4 Suppl):S105-30.

Gile KJ, Handcock MS. Respondent-Driven Sampling: An Assessment of Current Methodology. Sociological Methods, 2010. 40, 1: 285-327.

Goel S, Salganik MJ: Respondent-driven sampling as Markov chain Monte Carlo, Statistics in Medicine, 2009, 28(17), 2202 – 2229.

Goel S, Salganik MJ. 2010. “Assessing respondent-driven sampling.” Proceedings of the National Academy of Sciences, 107:6743-6747.

Heckathorn DD. Extensions of respondent-driven sampling: analyzing continuous variables and controlling for differential recruitment. Sociol Methodol. 2007;37:151-207.

McPherson M, Smith-Lovin L, Cook JM. 2001. “Birds of a feather: homophily in social networks”. Annual Review of Sociology. 27: 415-444.

Salganik MJ, Heckathorn D. 2003. “Sampling and estimation in hidden populations using respondent driven sampling.” Presented at the Sunbelt Social Networks Conference, Cancun, Mexico.

Salganik MJ: Variance estimation, design effects and sample size calculations for respondent driven sampling. Journal of Urban Health, 2006, 83(Suppl 7), 98–112.

Wejnert C: An Empirical Test of Respondent-Driven Sampling: Point Estimates, Variance, Degree Measures, and Out-of-Equilibrium Data. Sociological Methodology, 2009, 39, 73-116

Volz E, Heckathorn DD: Probability-Based Estimation Theory for Respondent-Driven Sampling. Journal of Official Statistics, 2008, 24 (Suppl 1), 79-97.

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