Assessing respondent-driven sampling: A simulation study across different networks

The purpose was to assess RDS estimators in populations simulated with diverse connectivity characteristics, incorporating the putative influence of misreported degrees and transmission processes. Four populations were simulated using different random graph models. Each population was “infected” using four different transmission processes. From each combination of population x transmission, one thousand samples were obtained using a RDS-like sampling strategy. Three estimators were used to predict the population-level prevalence of the “infection”. Several types of misreported degrees were simulated. Also, samples were generated using the standard random sampling method and the respective prevalence estimates, using the classical frequentist estimator. Estimation biases in relation to population parameters were assessed, as well as the variance. Variability was associated with the connectivity characteristics of each simulated population. Clustered populations yield greater variability and no RDS-based strategy could address the estimation biases. Misreporting degrees had modest effects, especially when RDS estimators were used. The best results for RDS-based samples were observed when the “infection” was randomly attributed, without any relation with the underlying network structure.

[1]  Linus Bengtsson,et al.  The sensitivity of respondent‐driven sampling , 2012 .

[2]  Francisco I Bastos,et al.  Putting Respondent-Driven Sampling on the Map: Insights from Rio de Janeiro, Brazil , 2011, Journal of acquired immune deficiency syndromes.

[3]  Douglas D. Heckathorn,et al.  Respondent-driven sampling II: deriving valid population estimates from chain-referral samples of hi , 2002 .

[4]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[5]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[6]  W. Miller,et al.  Reaching Men Who Have Sex with Men: A Comparison of Respondent-Driven Sampling and Time-Location Sampling in Guatemala City , 2013, AIDS and Behavior.

[7]  A. Lansky,et al.  Developing an HIV Behavioral Surveillance System for Injecting Drug Users: The National HIV Behavioral Surveillance System , 2007, Public health reports.

[8]  Douglas D. Heckathorn,et al.  Comment: Snowball versus Respondent-Driven Sampling , 2011 .

[9]  B. Selwyn,et al.  Prevalence of HIV Risk Behaviors among Undocumented Central American Immigrant Women in Houston, Texas , 2012, AIDS and Behavior.

[10]  Mark S Handcock,et al.  7. Respondent-Driven Sampling: An Assessment of Current Methodology , 2009, Sociological methodology.

[11]  S. Berg.  Snowball Sampling—I , 2006 .

[12]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[13]  Richard G. White,et al.  Evaluation of the role of location and distance in recruitment in respondent-driven sampling , 2011, International journal of health geographics.

[14]  J. Richardus,et al.  A Comparison Between Respondent-Driven Sampling and Time-Location Sampling Among Men Who Have Sex with Men in Shenzhen, China , 2015, Archives of sexual behavior.

[15]  L. Johnston,et al.  An Empirical Examination of Respondent Driven Sampling Design Effects Among HIV Risk Groups from Studies Conducted Around the World , 2013, AIDS and Behavior.

[16]  L. Johnston,et al.  Sampling Males Who Inject Drugs in Haiphong, Vietnam: Comparison of Time-Location and Respondent-Driven Sampling Methods , 2015, Journal of Urban Health.

[17]  Cyprian Wejnert,et al.  3. An Empirical Test of Respondent-Driven Sampling: Point Estimates, Variance, Degree Measures, and Out-of-Equilibrium Data , 2009, Sociological methodology.

[18]  Matthew J. Salganik,et al.  Assessing respondent-driven sampling , 2010, Proceedings of the National Academy of Sciences.

[19]  April M Young,et al.  Spatial, temporal and relational patterns in respondent-driven sampling: evidence from a social network study of rural drug users , 2014, Journal of Epidemiology & Community Health.

[20]  Nick S. Jones,et al.  Errors in reported degrees and respondent driven sampling: Implications for bias☆ , 2014, Drug and alcohol dependence.

[21]  Matthew J. Salganik,et al.  Diagnostics for respondent‐driven sampling , 2012, Journal of the Royal Statistical Society. Series A,.

[22]  H. Thiede,et al.  Assessing differences in groups randomized by recruitment chain in a respondent-driven sample of Seattle-area injection drug users. , 2014, Annals of epidemiology.

[23]  Douglas D. Heckathorn,et al.  Respondent-driven sampling : A new approach to the study of hidden populations , 1997 .

[24]  Andrew J Copas,et al.  Evaluation of Respondent-driven Sampling , 2012, Epidemiology.

[25]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[26]  Erik M. Volz,et al.  Probability based estimation theory for respondent driven sampling , 2008 .

[27]  Marie-Claude Boily,et al.  Changes in the Transmission Dynamics of the HIV Epidemic After the Wide-Scale Use of Antiretroviral Therapy Could Explain Increases in Sexually Transmitted Infections: Results From Mathematical Models , 2004, Sexually transmitted diseases.

[28]  Renaud Lambiotte,et al.  Respondent‐driven sampling bias induced by community structure and response rates in social networks , 2017 .

[29]  L. Johnston,et al.  Sex Differences in HIV Prevalence, Behavioral Risks and Prevention Needs Among Anglophone and Francophone Sub-Saharan African Migrants Living in Rabat, Morocco , 2016, AIDS and Behavior.

[30]  B. Selwyn,et al.  HIV Testing Behaviors Among Undocumented Central American Immigrant Women in Houston, Texas , 2012, Journal of Immigrant and Minority Health.

[31]  A. Young,et al.  Assessing the Geographic Coverage and Spatial Clustering of Illicit Drug Users Recruited through Respondent-Driven Sampling in New York City , 2015, Journal of Urban Health.

[32]  D. Heckathorn,et al.  SNOWBALL VERSUS RESPONDENT-DRIVEN SAMPLING. , 2011, Sociological methodology.

[33]  Matthew J. Salganik,et al.  Respondent‐driven sampling as Markov chain Monte Carlo , 2009, Statistics in medicine.