Estimating the size of populations at high risk for HIV using respondent-driven sampling data

Biometrics

Volumn 71, Issue 1, 2015, Pages 258-266

  Handcock, Mark S ^a   Gile, Krista J ^b   Mar, Corinne M ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b UNIVERSITY OF MASSACHUSETTS   (United States)

^c UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Hard to reach population sampling; Model based survey sampling; Network sampling; Social networks; Successive sampling

Indexed keywords

RISK PERCEPTION; SOCIAL NETWORKING (ONLINE);

CASE-STUDIES; HARD-TO-REACH POPULATION SAMPLING; MODEL-BASED OPC; MODEL-BASED SURVEY SAMPLING; NETWORK SAMPLINGS; NUMBER OF PEOPLES; POPULATION SIZES; SOCIAL NETWORK; SUCCESSIVE SAMPLINGS; SURVEY SAMPLINGS;

POPULATION STATISTICS;

BAYESIAN ANALYSIS; DISEASE PREVALENCE; ESTIMATION METHOD; HEALTH RISK; HEALTH SURVEY; HUMAN IMMUNODEFICIENCY VIRUS; SAMPLING; SOFTWARE; URBAN AREA;

EL SALVADOR [CENTRAL AMERICA];

SALVADORIA;

COMPUTER SIMULATION; EL SALVADOR; EPIDEMIOLOGY; HIV INFECTIONS; HUMAN; MALE; MALE HOMOSEXUALITY; PREVALENCE; PROCEDURES; REPRODUCIBILITY; RISK ASSESSMENT; SAMPLE SIZE; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; URBAN POPULATION;

COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; EL SALVADOR; EPIDEMIOLOGIC METHODS; HIV INFECTIONS; HOMOSEXUALITY, MALE; HUMANS; MALE; MODELS, STATISTICAL; PREVALENCE; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; SAMPLE SIZE; SENSITIVITY AND SPECIFICITY; URBAN POPULATION;

EID: 84926136694     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/biom.12255     Document Type: Article

References (39)

1. Andreatta, G. and Kaufman, G. M. (1986). Estimation of finite population properties when sampling is without replacement and proportional to magnitude. Journal of the American Statistical Association 81, 657-666.
2. Bao, L., Raftery, A. E., and Reddy, A. (2010). Estimating the size of populations at high risk of HIV in Bangladesh using a Bayesian hierarchical model. Department of Statistics Technical Report No. 573, University of Washington.
3. Berchenko, Y. and Frost, S. D. W. (2011). Capture-recapture methods and respondent-driven sampling: Their potential and limitations. Sexually Transmitted Infections 87, 267-268.
4. Bickel, P. J., Nair, V. N., and Wang, P. C. C. (1992). Nonparametric inference under biased sampling from a finite population. Annals of Statistics 20, 853-878.
5. Boguna, M., Pastor-Satorras, R., and Vespignani, A. (2004). Cut-offs and finite size effects in scale-free networks. European Physical Journal B 38, 205-209.
6. Burda, Z. and Krzywicki, A. (2003). Uncorrelated random networks. Physical Review E 67, 046118.
7. Catanzaro, M., Boguna, M., and Pastor-Satorras, R. (2005). Generation of uncorrelated random scale-free networks. Physical Review E 71, 1-4.
8. Chung, F. and Lu, L. (2002). Connected components in random graphs with given expected degree sequences. Annals of Combinatorics 6, 125-145.
9. de Estadistica y Censos, D. G. (2007). Vi censo de poblacion y v de vivienda El Salvador. Technical Report, Ministerio de Economia de El Salvador.
10. Fienberg, S. E., Johnson, M. S., and Junker, B. W. (1999). Classical multilevel and Bayesian approaches to population size estimation using multiple lists. Journal of the Royal Statistical Society, Series A, Statistics in Society 162, 383-405.
11. Foster, J. G., Foster, D. V., Grassberger, P., and Paczuski, M. (2007). Link and subgraph likelihoods in random undirected networks with fixed and partially fixed degree sequences. Physical Review E 76.
12. Gile, K. J. (2011). Improved inference for respondent-driven sampling data with application to HIV prevalence estimation. Journal of the American Statistical Association 106, 135-146.
13. Gile, K. J. and Handcock, M. S. (2010). Respondent-driven sampling: An assessment of current methodology. Sociological Methodology 40, 285-327.
14. Guardado, M. E., Creswell, J., Monterroso, E., and Paz-Bailey, G. (2010). Encuesta centroamericana de vigilancia de comportamiento sexual y prevalencia de VIH/ITS en poblaciones vulnerables, hombres que tienen sexo con hombres, trabajadoras sexuales y personas con VIH, ECVC El Salvador. Research report, Minisiterio de Salud de El Salvador.
15. Handcock, M. S. (2011). size: Estimating Population Size from Discovery Models using Successive Sampling Data. Hard-to-Reach Population Methods Research Group, Los Angeles, CA. R package version 0.20.
16. Handcock, M. S. and Gile, K. J. (2010). Modeling networks from sampled data. Annals of Applied Statistics 272, 383-426.
17. Handcock, M. S., Gile, K. J., and Mar, C. M. (2014). Estimating hidden population size using respondent-driven sampling data. Electronic Journal of Statistics 8, 1491-1521.
18. Handcock, M. S. and Jones, J. H. (2004). Likelihood-based inference for stochastic models of sexual network formation. Theoretical Population Biology 65, 413-422.
19. Heckathorn, D. D. (1997). Respondent-driven sampling: A new approach to the study of hidden populations. Social Problems 44, 174-199.
20. Heckathorn, D. D. (2007). Extensions of respondent-driven sampling: Analyzing continuous variables and controlling for differential recruitment. Sociological Methodology 37, 151-207.
21. Johnston, L. G. (2007). Conducting respondent driven sampling (RDS) studies in diverse settings: A training manual for planning RDS studies. Centers for Disease Control and Prevention, Atlanta, GA and Family Health International, Arlington, VA.
22. Johnston, L. G., Malekinejad, M., Kendall, C., Iuppa, I. M., and Rutherford, G. W. (2008). Implementation challenges to using respondent-driven sampling methodology for HIV biological and behavioral surveillance: Field experiences in international settings. AIDS and Behavior 12, 131-141.
23. Molloy, M. S. and Reed, B. A. (1995). A critical point for random graphs with a given degree sequence. Random Structures and Algorithms 6, 161-179.
24. Morales-Miranda, S., Paz-Bailey, G., and Alvarez, B. (2007). Behavioral and HIV survey among female sex workers and men who have sex with men in Honduras using respondent driven sampling. Technical Report, University of Valle of Guatemala.
25. Murthy, M. N. (1957). Ordered and unordered estimators in sampling without replacement. Sankhya: The Indian Journal of Statistics 18, 379-390.
26. Nair, V. N. and Wang, P. C. C. (1989). Maximum likelihood estimation under a successive sampling discovery model. Technometrics 31, 423-436.
27. Paz-Bailey, G., Jacobson, J. O., Guardado, M. E., Hernandez, F. M., Nieto, A. I., Estrada, M., and Creswell, J. (2011). 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. Sexually Transmitted Infections 87, 279-282.
28. Raj, D. (1956). Some estimators in sampling with varying probabilities without replacement. Journal of the American Statistical Association 51, 269-284.
29. Rocchetti, I., Bunge, J., and Böhning, D. (2011). Population size estimation based upon ratios of recapture probabilities. Annals of Applied Statistics 5, 1512-1533.
30. Salganik, M. J., Fazito, D., Bertoni, N., Abdo, A. H., Mello, M. B., and Bastos, F. I. (2011). Assessing network scale-up estimates for groups most at risk of HIV/AIDS: Evidence from a multiple-method study of heavy drug users in Curitiba, Brazil. American Journal of Epidemiology 174, 1190-1196.
31. Salganik, M. J. and Heckathorn, D. D. (2004). Sampling and estimation in hidden populations using respondent-driven sampling. Sociological Methodology 34, 193-239.
32. Shmueli, G., Minka, T. P., Kadane, J. B., Borle, S., and Boatwright, P. (2005). A useful distribution for fitting discrete data: Revival of the Conway-Maxwell-Poisson distribution. Journal of the Royal Statistical Society, Series C, Applied Statistics 54, 127-142.
33. Soto, R. J., Ghee, A. E., Nunez, C. A., Mayorga, R., Tapia, K. A., Astete, S. G., Hughes, J. P., Buffardi, A. L., Holte, S. E., Holmes, K. K., and the Estudio Multicentrico Study Team (2007). Sentinel surveillance of sexually transmitted infections/HIV and risk behaviors in vulnerable populations in 5 Central American countries. Journal of Acquired Immune Deficiency Syndromes and Human Retrovirology 46, 101-111.
34. UNAIDS (2009). Estimating national adult prevalence of HIV-1 in concentrated epidemics. Technical Report, UNAIDS-Joint United Nations Programme on HIV/AIDS.
35. UNAIDS and World Health Organization (2010). Guidelines on estimating the size of populations most at risk to HIV. Technical Report, UNAIDS/00.03E, UNAIDS-Joint United Nations Programme on HIV/AIDS.
36. UNAIDS/WHO (2003). Estimating the size of populations at risk for HIV: Issues and methods. Technical Report, UNAIDS/WHO Working Group on HIV/AIDS.
37. UNAIDS/WHO (2010). HIV/AIDS health profile of El Salvador. Technical Report, UNAIDS/WHO Working Group on HIV/AIDS.
38. Volz, E. and Heckathorn, D. D. (2008). Probability based estimation theory for respondent driven sampling. Journal of Official Statistics 24, 79-97.
39. West, M. (1996). Inference in successive sampling discovery models. Journal of Econometrics 75, 217-238.