Respondent Driven Sampling (RDS) is arguably the best and most common method being used to survey """"""""hidden"""""""" populations that are hard to sample using standard probability methods. Examples include injection drug users (IDU), men who have sex with men (MSM), and female sex workers. Infections amongst just two hidden populations (MSM, IDUs) accounted for the estimated 69% of 53,600 new HIV infections in the US during 2006. We propose to develop statistical methods for improving estimation in RDS samples with a focus on developing tools and methods that will help, and be accessible by, researchers in the field. From a statistical perspective, RDS is an adaptive sampling regime aimed at obtaining a probability sample. It is often effective at acquiring a sample, but the degree to which it can be considered a probability sample, with known inclusion probabilities, is unclear. The current estimators of these inclusion probabilities are known to be problematic. We will develop open-source user-friendly statistical software for RDS incorporating state-of-the-art methods, hold workshops to establish collaboration between applied and methodological researchers, disseminate these new methods and provide training using the tools developed. This project is significant because it will result in major advances in methodology for hidden population measurement and related scientific problems. The investigators are uniquely qualified as the first statisticians with close collaborative ties with field-researchers to systematize statistical understanding of RDS. The project is innovative in that it challenges the existing paradigm of RDS inference and proposes a new approach based on cutting edge statistical ideas and models. There is a dearth of statistical methodology justifying RDS. This project will produce a systematic statistical framework within which to understand the strengths and weaknesses of RDS. In the proposed work we will represent the complexities of the RDS procedure, but also allow the uncertainty of the resulting inference to be quantified. The development of statistical methodology for RDS is of vital importance to the social and behavioral sciences. We will disseminate the methodology for estimation, diagnostics and quantification of uncertainty via open-source user-friendly software aimed at field researchers. These will be applicable to both future RDS data, and the large existing data bases of RDS surveys.

Public Health Relevance

Respondent Driven Sampling (RDS) is arguably the best and most common method being used to survey """"""""hidden"""""""" populations that are hard to sample using standard probability methods. Examples include injection drug users (IDU), men who have sex with men (MSM), and female sex workers. Infections amongst just two hidden populations (MSM, IDUs) accounted for the estimated 69% of 53,600 new HIV infections in the US during 2006 (CDC report, Hall, 2008). The purpose of this grant is to improve these kinds of estimates as current estimation practices are known to be problematic.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21HD063000-01
Application #
7774481
Study Section
Special Emphasis Panel (ZRG1-AARR-F (50))
Program Officer
Newcomer, Susan
Project Start
2010-09-01
Project End
2012-08-31
Budget Start
2010-09-01
Budget End
2012-08-31
Support Year
1
Fiscal Year
2010
Total Cost
$147,001
Indirect Cost
Name
University of California Los Angeles
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
092530369
City
Los Angeles
State
CA
Country
United States
Zip Code
90095
Wesson, Paul; Handcock, Mark S; McFarland, Willi et al. (2015) If You Are Not Counted, You Don't Count: Estimating the Number of African-American Men Who Have Sex with Men in San Francisco Using a Novel Bayesian Approach. J Urban Health 92:1052-64
Gile, Krista J; Handcock, Mark S (2015) Network Model-Assisted Inference from Respondent-Driven Sampling Data. J R Stat Soc Ser A Stat Soc 178:619-639
Admiraal, Ryan; Handcock, Mark S (2015) A log-linear modelling approach to assessing the consistency of ego reports of dyadic outcomes with applications to fertility and sexual partnerships. J R Stat Soc Ser A Stat Soc 178:363-382
Handcock, Mark S; Gile, Krista J; Mar, Corinne M (2015) Estimating the size of populations at high risk for HIV using respondent-driven sampling data. Biometrics 71:258-266
Schweinberger, Michael; Handcock, Mark S (2015) Local dependence in random graph models: characterization, properties and statistical inference. J Am Stat Assoc 77:647-676
Johnston, Lisa G; McLaughlin, Katherine R; El Rhilani, Houssine et al. (2015) Estimating the Size of Hidden Populations Using Respondent-driven Sampling Data: Case Examples from Morocco. Epidemiology 26:846-52
Handcock, Mark S; Gile, Krista J; Mar, Corinne M (2014) Estimating hidden population size using Respondent-Driven Sampling data. Electron J Stat 8:1491-1521
Chi, Jocelyn T; Handcock, Mark S (2014) Identifying Sources of Health Care Underutilization Among California's Immigrants. J Racial Ethn Health Disparities 1:207-218
Krivitsky, Pavel N; Handcock, Mark S (2014) A Separable Model for Dynamic Networks. J R Stat Soc Series B Stat Methodol 76:29-46
Hunter, David R; Goodreau, Steven M; Handcock, Mark S (2013) ergm.userterms: A Template Package for Extending statnet. J Stat Softw 52:i02

Showing the most recent 10 out of 18 publications