Identification of Homophily and Preferential Recruitment in Respondent-Driven Sampling

Pathways to racial disparities in the effects of Good Samaritan Laws: A mixed methods pilot study

BACKGROUND

Drug overdose deaths continue to rise, and considerable racial inequities have emerged. Overdose Good Samaritan laws (GSLs) are intended to encourage overdose witnesses to seek emergency assistance. However, evidence of their effectiveness is mixed, and little is known regarding racial disparities in their implementation. This study examined GSL impact by assessing racial differences in awareness of and trust in New York state's GSL.

METHODS

Using a sequential mixed methods design, Black and white participants were recruited from an existing longitudinal cohort study of people who use illicit opioids in New York City to participate in a quantitative survey and qualitative interviews. Racially stratified survey responses were analyzed using chi-squared tests, Fisher exact tests, or t-tests. Qualitative interviews were analyzed using a hybrid inductive-deductive approach.

RESULTS

Participants (n=128) were 56% male and predominantly aged 50 years or older. Most met criteria for severe opioid use disorder (81%). Fifty-seven percent reported that the New York GSL makes them more likely to call 911 even though 42% reported not trusting law enforcement to abide by the GSL; neither differed by race. Black people were less likely to have heard of the GSL (36.1% vs 60%) and were less likely to have accurate information regarding its protections (40.4% vs 49.6%).

CONCLUSIONS

Though GSLs may reduce negative impacts of the criminalization of people who use drugs, their implementation may exacerbate existing racial disparities. Resources should be directed towards harm reduction strategies that do not rely on trust in law enforcement.

Hidden population size estimation and diagnostics using two respondent-driven samples with applications in Armenia

Estimating the size of hidden populations is essential to understand the magnitude of social and healthcare needs, risk behaviors, and disease burden. However, due to the hidden nature of these populations, they are difficult to survey, and there are no gold standard size estimation methods. Many different methods and variations exist, and diagnostic tools are needed to help researchers assess method-specific assumptions as well as compare between methods. Further, because many necessary mathematical assumptions are unrealistic for real survey implementation, assessment of how robust methods are to deviations from the stated assumptions is essential. We describe diagnostics and assess the performance of a new population size estimation method, capture-recapture with successive sampling population size estimation (CR-SS-PSE), which we apply to data from 3 years of studies from three cities and three hidden populations in Armenia. CR-SS-PSE relies on data from two sequential respondent-driven sampling surveys and extends the successive sampling population size estimation (SS-PSE) framework by using the number of individuals in the overlap between the two surveys and a model for the successive sampling process to estimate population size. We demonstrate that CR-SS-PSE is more robust to violations of successive sampling assumptions than SS-PSE. Further, we compare the CR-SS-PSE estimates to population size estimations using other common methods, including unique object and service multipliers, wisdom of the crowd, and two-source capture-recapture to illustrate volatility across estimation methods.

Integration of a geospatially targeted community-based testing approach with respondent-driven sampling to identify people who inject drugs living with HIV and HCV in Patti and Gorakhpur, India

BACKGROUND

Respondent-driven sampling (RDS), a network recruitment approach, is effective at reaching people who inject drugs (PWID), but other strategies may be needed to reach PWID at risk or living with HIV and/or Hepatitis C (HCV). We examined the impact of integrating geospatially targeted community-based HIV/HCV testing with an RDS survey.

METHODS

PWID were recruited between 2019 and 2021 in Patti and Gorakhpur, India, in a two-phased approach for identifying PWID living with HIV/HCV. Phase 1 was an RDS survey, in which participants reported injection venues. Venues with the highest prevalence of HIV/HCV viremia were selected for Phase 2: community-based testing. All participants underwent rapid HIV and HCV testing and viral load quantification. Using Pearson's chi-squared test, two-sided exact significance tests, and t-tests, we compared prevalence and identification rates for each of the primary outcomes: the number of PWID 1) living with HIV/HCV, 2) undiagnosed, and 3) viremic.

RESULTS

Both approaches identified large numbers of PWID (n∼500 each; N=2011) who were living with HIV/HCV and had transmission potential (i.e., detectable viremia). The community-based approach identified a higher proportion of individuals living with HCV (76.4% vs. 69.6% in Gorakhpur and 36.3% vs. 29.0% in Patti). Community-based testing was also faster at identifying PWID with detectable HIV viremia. Both approaches identified PWID with varying demographic characteristics.

CONCLUSIONS

Community-based testing was more efficient than RDS overall, but both may be required to reach PWID of varying characteristics. Surveillance should collect data on injection venues to facilitate community-based testing and maximize case identification.

Methods for Assessing Spillover in Network-Based Studies of HIV/AIDS Prevention among People Who Use Drugs

Human Immunodeficiency Virus (HIV) interventions among people who use drugs (PWUD) often have spillover, also known as interference or dissemination, which occurs when one participant's exposure affects another participant's outcome. PWUD are often members of networks defined by social, sexual, and drug-use partnerships and their receipt of interventions can affect other members in their network. For example, HIV interventions with possible spillover include educational training about HIV risk reduction, pre-exposure prophylaxis, or treatment as prevention. In turn, intervention effects frequently depend on the network structure, and intervention coverage levels and spillover can occur even if not measured in a study, possibly resulting in an underestimation of intervention effects. Recent methodological approaches were developed to assess spillover in the context of network-based studies. This tutorial provides an overview of different study designs for network-based studies and related methodological approaches for assessing spillover in each design. We also provide an overview of other important methodological issues in network studies, including causal influence in networks and missing data. Finally, we highlight applications of different designs and methods from studies of PWUD and conclude with an illustrative example from the Transmission Reduction Intervention Project (TRIP) in Athens, Greece.

Sociodemographic characteristics and HIV risk behaviors of native-born and displaced Syrian men and transgender women who have sex with men in Lebanon

HIV rates among men and transgender women who have sex with men (MTWSM) in Lebanon are consistent with a concentrated epidemic. Geopolitical and social circumstances leave these communities vulnerable to HIV spread. To document this risk encountered by Lebanese native and displaced Syrian MTWSM, participants, recruited by respondent driven sampling beginning with Syrian seeds, completed a survey with questions covering sociodemographic, behavioral, medical, and stigma, followed by opt-out HIV testing. Analyses included descriptive statistics and linear regression to differentiate between native Lebanese and Syrians who migrated after the onset of the civil war to identify correlations among sociodemographic factors, stigma, and risk behavior as a function of country of birth. Experienced and internalized stigmas were higher in the Syrian born MTWSM and correlated with elements of HIV risk. Combatting the intersectional stigmas of Syrian MTWSM in Lebanon would be most beneficial in mitigating HIV risk for these individuals.

Preserving Multiple Homophilies in a Network Configuration Model

Respondent-driven sampling (RDS) is a popular method for surveying hidden populations based on friendships and existing social network connections. In such a survey the underlying hidden network remains largely unknown. However, it is useful to estimate its size as well as the relative proportions of surveyed features. The fact that linked network participants are likely to share common features is called homophily, and is an important property in understanding the topology of social networks. In this paper we present a methodology that scales up RDS data to model the underlying hidden population in a way that preserves multiple homophilies among different features. We test our model using 46 features of the population sampled by the SATHCAP RDS survey. Our network generation methodology successfully preserves the homophilic associations in a randomly generated Barabasi-Albert network. Having created a realistic model of the expanded SATHCAP network, we test our model by simulating RDS surveys over it, and comparing the resulting sub-networks with SATHCAP. In our generated network, we preserve 85% of homophilies to under 2% error. In our simulated RDS surveys we preserve 85% of homophilies to under 15% error.

Violence and Discrimination Against Men Who Have Sex With Men in Lebanon: The Role of International Displacement and Migration

Homosexuality is illegal in Lebanon and men who have sex with men (MSM) may experience discrimination. Displaced Syrians, who currently comprise approximately 20% of Lebanon's population, also face discrimination. Individuals who are members of both groups may experience heightened levels of discrimination and abuse. In partnership with local nongovernmental organizations serving the community, we recruited N = 292 MSM in Beirut, Lebanon. Participants were interviewed about experiences of violence and discrimination in the context of a larger health behavior survey, and all were offered anonymous HIV testing. Responses were analyzed using the framework of intersectionality, combining regression, geographical mapping of reported experiences, and network analysis of the participant recruitment pattern. MSM, born outside of Lebanon, who are primarily from Syria, face higher levels of discrimination and violence than native-born MSM (71% vs. 32% reporting at least one type of discrimination or violence). Socioeconomic status is also associated with discrimination and violence overall, and among native- and foreign-born MSM. Experiences vary by town and neighborhood, and are highly correlated between recruiting and recruited participants.These results highlight health risks faced by foreign-born MSM in Lebanon.

A Bayesian framework for modelling the preferential selection process in respondent-driven sampling

In sampling designs that utilize peer recruitment, the sampling process is partially unknown and must be modelled to make inference about the population and estimate standard outcomes like prevalence. We develop a Bayesian model for the recruitment process for respondent-driven sampling (RDS), a network sampling methodology used worldwide to sample hidden populations that are not reachable by conventional sampling techniques, including those at high risk for HIV/AIDS. Current models for the RDS sampling process typically assume that recruitment occurs randomly given the population social network, but this is likely untrue in practice. To model preferential selection on covariates, we develop a sequential two-sided rational choice framework, which allows generative probabilistic network models to be created for the RDS sampling process. In the rational choice framework, members of the population make recruitment and participation choices based on observable nodal and dyadic covariates to maximize their utility given constraints. Inference is made about recruitment preferences given the observed recruitment chain in a Bayesian framework by incorporating the latent utilities and sampling from the joint posterior distribution via Markov chain Monte Carlo. We present simulation results and apply the model to an RDS study of Francophone migrants in Rabat, Morocco.

General regression methods for respondent-driven sampling data

Respondent-driven sampling is a variant of link-tracing sampling techniques that aim to recruit hard-to-reach populations by leveraging individuals’ social relationships. As such, a respondent-driven sample has a graphical component which represents a partially observed network of unknown structure. Moreover, it is common to observe homophily, or the tendency to form connections with individuals who share similar traits. Currently, there is a lack of principled guidance on multivariate modelling strategies for respondent-driven sampling to address peer effects driven by homophily and the dependence between observations within the network. In this work, we propose a methodology for general regression techniques using respondent-driven sampling data. This is used to study the socio-demographic predictors of HIV treatment optimism (about the value of antiretroviral therapy) among gay, bisexual and other men who have sex with men, recruited into a respondent-driven sampling study in Montreal, Canada.

A comparison of the effectiveness of respondent-driven and venue-based sampling for identifying undiagnosed HIV infection among cisgender men who have sex with men and transgender women in Tijuana, Mexico

BACKGROUND

Efforts to increase HIV testing, diagnosis and care are critical to curbing HIV epidemics among cisgender men who have sex with men (MSM) and transgender women (TW) in low- and middle-income countries (LMIC). We compared the effectiveness of respondent-driven sampling (RDS) and venue-based sampling (VBS) for identifying previously undiagnosed HIV infection among MSM and TW in Tijuana, Mexico.

METHODS

Between March 2015 and December 2018, we conducted RDS within the social networks of MSM and TW and VBS at venues frequented by MSM and TW to socialize and meet sexual partners. Those reached by RDS/VBS who reported at least 18 years of age, anal sex with MSM or TW, and no previous HIV diagnosis were eligible for HIV testing.

RESULTS

Of those screened following recruitment via RDS (N = 1232; 98.6% MSM; 1.3% TW), 60.8% (749/1232) were eligible for HIV testing and 97.5% (730/749) were tested for HIV infection, which led to the identification of 36 newly diagnosed HIV infections (4.9%). Of those screened following recruitment via VBS (N = 2560; 95.2% MSM; 4.6% TW), 56.5% (1446/2560) were eligible for HIV testing and 92.8% (1342/1446) were tested for HIV infection, which led to the identification of 82 newly diagnosed HIV infections (6.1%). The proportion of new HIV diagnoses did not differ by recruitment method (ratio = 0.81, 95% confidence interval: 0.55 to 1.18). Compared to those recruited via RDS, those tested following recruitment via VBS were younger, more likely to identify as gay, and more likely to identify as TW. Compared to those recruited via VBS, those newly diagnosed with HIV infection following recruitment via RDS reported higher levels of internalized stigma and were more likely to report injection drug use and a history of deportation from the United States.

CONCLUSIONS

Despite RDS and VBS being equally effective for identifying undiagnosed HIV infection, each recruitment method reached different subgroups of MSM and TW in Tijuana. Our findings suggest that there may be benefits to using both RDS and VBS to increase the identification of previously undiagnosed HIV infection and ultimately support HIV care engagement among MSM and TW in Mexico and other similar LMIC.

Estimation and correction of bias in network simulations based on respondent-driven sampling data

Respondent-driven sampling (RDS) is widely used for collecting data on hard-to-reach populations, including information about the structure of the networks connecting the individuals. Characterizing network features can be important for designing and evaluating health programs, particularly those that involve infectious disease transmission. While the validity of population proportions estimated from RDS-based datasets has been well studied, little is known about potential biases in inference about network structure from RDS. We developed a mathematical and statistical platform to simulate network structures with exponential random graph models, and to mimic the data generation mechanisms produced by RDS. We used this framework to characterize biases in three important network statistics – density/mean degree, homophily, and transitivity. Generalized linear models were used to predict the network statistics of the original network from the network statistics of the sample network and observable sample design features. We found that RDS may introduce significant biases in the estimation of density/mean degree and transitivity, and may exaggerate homophily when preferential recruitment occurs. Adjustments to network-generating statistics derived from the prediction models could substantially improve validity of simulated networks in terms of density, and could reduce bias in replicating mean degree, homophily, and transitivity from the original network.

Model-based Respondent-driven sampling analysis for HIV prevalence in brazilian MSM

Respondent Driven Sampling study (RDS) is a population sampling method developed to study hard-to-reach populations. A sample is obtained by chain-referral recruitment in a network of contacts within the population of interest. Such self-selected samples are not representative of the target population and require weighing observations to reduce estimation bias. Recently, the Network Model-Assisted (NMA) method was described to compute the required weights. The NMA method relies on modeling the underlying contact network in the population where the RDS was conducted, in agreement with directly observable characteristics of the sample such as the number of contacts, but also with more difficult-to-measure characteristics such as homophily or differential characteristics according to the response variable. Here we investigated the use of the NMA method to estimate HIV prevalence from RDS data when information on homophily is limited. We show that an iterative procedure based on the NMA approach allows unbiased estimations even in the case of strong population homophily and differential activity and limits bias in case of preferential recruitment. We applied the methods to determine HIV prevalence in men having sex with men in Brazilian cities and confirmed a high prevalence of HIV in these populations from 3.8% to 22.1%.

Divergent estimates of HIV incidence among people who inject drugs in Ukraine

BACKGROUND

Divergent estimates of HIV incidence among people who inject drugs (PWID) in Ukraine have been reported in modeling studies, longitudinal cohort studies, and recent infection assays used in cross-sectional surveys. Estimates range from 0.65 to 24.8 infections per 100 person-years with substantial regional variation. In this paper, we study the sources of this discrepancy.

METHODS

We compared baseline characteristics of study subjects recruited in the cross-sectional integrated bio-behavioral surveillance surveys (IBBS) in 2011 and 2013, with those from the longitudinal network intervention trial (network RCT) conducted between 2010 - 2013, the study that found a remarkably high incidence of HIV among PWID in Ukraine. The analysis was conducted for two cities: Mykolaiv and Odesa.

RESULTS

Significant differences were found in the characteristics of study subjects recruited in the IBBS surveys and the network RCT, in particular in Odesa, where the mismatch in the estimates of HIV incidence is greatest. In Odesa, recent syringe sharing was about three times as prevalent in the network RCT as in the IBBS; 39% of the network RCT and 16-18% of the IBBS participants indicated stimulants rather than opiates as their drug of choice; 97% of respondents in the network RCT and 45% in the IBBS-2013 reported injecting in a group over half of the time; and the average monthly number of injections in the network RCT was about twice that in the IBBS studies.

CONCLUSIONS

Differences in study designs and sampling methodologies may be responsible for the substantial differences in HIV incidence estimates among PWID in Ukraine. The potential sources of selection bias differed between the studies and likely resulted in the recruitment of lower risk individuals into the IBBS studies compared to the network RCT. Risk stratification in the population of PWID may have implications for future surveillance and intervention efforts.

Causal Inference Under Interference And Network Uncertainty

Classical causal and statistical inference methods typically assume the observed data consists of independent realizations. However, in many applications this assumption is inappropriate due to a network of dependences between units in the data. Methods for estimating causal effects have been developed in the setting where the structure of dependence between units is known exactly [10, 36, 20], but in practice there is often substantial uncertainty about the precise network structure. This is true, for example, in trial data drawn from vulnerable communities where social ties are difficult to query directly. In this paper we combine techniques from the structure learning and interference literatures in causal inference, proposing a general method for estimating causal effects under data dependence when the structure of this dependence is not known a priori. We demonstrate the utility of our method on synthetic datasets which exhibit network dependence.

Empirical evidence of recruitment bias in a network study of people who inject drugs

Background: Epidemiologic surveys of people who inject drugs (PWID) can be difficult to conduct because potential participants may fear exposure or legal repercussions. Respondent-driven sampling (RDS) is a procedure in which subjects recruit their eligible social contacts. The statistical validity of RDS surveys of PWID and other risk groups depends on subjects recruiting at random from among their network contacts. Objectives: We sought to develop and apply a rigorous definition and statistical tests for uniform network recruitment in an RDS survey. Methods: We undertook a detailed study of recruitment bias in a unique RDS study of PWID in Hartford, CT, the USA in which the network, individual-level covariates, and social link attributes were recorded. A total of n=527 participants (402 male, 123 female, and two individuals who did not specify their gender) within a network of 2626 PWID were recruited. Results: We found strong evidence of recruitment bias with respect to age, homelessness, and social relationship characteristics. In the discrete model, the estimated hazard ratios regarding the significant features of recruitment time and choice of recruitee were: alter's age 1.03 [1.02, 1.05], alter's crack-using status 0.70 [0.50, 1.00], homelessness difference 0.61 [0.43, 0.87], and sharing activities in drug preparation 2.82 [1.39, 5.72]. Under both the discrete and continuous-time recruitment regression models, we reject the null hypothesis of uniform recruitment. Conclusions: The results provide the evidence that for this study population of PWID, recruitment bias may significantly alter the sample composition, making results of RDS surveys less reliable. More broadly, RDS studies that fail to collect comprehensive network data may not be able to detect biased recruitment when it occurs.

A SIMULATION-BASED FRAMEWORK FOR ASSESSING THE FEASIBILITY OF RESPONDENT-DRIVEN SAMPLING FOR ESTIMATING CHARACTERISTICS IN POPULATIONS OF LESBIAN, GAY AND BISEXUAL OLDER ADULTS

Respondent-driven sampling (RDS) is a method for sampling from a target population by leveraging social connections. RDS is invaluable to the study of hard-to-reach populations. However, RDS is costly and can be infeasible. RDS is infeasible when RDS point estimators have small effective sample sizes (large design effects) or when RDS interval estimators have large confidence intervals relative to estimates obtained in previous studies or poor coverage. As a result, researchers need tools to assess whether or not estimation of certain characteristics of interest for specific populations is feasible in advance. In this paper, we develop a simulation-based framework for using pilot data-in the form of a convenience sample of aggregated, egocentric data and estimates of subpopulation sizes within the target population-to assess whether or not RDS is feasible for estimating characteristics of a target population. in doing so, we assume that more is known about egos than alters in the pilot data, which is often the case with aggregated, egocentric data in practice. We build on existing methods for estimating the structure of social networks from aggregated, egocentric sample data and estimates of subpopulation sizes within the target population. We apply this framework to assess the feasibility of estimating the proportion male, proportion bisexual, proportion depressed and proportion infected with HIV/AIDS within three spatially distinct target populations of older lesbian, gay and bisexual adults using pilot data from the caring and Aging with Pride Study and the Gallup Daily Tracking Survey. We conclude that using an RDS sample of 300 subjects is infeasible for estimating the proportion male, but feasible for estimating the proportion bisexual, proportion depressed and proportion infected with HIV/AIDS in all three target populations.

Social network methods for HIV case-finding among people who inject drugs in Tajikistan

INTRODUCTION

HIV testing programmes have struggled to reach the most marginalized populations at risk for HIV. Social network methods such as respondent-driven sampling (RDS) and peer-based active case-finding (ACF) may be effective in overcoming barriers to reaching these populations. We compared the client characteristics, proportion testing HIV positive (yield), and number of new cases found through two RDS strategies and an ACF approach to HIV case-finding among people who inject drugs (PWID) in Tajikistan.

METHODS

Routine programme data from adult PWID recruited to testing under the HIV Flagship Project in Tajikistan were analysed to compare client demographic and clinical characteristics across the three approaches. We also compared the number of previously untested clients, the number of new HIV cases found, and the yield across the case-finding strategies, and evaluated predictors of new HIV diagnosis using fixed-effects logistic regression.

RESULTS

From 24 October 2016 to 30 June 2017, Flagship tested 10,300 PWID for HIV, including 2143 under RDS with unrestricted waves (RDS1, yield: 1.5%), 3517 under restricted RDS (RDS2, yield: 2.6%), and 4640 under ACF (yield: 1.5%). Clients recruited under ACF were similar in age (35.8 vs. 36.8) and gender (91% vs. 90% male) to those recruited through RDS, though ACF clients were more likely to report being a first-time tester (85.1% vs. 68.3%, p < 0.001). After controlling for age, sex, previous testing history and accounting for clustering at the site level, we found that clients tested under both RDS1 (aOR: 1.74, 95% CI: 1.04 to 2.90) and RDS2 (aOR: 1.54, 95% CI: 1.11 to 2.15) had higher odds of testing newly positive for HIV relative to clients recruited through ACF. We did not find significant differences in the odds of new HIV infection between those recruited from RDS1 versus RDS2 (aOR: 1.12, 95% CI: 0.67 to 1.86).

CONCLUSIONS

RDS-based interventions resulted in higher yields and overall case-finding, especially when recruitment was restricted. However, ACF identified a higher proportion of first-time testers. To find at least 90% of PWID living with HIV in Tajikistan, it may be necessary to implement multiple case-finding approaches concurrently to maximize testing coverage.

Overlooked Threats to Respondent Driven Sampling Estimators: Peer Recruitment Reality, Degree Measures, and Random Selection Assumption

Intensive sociometric network data were collected from a typical respondent driven sample (RDS) of 528 people who inject drugs residing in Hartford, Connecticut in 2012-2013. This rich dataset enabled us to analyze a large number of unobserved network nodes and ties for the purpose of assessing common assumptions underlying RDS estimators. Results show that several assumptions central to RDS estimators, such as random selection, enrollment probability proportional to degree, and recruitment occurring over recruiter's network ties, were violated. These problems stem from an overly simplistic conceptualization of peer recruitment processes and dynamics. We found nearly half of participants were recruited via coupon redistribution on the street. Non-uniform patterns occurred in multiple recruitment stages related to both recruiter behavior (choosing and reaching alters, passing coupons, etc.) and recruit behavior (accepting/rejecting coupons, failing to enter study, passing coupons to others). Some factors associated with these patterns were also associated with HIV risk.

Social Network Clustering and the Spread of HIV/AIDS Among Persons Who Inject Drugs in 2 Cities in the Philippines

INTRODUCTION

The Philippines has seen rapid increases in HIV prevalence among people who inject drugs. We study 2 neighboring cities where a linked HIV epidemic differed in timing of onset and levels of prevalence. In Cebu, prevalence rose rapidly from below 1% to 54% between 2009 and 2011 and remained high through 2013. In nearby Mandaue, HIV remained below 4% through 2011 then rose rapidly to 38% by 2013.

OBJECTIVES

We hypothesize that infection prevalence differences in these cities may owe to aspects of social network structure, specifically levels of network clustering. Building on previous research, we hypothesize that higher levels of network clustering are associated with greater epidemic potential.

METHODS

Data were collected with respondent-driven sampling among men who inject drugs in Cebu and Mandaue in 2013. We first examine sample composition using estimators for population means. We then apply new estimators of network clustering in respondent-driven sampling data to examine associations with HIV prevalence.

RESULTS

Samples in both cities were comparable in composition by age, education, and injection locations. Dyadic needle-sharing levels were also similar between the 2 cities, but network clustering in the needle-sharing network differed dramatically. We found higher clustering in Cebu than Mandaue, consistent with expectations that higher clustering is associated with faster epidemic spread.

CONCLUSIONS

This article is the first to apply estimators of network clustering to empirical respondent-driven samples, and it offers suggestive evidence that researchers should pay greater attention to network structure's role in HIV transmission dynamics.

NEW SURVEY QUESTIONS AND ESTIMATORS FOR NETWORK CLUSTERING WITH RESPONDENT-DRIVEN SAMPLING DATA

Respondent-driven sampling (RDS) is a popular method for sampling hard-to-survey populations that leverages social network connections through peer recruitment. While RDS is most frequently applied to estimate the prevalence of infections and risk behaviors of interest to public health, such as HIV/AIDS or condom use, it is rarely used to draw inferences about the structural properties of social networks among such populations because it does not typically collect the necessary data. Drawing on recent advances in computer science, we introduce a set of data collection instruments and RDS estimators for network clustering, an important topological property that has been linked to a network's potential for diffusion of information, disease, and health behaviors. We use simulations to explore how these estimators, originally developed for random walk samples of computer networks, perform when applied to RDS samples with characteristics encountered in realistic field settings that depart from random walks. In particular, we explore the effects of multiple seeds, without replacement versus with replacement, branching chains, imperfect response rates, preferential recruitment, and misreporting of ties. We find that clustering coefficient estimators retain desirable properties in RDS samples. This paper takes an important step toward calculating network characteristics using nontraditional sampling methods, and it expands the potential of RDS to tell researchers more about hidden populations and the social factors driving disease prevalence.