A review of reported network degree and recruitment characteristics in respondent driven sampling implications for applied researchers and methodologists.
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ABSTRACT: Objective
Respondent driven sampling (RDS) is an important tool for measuring disease prevalence in populations with no sampling frame. We aim to describe key properties of these samples to guide those using this method and to inform methodological research.Methods
In 2019, authors who published respondent driven sampling studies were contacted with a request to share reported degree and network information. Of 59 author groups identified, 15 (25%) agreed to share data, representing 53 distinct study samples containing 36,547 participants across 12 countries and several target populations including migrants, sex workers and men who have sex with men. Distribution of reported network degree was described for each sample and characteristics of recruitment chains, and their relationship to coupons, were reported.Results
Reported network degree is severely skewed and is best represented by a log normal distribution. For participants connected to more than 15 other people, reported degree is imprecise and frequently rounded to the nearest five or ten. Our results indicate that many samples contain highly connected individuals, who may be connected to at least 1000 other people.Conclusion
Because very large reported degrees are common; we caution against treating these reports as outliers. The imprecise and skewed distribution of the reported degree should be incorporated into future RDS methodological studies to better capture real-world performance. Previous results indicating poor performance of regression estimators using RDS weights may be widely generalizable. Fewer recruitment coupons may be associated with longer recruitment chains.
SUBMITTER: Avery L
PROVIDER: S-EPMC8049306 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundRespondent Driven Sampling (RDS) is a network or chain sampling method designed to access individuals from hard-to-reach populations such as people who inject drugs (PWID). RDS surveys are used to monitor behaviour and infection occurence over time; these estimations require adjusting to account for over-sampling of individuals with many contacts. Adjustment is done based on individuals' reported total number of contacts, assuming these are correct.MethodsData on the number of contacts (degrees) of individuals sampled in two RDS surveys in Bristol, UK, show larger numbers of individuals reporting numbers of contacts in multiples of 5 and 10 than would be expected at random. To mimic these patterns we generate contact networks and explore different methods of mis-reporting degrees. We simulate RDS surveys and explore the sensitivity of adjusted estimates to these different methods.ResultsWe find that inaccurate reporting of degrees can cause large and variable bias in estimates of prevalence or incidence. Our simulations imply that paired RDS surveys could over- or under-estimate any change in prevalence by as much as 25%. These are particularly sensitive to inaccuracies in the degree estimates of individuals with who have low degree.ConclusionsThere is a substantial risk of bias in estimates from RDS if degrees are not correctly reported. This is particularly important when analysing consecutive RDS samples to assess trends in population prevalence and behaviour. RDS questionnaires should be refined to obtain high resolution degree information, particularly from low-degree individuals. Additionally, larger sample sizes can reduce uncertainty in estimates.
| S-EPMC4150985 | biostudies-literature
Project description:This article reports on the use of respondent-driven sampling (RDS) in participatory and community-based research. Participant-driven recruitment (PDR) retains all of the analytic capabilities of RDS while enhancing the role of respondents in framing research questions, instrument development, data interpretation, and other aspects of the research process. Merging the capabilities of RDS with participatory research methods, PDR creates new opportunities for engaging community members in research addressing social issues and in utilizing research findings within community contexts. This article outlines PDR's synthesis of RDS and participatory research approaches, describes how PDR is implemented in community contexts, and provides two examples of the use of PDR, illustrating its process, potentials, and challenges.
| S-EPMC1705484 | biostudies-other
Project description:Respondent-driven sampling (RDS) is a link-tracing procedure used in epidemiologic research on hidden or hard-to-reach populations in which subjects recruit others via their social networks. Estimates from RDS studies may have poor statistical properties due to statistical dependence in sampled subjects' traits. Two distinct mechanisms account for dependence in an RDS study: homophily, the tendency for individuals to share social ties with others exhibiting similar characteristics, and preferential recruitment, in which recruiters do not recruit uniformly at random from their network alters. The different effects of network homophily and preferential recruitment in RDS studies have been a source of confusion and controversy in methodological and empirical research in epidemiology. In this work, we gave formal definitions of homophily and preferential recruitment and showed that neither is identified in typical RDS studies. We derived nonparametric identification regions for homophily and preferential recruitment and showed that these parameters were not identified unless the network took a degenerate form. The results indicated that claims of homophily or recruitment bias measured from empirical RDS studies may not be credible. We applied our identification results to a study involving both a network census and RDS on a population of injection drug users in Hartford, Connecticut (2012-2013).
| S-EPMC5860647 | biostudies-literature
Project description:Respondent-driven sampling (RDS) is a network-based technique for estimating traits in hard-to-reach populations, for example, the prevalence of HIV among drug injectors. In recent years RDS has been used in more than 120 studies in more than 20 countries and by leading public health organizations, including the Centers for Disease Control and Prevention in the United States. Despite the widespread use and growing popularity of RDS, there has been little empirical validation of the methodology. Here we investigate the performance of RDS by simulating sampling from 85 known, network populations. Across a variety of traits we find that RDS is substantially less accurate than generally acknowledged and that reported RDS confidence intervals are misleadingly narrow. Moreover, because we model a best-case scenario in which the theoretical RDS sampling assumptions hold exactly, it is unlikely that RDS performs any better in practice than in our simulations. Notably, the poor performance of RDS is driven not by the bias but by the high variance of estimates, a possibility that had been largely overlooked in the RDS literature. Given the consistency of our results across networks and our generous sampling conditions, we conclude that RDS as currently practiced may not be suitable for key aspects of public health surveillance where it is now extensively applied.
| S-EPMC2872407 | biostudies-literature
Project description:BackgroundWeb-based respondent-driven sampling is a novel sampling method for the recruitment of participants for generating population estimates, studying social network characteristics, and delivering health interventions. However, the application, barriers and facilitators, and recruitment performance of web-based respondent-driven sampling have not yet been systematically investigated.ObjectiveOur objectives were to provide an overview of published research using web-based respondent-driven sampling and to investigate factors related to the recruitment performance of web-based respondent-driven sampling.MethodsWe conducted a scoping review on web-based respondent-driven sampling studies published between 2000 and 2019. We used the process evaluation of complex interventions framework to gain insights into how web-based respondent-driven sampling was implemented, what mechanisms of impact drove recruitment, what the role of context was in the study, and how these components together influenced the recruitment performance of web-based respondent-driven sampling.ResultsWe included 18 studies from 8 countries (high- and low-middle income countries), in which web-based respondent-driven sampling was used for making population estimates (n=12), studying social network characteristics (n=3), and delivering health-related interventions (n=3). Studies used web-based respondent-driven sampling to recruit between 19 and 3448 participants from a variety of target populations. Studies differed greatly in the number of seeds recruited, the proportion of successfully recruiting participants, the number of recruitment waves, the type of incentives offered to participants, and the duration of data collection. Studies that recruited relatively more seeds, through online platforms, and with less rigorous selection procedures reported relatively low percentages of successfully recruiting seeds. Studies that did not offer at least one guaranteed material incentive reported relatively fewer waves and lower percentages of successfully recruiting participants. The time of data collection was shortest in studies with university students.ConclusionsWeb-based respondent-driven sampling can be successfully applied to recruit individuals for making population estimates, studying social network characteristics, and delivering health interventions. In general, seed and peer recruitment may be enhanced by rigorously selecting and motivating seeds, offering at least one guaranteed material incentive, and facilitating adequate recruitment options regarding the target population's online connectedness and communication behavior. Potential trade-offs should be taken into account when implementing web-based respondent-driven sampling, such as having less opportunities to implement rigorous seed selection procedures when recruiting many seeds, as well as issues around online rather than physical participation, such as the risk of cheaters participating repeatedly.
| S-EPMC7846441 | biostudies-literature
Project description:Respondent-driven sampling is a novel variant of link-tracing sampling for estimating the characteristics of hard-to-reach groups, such as HIV prevalence in sex workers. Despite its use by leading health organizations, the performance of this method in realistic situations is still largely unknown. We evaluated respondent-driven sampling by comparing estimates from a respondent-driven sampling survey with total population data.Total population data on age, tribe, religion, socioeconomic status, sexual activity, and HIV status were available on a population of 2402 male household heads from an open cohort in rural Uganda. A respondent-driven sampling (RDS) survey was carried out in this population, using current methods of sampling (RDS sample) and statistical inference (RDS estimates). Analyses were carried out for the full RDS sample and then repeated for the first 250 recruits (small sample).We recruited 927 household heads. Full and small RDS samples were largely representative of the total population, but both samples underrepresented men who were younger, of higher socioeconomic status, and with unknown sexual activity and HIV status. Respondent-driven sampling statistical inference methods failed to reduce these biases. Only 31%-37% (depending on method and sample size) of RDS estimates were closer to the true population proportions than the RDS sample proportions. Only 50%-74% of respondent-driven sampling bootstrap 95% confidence intervals included the population proportion.Respondent-driven sampling produced a generally representative sample of this well-connected nonhidden population. However, current respondent-driven sampling inference methods failed to reduce bias when it occurred. Whether the data required to remove bias and measure precision can be collected in a respondent-driven sampling survey is unresolved. Respondent-driven sampling should be regarded as a (potentially superior) form of convenience sampling method, and caution is required when interpreting findings based on the sampling method.
| S-EPMC3277908 | biostudies-literature
Project description:Respondent-driven sampling (RDS) is a widely used method for sampling from hard-to-reach human populations, especially populations at higher risk for HIV. Data are collected through peer-referral over social networks. RDS has proven practical for data collection in many difficult settings and is widely used. Inference from RDS data requires many strong assumptions because the sampling design is partially beyond the control of the researcher and partially unobserved. We introduce diagnostic tools for most of these assumptions and apply them in 12 high risk populations. These diagnostics empower researchers to better understand their data and encourage future statistical research on RDS.
| S-EPMC4877136 | biostudies-literature
Project description:IntroductionRespondent-driven sampling (RDS) is a variant of a link-tracing design intended for generating unbiased estimates of the composition of hidden populations that typically involves giving participants several coupons to recruit their peers into the study. RDS may generate biased estimates if coupons are distributed non-randomly or if potential recruits present for interview non-randomly. We explore if biases detected in an RDS study were due to either of these mechanisms, and propose and apply weights to reduce bias due to non-random presentation for interview.MethodsUsing data from the total population, and the population to whom recruiters offered their coupons, we explored how age and socioeconomic status were associated with being offered a coupon, and, if offered a coupon, with presenting for interview. Population proportions were estimated by weighting by the assumed inverse probabilities of being offered a coupon (as in existing RDS methods), and also of presentation for interview if offered a coupon by age and socioeconomic status group.ResultsYounger men were under-recruited primarily because they were less likely to be offered coupons. The under-recruitment of higher socioeconomic status men was due in part to them being less likely to present for interview. Consistent with these findings, weighting for non-random presentation for interview by age and socioeconomic status group greatly improved the estimate of the proportion of men in the lowest socioeconomic group, reducing the root-mean-squared error of RDS estimates of socioeconomic status by 38%, but had little effect on estimates for age. The weighting also improved estimates for tribe and religion (reducing root-mean-squared-errors by 19-29%), but had little effect for sexual activity or HIV status.ConclusionsData collected from recruiters on the characteristics of men to whom they offered coupons may be used to reduce bias in RDS studies. Further evaluation of this new method is required.
| S-EPMC3814964 | biostudies-literature
Project description:BackgroundRespondent-driven sampling(RDS) is an increasingly widely used variant of a link tracing design for recruiting hidden populations. The role of the spatial distribution of the target population has not been robustly examined for RDS. We examine patterns of recruitment by location, and how they may have biased an RDS study findings.MethodsTotal-population data were available on a range of characteristics on a population of 2402 male household-heads from an open cohort of 25 villages in rural Uganda. The locations of households were known a-priori. An RDS survey was carried out in this population, employing current RDS methods of sampling and statistical inference.ResultsThere was little heterogeneity in the population by location. Data suggested more distant contacts were less likely to be reported, and therefore recruited, but if reported more distant contacts were as likely as closer contacts to be recruited. There was no evidence that closer proximity to a village meeting place was associated with probability of being recruited, however it was associated with a higher probability of recruiting a larger number of recruits. People living closer to an interview site were more likely to be recruited.ConclusionsHousehold location affected the overall probability of recruitment, and the probability of recruitment by a specific recruiter. Patterns of recruitment do not appear to have greatly biased estimates in this study. The observed patterns could result in bias in more geographically heterogeneous populations. Care is required in RDS studies when choosing the network size question and interview site location(s).
| S-EPMC3215957 | biostudies-literature
Project description:Respondent-driven sampling (RDS) is a chain-referral method for sampling members of hidden or hard-to-reach populations, such as sex workers, homeless people, or drug users, via their social networks. Most methodological work on RDS has focused on inference of population means under the assumption that subjects' network degree determines their probability of being sampled. Criticism of existing estimators is usually focused on missing data: the underlying network is only partially observed, so it is difficult to determine correct sampling probabilities. In this article, the author shows that data collected in ordinary RDS studies contain information about the structure of the respondents' social network. The author constructs a continuous-time model of RDS recruitment that incorporates the time series of recruitment events, the pattern of coupon use, and the network degrees of sampled subjects. Together, the observed data and the recruitment model place a well-defined probability distribution on the recruitment-induced subgraph of respondents. The author shows that this distribution can be interpreted as an exponential random graph model and develops a computationally efficient method for estimating the hidden graph. The author validates the method using simulated data and applies the technique to an RDS study of injection drug users in St. Petersburg, Russia.
| S-EPMC6788810 | biostudies-literature