ArticleJournal of the American Statistical Association2018
Hidden population size estimation from respondent-driven sampling: a network approach.
Article in Journal of the American Statistical Association, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Review of Network Models for HIV Spread.Journal of acquired immune deficiency syndromes (1999) · 2025Pooled it
- The Right to Be Counted: Unmasking First Nations and Métis Population Undercounts in Winnipeg, Manitoba.International journal of environmental research and public health · 2026Article
- Sex disparities and partners' positive HIV status in HIV recent infection among people who inject drugs in Ukraine.AIDS (London, England) · 2026Article
- Sexual health and sexual behaviours in Chinese women of varied sexual identities: a sequential mixed methods study.Sexual and reproductive health matters · 2025Article
- Optimal provision of opiate substitution therapy and needle and syringe programmes: a multi-method realist evaluation.Health technology assessment (Winchester, England) · 2025Article
- Temporal trends in HIV prevention service access and use among female sex workers (FSW) in Mozambique: a comparative analysis 2011-2019.BMC public health · 2025Article
- Exploring Social-Ecological Pathways From Sexual Identity to Sleep Among Chinese Women: Structural Equation Modeling Analysis.JMIR public health and surveillance · 2025Article
- Comparing the Robustness of Simple Network Scale-Up Method Estimators.Sociological methodology · 2024Article
- Estimation of the prevalence of opioid misuse in New York State counties, 2007-2018: a bayesian spatiotemporal abundance model approach.American journal of epidemiology · 2024Article
- Hidden Population Estimation with Indirect Inference and Auxiliary Information.Proceedings of machine learning research · 2024Article
- Article
- Using the network scale-up method to characterise kidney trafficking in Kalai Upazila, Bangladesh.BMJ global health · 2023Article
- Dependence-Robust Confidence Intervals for Capture-Recapture Surveys.Journal of survey statistics and methodology · 2023Article
- Quality of Life and Mental Health of Chinese Sexual and Gender Minority Women and Cisgender Heterosexual Women: Cross-sectional Survey and Mediation Analysis.JMIR public health and surveillance · 2023Article
- Methods for Assessing Spillover in Network-Based Studies of HIV/AIDS Prevention among People Who Use Drugs.Pathogens (Basel, Switzerland) · 2023Article
- An integrated abundance model for estimating county-level prevalence of opioid misuse in Ohio.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2023Article
- What is the prevalence of and trend in opioid use disorder in the United States from 2010 to 2019? Using multiplier approaches to estimate prevalence for an unknown population size.Drug and alcohol dependence reports · 2022Article
- Impact of Survey Design on Estimation of Exponential-Family Random Graph Models from Egocentrically-Sampled Data.Social networks · 2022Article
- Using geographical data and rolling statistics for diagnostics of respondent-driven sampling.Social networks · 2022Article
- Molecular and spatial epidemiology of HCV among people who inject drugs in Boston, Massachusetts.PloS one · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Estimating the size of stigmatized, hidden, or hard-to-reach populations is a major problem in epidemiology, demography, and public health research. Capture-recapture and multiplier methods are standard tools for inference of hidden population sizes, but they require random sampling of target population members, which is rarely possible. Respondent-driven sampling (RDS) is a survey method for hidden populations that relies on social link tracing. The RDS recruitment process is designed to spread through the social network connecting members of the target population. In this paper, we show how to use network data revealed by RDS to estimate hidden population size. The key insight is that the recruitment chain, timing of recruitments, and network degrees of recruited subjects provide information about the number of individuals belonging to the target population who are not yet in the sample. We use a computationally efficient Bayesian method to integrate over the missing edges in the subgraph of recruited individuals. We validate the method using simulated data and apply the technique to estimate the number of people who inject drugs in St. Petersburg, Russia.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.