IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0207507.html
   My bibliography  Save this article

A stochastic simulation model to study respondent-driven recruitment

Author

Listed:
  • Mart L Stein
  • Vincent Buskens
  • Peter G M van der Heijden
  • Jim E van Steenbergen
  • Albert Wong
  • Martin C J Bootsma
  • Mirjam E E Kretzschmar

Abstract

Respondent-driven detection is a chain recruitment method used to sample contact persons of infected persons in order to enhance case finding. It starts with initial individuals, so-called seeds, who are invited for participation. Afterwards, seeds receive a fixed number of coupons to invite individuals with whom they had contact during a specific time period. Recruitees are then asked to do the same, resulting in successive waves of contact persons who are connected in one recruitment tree. However, often the majority of participants fail to invite others, or invitees do not accept an invitation, and recruitment stops after several waves. A mathematical model can help to analyse how various factors influence peer recruitment and to understand under which circumstances sustainable recruitment is possible. We implemented a stochastic simulation model, where parameters were suggested by empirical data from an online survey, to determine the thresholds for obtaining large recruitment trees and the number of waves needed to reach a steady state in the sample composition for individual characteristics. We also examined the relationship between mean and variance of the number of invitations sent out by participants and the probability of obtaining a large recruitment tree. Our main finding is that a situation where participants send out any number of coupons between one and the maximum number is more effective in reaching large recruitment trees, compared to a situation where the majority of participants does not send out any invitations and a smaller group sends out the maximum number of invitations. The presented model is a helpful tool that can assist public health professionals in preparing research and contact tracing using online respondent-driven detection. In particular, it can provide information on the required minimum number of successfully sent invitations to reach large recruitment trees, a certain sample composition or certain number of waves.

Suggested Citation

  • Mart L Stein & Vincent Buskens & Peter G M van der Heijden & Jim E van Steenbergen & Albert Wong & Martin C J Bootsma & Mirjam E E Kretzschmar, 2018. "A stochastic simulation model to study respondent-driven recruitment," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-19, November.
    • Handle: RePEc:plo:pone00:0207507
    DOI: 10.1371/journal.pone.0207507
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0207507
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0207507&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0207507?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Gile, Krista J., 2011. "Improved Inference for Respondent-Driven Sampling Data With Application to HIV Prevalence Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 135-146.
    2. Stein, M.L. & Van Steenbergen, J.E. & Buskens, V. & Van Der Heijden, P.G.M. & Koppeschaar, C.E. & Bengtsson, L. & Thorson, A. & Kretzschmar, M.E.E., 2015. "Enhancing syndromic surveillance with online respondent-driven detection," American Journal of Public Health, American Public Health Association, vol. 105(8), pages 90-97.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ian E. Fellows & Mark S. Handcock, 2023. "Modeling of networked populations when data is sampled or missing," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 21-35, April.
    2. Fatemi, Samira & Salehi, Mostafa & Veisi, Hadi & Jalili, Mahdi, 2018. "A fuzzy logic based estimator for respondent driven sampling of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 42-51.
    3. Chien-Min Huang & F. Jay Breidt, 2023. "A dual-frame approach for estimation with respondent-driven samples," METRON, Springer;Sapienza Università di Roma, vol. 81(1), pages 65-81, April.
    4. Lisa Avery & Alison Macpherson & Sarah Flicker & Michael Rotondi, 2021. "A review of reported network degree and recruitment characteristics in respondent driven sampling implications for applied researchers and methodologists," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-19, April.
    5. Dongah Kim & Krista J. Gile & Honoria Guarino & Pedro Mateu‐Gelabert, 2021. "Inferring bivariate association from respondent‐driven sampling data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 415-433, March.
    6. Florence Samkange-Zeeb & Ronja Foraita & Stefan Rach & Tilman Brand, 2019. "Feasibility of using respondent-driven sampling to recruit participants in superdiverse neighbourhoods for a general health survey," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 64(3), pages 451-459, April.
    7. Malmros Jens & Masuda Naoki & Britton Tom, 2016. "Random Walks on Directed Networks: Inference and Respondent-Driven Sampling," Journal of Official Statistics, Sciendo, vol. 32(2), pages 433-459, June.
    8. Yakir Berchenko & Jonathan D. Rosenblatt & Simon D. W. Frost, 2017. "Modeling and analyzing respondent‐driven sampling as a counting process," Biometrics, The International Biometric Society, vol. 73(4), pages 1189-1198, December.
    9. Merli, M. Giovanna & Moody, James & Smith, Jeffrey & Li, Jing & Weir, Sharon & Chen, Xiangsheng, 2015. "Challenges to recruiting population representative samples of female sex workers in China using Respondent Driven Sampling," Social Science & Medicine, Elsevier, vol. 125(C), pages 79-93.
    10. Barash Vladimir D. & Cameron Christopher J. & Spiller Michael W. & Heckathorn Douglas D., 2016. "Respondent-Driven Sampling – Testing Assumptions: Sampling with Replacement," Journal of Official Statistics, Sciendo, vol. 32(1), pages 29-73, March.
    11. Nicky McCreesh & Andrew Copas & Janet Seeley & Lisa G Johnston & Pam Sonnenberg & Richard J Hayes & Simon D W Frost & Richard G White, 2013. "Respondent Driven Sampling: Determinants of Recruitment and a Method to Improve Point Estimation," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-9, October.
    12. Aronow, Peter M. & Crawford, Forrest W., 2015. "Nonparametric identification for respondent-driven sampling," Statistics & Probability Letters, Elsevier, vol. 106(C), pages 100-102.
    13. Ali Mirzazadeh & Yea-Hung Chen & Jess Lin & Katie Burk & Erin C Wilson & Desmond Miller & Danielle Veloso & Willi McFarland & Meghan D Morris, 2021. "Progress toward closing gaps in the hepatitis C virus cascade of care for people who inject drugs in San Francisco," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-11, April.
    14. Thespina J. Yamanis & M. Giovanna Merli & William Whipple Neely & Felicia Feng Tian & James Moody & Xiaowen Tu & Ersheng Gao, 2013. "An Empirical Analysis of the Impact of Recruitment Patterns on RDS Estimates among a Socially Ordered Population of Female Sex Workers in China," Sociological Methods & Research, , vol. 42(3), pages 392-425, August.
    15. Hernández, Hugo & Quiroz, Gabriel & Zambrano, Omar & Zanoni, Wladimir, 2023. "Measuring Labor Market Discrimination against LGTBQ+ in the Case of Ecuador: A Field Experiment," IDB Publications (Working Papers) 12977, Inter-American Development Bank.
    16. Mark S. Handcock & Krista J. Gile & Corinne M. Mar, 2015. "Estimating the size of populations at high risk for HIV using respondent-driven sampling data," Biometrics, The International Biometric Society, vol. 71(1), pages 258-266, March.
    17. Schonlau, Matthias & Liebau, Elisabeth, 2012. "Respondent-Driven Sampling," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 12(1), pages 72-93.
    18. Lee Sunghee & Suzer-Gurtekin Tuba & Wagner James & Valliant Richard, 2017. "Total Survey Error and Respondent Driven Sampling: Focus on Nonresponse and Measurement Errors in the Recruitment Process and the Network Size Reports and Implications for Inferences," Journal of Official Statistics, Sciendo, vol. 33(2), pages 335-366, June.
    19. Matthias Schonlau & Elisabeth Liebau, 2012. "Respondent-driven sampling," Stata Journal, StataCorp LP, vol. 12(1), pages 72-93, March.
    20. Zanoni, Wladimir & Hernández, Hugo & Zambrano, Omar & Quiroz, Gabriel, 2024. "Discrimination against lesbian and gay job seekers: An artefactual field experiment in urban Ecuador," Labour Economics, Elsevier, vol. 88(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0207507. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.