IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v56y2000i4p1126-1133.html
   My bibliography  Save this article

Regression Models for Disease Prevalence with Diagnostic Tests on Pools of Serum Samples

Author

Listed:
  • S. Vansteelandt
  • E. Goetghebeur
  • T. Verstraeten

Abstract

No abstract is available for this item.

Suggested Citation

  • S. Vansteelandt & E. Goetghebeur & T. Verstraeten, 2000. "Regression Models for Disease Prevalence with Diagnostic Tests on Pools of Serum Samples," Biometrics, The International Biometric Society, vol. 56(4), pages 1126-1133, December.
  • Handle: RePEc:bla:biomet:v:56:y:2000:i:4:p:1126-1133
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2000.01126.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ron Brookmeyer, 1999. "Analysis of Multistage Pooling Studies of Biological Specimens for Estimating Disease Incidence and Prevalence," Biometrics, The International Biometric Society, vol. 55(2), pages 608-612, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xianzheng Huang & Md Shamim Sarker Warasi, 2017. "Maximum Likelihood Estimators in Regression Models for Error-prone Group Testing Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(4), pages 918-931, December.
    2. Dewei Wang & Xichen Mou & Yan Liu, 2022. "Varying‐coefficient regression analysis for pooled biomonitoring," Biometrics, The International Biometric Society, vol. 78(4), pages 1328-1341, December.
    3. A. Delaigle & P. Hall & J. R. Wishart, 2014. "New approaches to nonparametric and semiparametric regression for univariate and multivariate group testing data," Biometrika, Biometrika Trust, vol. 101(3), pages 567-585.
    4. Md S. Warasi & Laura L. Hungerford & Kevin Lahmers, 2022. "Optimizing Pooled Testing for Estimating the Prevalence of Multiple Diseases," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(4), pages 713-727, December.
    5. Emily M. Mitchell & Robert H. Lyles & Amita K. Manatunga & Michelle Danaher & Neil J. Perkins & Enrique F. Schisterman, 2014. "Regression for skewed biomarker outcomes subject to pooling," Biometrics, The International Biometric Society, vol. 70(1), pages 202-211, March.
    6. Pritha Guha, 2022. "Application of Pooled Testing Methodologies in Tackling the COVID-19 Pandemic," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 47(1), pages 7-21, February.
    7. Christopher S. McMahan & Joshua M. Tebbs & Christopher R. Bilder, 2012. "Informative Dorfman Screening," Biometrics, The International Biometric Society, vol. 68(1), pages 287-296, March.
    8. Xianzheng Huang & Joshua M. Tebbs, 2009. "On Latent-Variable Model Misspecification in Structural Measurement Error Models for Binary Response," Biometrics, The International Biometric Society, vol. 65(3), pages 710-718, September.
    9. D. Wang & C. S. McMahan & C. M. Gallagher & K. B. Kulasekera, 2014. "Semiparametric group testing regression models," Biometrika, Biometrika Trust, vol. 101(3), pages 587-598.
    10. Chase N. Joyner & Christopher S. McMahan & Joshua M. Tebbs & Christopher R. Bilder, 2020. "From mixed effects modeling to spike and slab variable selection: A Bayesian regression model for group testing data," Biometrics, The International Biometric Society, vol. 76(3), pages 913-923, September.
    11. Yaakov Malinovsky & Paul S. Albert & Enrique F. Schisterman, 2012. "Pooling Designs for Outcomes under a Gaussian Random Effects Model," Biometrics, The International Biometric Society, vol. 68(1), pages 45-52, March.
    12. Peng Chen & Joshua M. Tebbs & Christopher R. Bilder, 2009. "Group Testing Regression Models with Fixed and Random Effects," Biometrics, The International Biometric Society, vol. 65(4), pages 1270-1278, December.
    13. Nguyen, Ngoc T. & Bish, Ebru K. & Bish, Douglas R., 2021. "Optimal pooled testing design for prevalence estimation under resource constraints," Omega, Elsevier, vol. 105(C).
    14. Karl B. Gregory & Dewei Wang & Christopher S. McMahan, 2019. "Adaptive elastic net for group testing," Biometrics, The International Biometric Society, vol. 75(1), pages 13-23, March.
    15. Xinlei Zuo & Juan Ding & Junjian Zhang & Wenjun Xiong, 2024. "Nonparametric Additive Regression for High-Dimensional Group Testing Data," Mathematics, MDPI, vol. 12(5), pages 1-21, February.
    16. Hui Lin & Adam Zhu & Chong Wang, 2024. "Statistical Tests for Proportion Difference in One-to-Two Matched Binary Diagnostic Data: Application to Environmental Testing of Salmonella in the United States," Mathematics, MDPI, vol. 12(5), pages 1-12, March.
    17. Christopher S. McMahan & Joshua M. Tebbs & Timothy E. Hanson & Christopher R. Bilder, 2017. "Bayesian regression for group testing data," Biometrics, The International Biometric Society, vol. 73(4), pages 1443-1452, December.

    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. Graham Hepworth & Brad J. Biggerstaff, 2017. "Bias Correction in Estimating Proportions by Pooled Testing," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(4), pages 602-614, December.
    2. Peng Chen & Joshua M. Tebbs & Christopher R. Bilder, 2009. "Group Testing Regression Models with Fixed and Random Effects," Biometrics, The International Biometric Society, vol. 65(4), pages 1270-1278, December.
    3. Francheska M. Merced-Nieves & Kelsey L. C. Dzwilewski & Andrea Aguiar & Salma Musaad & Susan A. Korrick & Susan L. Schantz, 2021. "Associations of Prenatal Exposure to Phthalates with Measures of Cognition in 4.5-Month-Old Infants," IJERPH, MDPI, vol. 18(4), pages 1-13, February.
    4. Robert H. Lyles & Dane Van Domelen & Emily M. Mitchell & Enrique F. Schisterman, 2015. "A Discriminant Function Approach to Adjust for Processing and Measurement Error When a Biomarker is Assayed in Pooled Samples," IJERPH, MDPI, vol. 12(11), pages 1-18, November.

    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:bla:biomet:v:56:y:2000:i:4:p:1126-1133. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.