IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v63y2007i2p593-602.html
   My bibliography  Save this article

Random Effects Modeling Approaches for Estimating ROC Curves from Repeated Ordinal Tests without a Gold Standard

Author

Listed:
  • Paul S. Albert

Abstract

No abstract is available for this item.

Suggested Citation

  • Paul S. Albert, 2007. "Random Effects Modeling Approaches for Estimating ROC Curves from Repeated Ordinal Tests without a Gold Standard," Biometrics, The International Biometric Society, vol. 63(2), pages 593-602, June.
  • Handle: RePEc:bla:biomet:v:63:y:2007:i:2:p:593-602
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2006.00712.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. Xiao-Hua Zhou & Pete Castelluccio & Chuan Zhou, 2005. "Nonparametric Estimation of ROC Curves in the Absence of a Gold Standard," Biometrics, The International Biometric Society, vol. 61(2), pages 600-609, June.
    2. Tutz, Gerhard & Hennevogl, Wolfgang, 1996. "Random effects in ordinal regression models," Computational Statistics & Data Analysis, Elsevier, vol. 22(5), pages 537-557, September.
    3. Paul S. Albert & Lori E. Dodd, 2004. "A Cautionary Note on the Robustness of Latent Class Models for Estimating Diagnostic Error without a Gold Standard," Biometrics, The International Biometric Society, vol. 60(2), pages 427-435, June.
    4. Paul S. Albert & Lisa M. McShane & Joanna H. Shih, 2001. "Latent Class Modeling Approaches for Assessing Diagnostic Error without a Gold Standard: With Applications to p53 Immunohistochemical Assays in Bladder Tumors," Biometrics, The International Biometric Society, vol. 57(2), pages 610-619, 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. Aurélie Bertrand & Christian Hafner, 2014. "On heterogeneous latent class models with applications to the analysis of rating scores," Computational Statistics, Springer, vol. 29(1), pages 307-330, February.
    2. Cheng, Dunlei & Branscum, Adam J. & Stamey, James D., 2010. "A Bayesian approach to sample size determination for studies designed to evaluate continuous medical tests," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 298-307, February.
    3. Alla Slynko, 2022. "Asymptotic analysis of reliability measures for an imperfect dichotomous test," Statistical Papers, Springer, vol. 63(4), pages 995-1012, August.

    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. Clara Drew & Moses Badio & Dehkontee Dennis & Lisa Hensley & Elizabeth Higgs & Michael Sneller & Mosoka Fallah & Cavan Reilly, 2023. "Simplifying the estimation of diagnostic testing accuracy over time for high specificity tests in the absence of a gold standard," Biometrics, The International Biometric Society, vol. 79(2), pages 1546-1558, June.
    2. Chinyereugo M Umemneku Chikere & Kevin Wilson & Sara Graziadio & Luke Vale & A Joy Allen, 2019. "Diagnostic test evaluation methodology: A systematic review of methods employed to evaluate diagnostic tests in the absence of gold standard – An update," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-25, October.
    3. Bruce D. Spencer, 2012. "When Do Latent Class Models Overstate Accuracy for Diagnostic and Other Classifiers in the Absence of a Gold Standard?," Biometrics, The International Biometric Society, vol. 68(2), pages 559-566, June.
    4. Liu, Wei & Zhang, Bo & Zhang, Zhiwei & Chen, Baojiang & Zhou, Xiao-Hua, 2015. "A pseudo-likelihood approach for estimating diagnostic accuracy of multiple binary medical tests," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 85-98.
    5. Bo Zhang & Zhen Chen & Paul S. Albert, 2012. "Estimating Diagnostic Accuracy of Raters Without a Gold Standard by Exploiting a Group of Experts," Biometrics, The International Biometric Society, vol. 68(4), pages 1294-1302, December.
    6. Elizabeth R. Brown, 2010. "Bayesian Estimation of the Time-Varying Sensitivity of a Diagnostic Test with Application to Mother-to-Child Transmission of HIV," Biometrics, The International Biometric Society, vol. 66(4), pages 1266-1274, December.
    7. Pankaj Patel & Sherry Thatcher & Katerina Bezrukova, 2013. "Organizationally-relevant configurations: the value of modeling local dependence," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(1), pages 287-311, January.
    8. Donal O'Neill & Olive Sweetman, 2013. "Estimating Obesity Rates in Europe in the Presence of Self-Reporting Errors," Economics Department Working Paper Series n236-13.pdf, Department of Economics, National University of Ireland - Maynooth.
    9. Z. Rezaei Ghahroodi & M. Ganjali, 2013. "A Bayesian approach for analysing longitudinal nominal outcomes using random coefficients transitional generalized logit model: an application to the labour force survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(7), pages 1425-1445, July.
    10. O’Neill, Donal, 2015. "Measuring obesity in the absence of a gold standard," Economics & Human Biology, Elsevier, vol. 17(C), pages 116-128.
    11. S. Noorian & M. Ganjali & E. Bahrami Samani, 2016. "A Bayesian test of homogeneity of association parameter using transition modelling of longitudinal mixed responses," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(10), pages 1850-1863, August.
    12. Robert Cudeck, 2005. "Fitting Psychometric Models with Methods Based on Automatic Differentiation," Psychometrika, Springer;The Psychometric Society, vol. 70(4), pages 599-617, December.
    13. Leandro García Barrado & Els Coart & Tomasz Burzykowski, 2017. "Estimation of diagnostic accuracy of a combination of continuous biomarkers allowing for conditional dependence between the biomarkers and the imperfect reference-test," Biometrics, The International Biometric Society, vol. 73(2), pages 646-655, June.
    14. Guan-Hua Huang & Su-Mei Wang & Chung-Chu Hsu, 2011. "Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses," Psychometrika, Springer;The Psychometric Society, vol. 76(4), pages 584-611, October.
    15. Geoffrey Jones & Wesley O. Johnson & Timothy E. Hanson & Ronald Christensen, 2010. "Identifiability of Models for Multiple Diagnostic Testing in the Absence of a Gold Standard," Biometrics, The International Biometric Society, vol. 66(3), pages 855-863, September.
    16. Subtil, Ana & de Oliveira, M. Rosário & Gonçalves, Luzia, 2012. "Conditional dependence diagnostic in the latent class model: A simulation study," Statistics & Probability Letters, Elsevier, vol. 82(7), pages 1407-1412.
    17. Simone, Rosaria & Tutz, Gerhard & Iannario, Maria, 2020. "Subjective heterogeneity in response attitude for multivariate ordinal outcomes," Econometrics and Statistics, Elsevier, vol. 14(C), pages 145-158.
    18. Nasim Vahabi & Anoshirvan Kazemnejad & Somnath Datta, 2018. "A Marginalized Overdispersed Location Scale Model for Clustered Ordinal Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 103-134, December.
    19. Ivy Liu & Alan Agresti, 2005. "The analysis of ordered categorical data: An overview and a survey of recent developments," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(1), pages 1-73, June.
    20. Bach-Mortensen, Anders Malthe & Goodair, Benjamin & Barlow, Jane, 2022. "Outsourcing and children's social care: A longitudinal analysis of inspection outcomes among English children's homes and local authorities," Social Science & Medicine, Elsevier, vol. 313(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:bla:biomet:v:63:y:2007:i:2:p:593-602. 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.