IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v57y2001i2p610-619.html
   My bibliography  Save this article

Latent Class Modeling Approaches for Assessing Diagnostic Error without a Gold Standard: With Applications to p53 Immunohistochemical Assays in Bladder Tumors

Author

Listed:
  • Paul S. Albert
  • Lisa M. McShane
  • Joanna H. Shih

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:biomet:v:57:y:2001:i:2:p:610-619
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2001.00610.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. Rolf Langeheine & Jeroen Pannekoek & Frank Van De Pol, 1996. "Bootstrapping Goodness-of-Fit Measures in Categorical Data Analysis," Sociological Methods & Research, , vol. 24(4), pages 492-516, May.
    2. Clifford Clogg & Leo Goodman, 1986. "On scaling models applied to data from several groups," Psychometrika, Springer;The Psychometric Society, vol. 51(1), pages 123-135, March.
    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. David R Blair & Kanix Wang & Svetlozar Nestorov & James A Evans & Andrey Rzhetsky, 2014. "Quantifying the Impact and Extent of Undocumented Biomedical Synonymy," PLOS Computational Biology, Public Library of Science, vol. 10(9), pages 1-17, September.
    2. 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.
    3. Clara Schoneberg & Jens Böttcher & Britta Janowetz & Anja Rostalski & Lothar Kreienbrock & Amely Campe, 2022. "An intercomparison study of ELISAs for the detection of porcine reproductive and respiratory syndrome virus – evaluating six conditionally dependent tests," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-16, January.
    4. 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.
    5. 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.
    6. 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.
    7. Huiping Xu & Bruce A. Craig, 2009. "A Probit Latent Class Model with General Correlation Structures for Evaluating Accuracy of Diagnostic Tests," Biometrics, The International Biometric Society, vol. 65(4), pages 1145-1155, December.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.

    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. Fetene B. Tekle & Dereje W. Gudicha & Jeroen K. Vermunt, 2016. "Power analysis for the bootstrap likelihood ratio test for the number of classes in latent class models," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 209-224, June.
    2. Michael Gillespie & Elisabeth Tenvergert & Johannes Kingma, 1988. "Using Mokken methods to develop robust cross-national scales: American and West German attitudes toward abortion," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 20(2), pages 181-203, April.
    3. Geoffrey Evans & Colin Mills, 1998. "Assessing the Cross-Sex Validity of the Goldthorpe Class Schema Using Log-linear Models with Latent Variables," Quality & Quantity: International Journal of Methodology, Springer, vol. 32(3), pages 275-296, August.
    4. James W. Shockey, 1988. "Adjusting for Response Error in Panel Surveys," Sociological Methods & Research, , vol. 17(1), pages 65-92, August.
    5. Matthew J. Madison & Laine P. Bradshaw, 2018. "Assessing Growth in a Diagnostic Classification Model Framework," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 963-990, December.
    6. Pasquale Anselmi & Egidio Robusto & Luca Stefanutti & Debora Chiusole, 2016. "An Upgrading Procedure for Adaptive Assessment of Knowledge," Psychometrika, Springer;The Psychometric Society, vol. 81(2), pages 461-482, June.
    7. Huiping Xu & Bruce A. Craig, 2009. "A Probit Latent Class Model with General Correlation Structures for Evaluating Accuracy of Diagnostic Tests," Biometrics, The International Biometric Society, vol. 65(4), pages 1145-1155, December.
    8. repec:jss:jstsof:42:i10 is not listed on IDEAS
    9. McCutcheon, A.L., 1993. "Multi-sample latent logit models with polytomous effects variables," WORC Paper 93.08.014/7, Tilburg University, Work and Organization Research Centre.
    10. Li, Xiaoshu & Boyle, Kevin J. & Holmes, Thomas P. & LaRouche, Genevieve Pullis, 2014. "The effect of on-site forest experience on stated preferences for low-impact timber harvesting programs," Journal of Forest Economics, Elsevier, vol. 20(4), pages 348-362.
    11. George Macready & C. Mitchell Dayton, 1992. "The application of latent class models in adaptive testing," Psychometrika, Springer;The Psychometric Society, vol. 57(1), pages 71-88, March.
    12. Rolf Langeheine & Frank Van De Pol, 1990. "A Unifying Framework for Markov Modeling in Discrete Space and Discrete Time," Sociological Methods & Research, , vol. 18(4), pages 416-441, May.
    13. Anton K. Formann & Thomas Kohlmann, 1998. "Structural Latent Class Models," Sociological Methods & Research, , vol. 26(4), pages 530-565, May.

    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:57:y:2001:i:2:p:610-619. 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.