IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v63y2007i1p272-279.html
   My bibliography  Save this article

Robustness of Prevalence Estimates Derived from Misclassified Data from Administrative Databases

Author

Listed:
  • Martin Ladouceur
  • Elham Rahme
  • Christian A. Pineau
  • Lawrence Joseph

Abstract

No abstract is available for this item.

Suggested Citation

  • Martin Ladouceur & Elham Rahme & Christian A. Pineau & Lawrence Joseph, 2007. "Robustness of Prevalence Estimates Derived from Misclassified Data from Administrative Databases," Biometrics, The International Biometric Society, vol. 63(1), pages 272-279, March.
    • Handle: RePEc:bla:biomet:v:63:y:2007:i:1:p:272-279
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2006.00665.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. Sander Greenland, 2005. "Multiple‐bias modelling for analysis of observational data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 267-306, March.
    2. Nandini Dendukuri & Lawrence Joseph, 2001. "Bayesian Approaches to Modeling the Conditional Dependence Between Multiple Diagnostic Tests," Biometrics, The International Biometric Society, vol. 57(1), pages 158-167, March.
    3. E. Rahme & L. Joseph & T. W. Gyorkos, 2000. "Bayesian sample size determination for estimating binomial parameters from data subject to misclassification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(1), pages 119-128.
    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. Dlugosz, Stephan & Mammen, Enno & Wilke, Ralf A., 2017. "Generalized partially linear regression with misclassified data and an application to labour market transitions," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 145-159.
    2. Steve Gallivan & Christina Pagel, 2008. "Modelling of errors in databases," Health Care Management Science, Springer, vol. 11(1), pages 35-40, March.
    3. Mohammad Ehsanul Karim & Paul Gustafson, 2016. "Hypothesis Testing for an Exposure–Disease Association in Case–Control Studies Under Nondifferential Exposure Misclassification in the Presence of Validation Data: Bayesian and Frequentist Adjustments," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 234-252, October.
    4. Mingkai Peng & Guanmin Chen & Lisa M Lix & Finlay A McAlister & Karen Tu & Norm R Campbell & Brenda R Hemmelgarn & Lawrence W Svenson & Hude Quan & Hypertension Outcomes Surveillance Team, 2015. "Refining Hypertension Surveillance to Account for Potentially Misclassified Cases," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-11, March.
    5. Edlira Skrami & Flavia Carle & Simona Villani & Paola Borrelli & Antonella Zambon & Giovanni Corrao & Paolo Trerotoli & Vincenzo Guardabasso & Rosaria Gesuita, 2019. "Availability of Real-World Data in Italy: A Tool to Navigate Regional Healthcare Utilization Databases," IJERPH, MDPI, vol. 17(1), pages 1-12, 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. Paul Gustafson, 2006. "Sample size implications when biases are modelled rather than ignored," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(4), pages 865-881, October.
    2. Nandini Dendukuri & Elham Rahme & Patrick Bélisle & Lawrence Joseph, 2004. "Bayesian Sample Size Determination for Prevalence and Diagnostic Test Studies in the Absence of a Gold Standard Test," Biometrics, The International Biometric Society, vol. 60(2), pages 388-397, June.
    3. Lawrence C. McCandless & Sylvia Richardson & Nicky Best, 2012. "Adjustment for Missing Confounders Using External Validation Data and Propensity Scores," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 40-51, March.
    4. Julian P. T. Higgins & Simon G. Thompson & David J. Spiegelhalter, 2009. "A re‐evaluation of random‐effects meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 137-159, January.
    5. Nandini Dendukuri & Ian Schiller & Lawrence Joseph & Madhukar Pai, 2012. "Bayesian Meta-Analysis of the Accuracy of a Test for Tuberculous Pleuritis in the Absence of a Gold Standard Reference," Biometrics, The International Biometric Society, vol. 68(4), pages 1285-1293, December.
    6. Douglas E. Schaubel & Guanghui Wei, 2011. "Double Inverse-Weighted Estimation of Cumulative Treatment Effects Under Nonproportional Hazards and Dependent Censoring," Biometrics, The International Biometric Society, vol. 67(1), pages 29-38, March.
    7. Adam Branscum & Timothy Hanson & Ian Gardner, 2008. "Bayesian non-parametric models for regional prevalence estimation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(5), pages 567-582.
    8. Carlos Díaz-Venegas, 2014. "Identifying the Confounders of Marginalization and Mortality in Mexico, 2003–2007," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 118(2), pages 851-875, September.
    9. A. Goubar & A. E. Ades & D. De Angelis & C. A. McGarrigle & C. H. Mercer & P. A. Tookey & K. Fenton & O. N. Gill, 2008. "Estimates of human immunodeficiency virus prevalence and proportion diagnosed based on Bayesian multiparameter synthesis of surveillance data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 541-580, June.
    10. Sander Greenland, 2005. "Multiple‐bias modelling for analysis of observational data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 267-306, March.
    11. Geoffrey Jones & Wesley O. Johnson, 2016. "A Bayesian Superpopulation Approach to Inference for Finite Populations Based on Imperfect Diagnostic Outcomes," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(2), pages 314-327, June.
    12. McCandless Lawrence C., 2012. "Meta-Analysis of Observational Studies with Unmeasured Confounders," The International Journal of Biostatistics, De Gruyter, vol. 8(2), pages 1-31, January.
    13. Hae-Young Kim & Michael G. Hudgens & Jonathan M. Dreyfuss & Daniel J. Westreich & Christopher D. Pilcher, 2007. "Comparison of Group Testing Algorithms for Case Identification in the Presence of Test Error," Biometrics, The International Biometric Society, vol. 63(4), pages 1152-1163, December.
    14. Maria Gheorghe & Susan Picavet & Monique Verschuren & Werner B. F. Brouwer & Pieter H. M. Baal, 2017. "Health losses at the end of life: a Bayesian mixed beta regression approach," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 723-749, June.
    15. Fabio Principato & Angela Vullo & Domenica Matranga, 2010. "On implementation of the Gibbs sampler for estimating the accuracy of multiple diagnostic tests," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(8), pages 1335-1354.
    16. Martijn van Hasselt & Christopher R. Bollinger & Jeremy W. Bray, 2022. "A Bayesian approach to account for misclassification in prevalence and trend estimation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 351-367, March.
    17. K. M. Rhodes & J. Savović & R. Elbers & H. E. Jones & J. P. T. Higgins & J. A. C. Sterne & N. J. Welton & R. M. Turner, 2020. "Adjusting trial results for biases in meta‐analysis: combining data‐based evidence on bias with detailed trial assessment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(1), pages 193-209, January.
    18. Andrew Gelman & Christian Hennig, 2017. "Beyond subjective and objective in statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 967-1033, October.
    19. Boris G. Zaslavsky, 2013. "Bayesian Hypothesis Testing in Two-Arm Trials with Dichotomous Outcomes," Biometrics, The International Biometric Society, vol. 69(1), pages 157-163, March.
    20. Rebecca M. Turner & David J. Spiegelhalter & Gordon C. S. Smith & Simon G. Thompson, 2009. "Bias modelling in evidence synthesis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 21-47, January.

    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:1:p:272-279. 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.