IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v60y2004i2p388-397.html
   My bibliography  Save this article

Bayesian Sample Size Determination for Prevalence and Diagnostic Test Studies in the Absence of a Gold Standard Test

Author

Listed:
  • Nandini Dendukuri
  • Elham Rahme
  • Patrick Bélisle
  • Lawrence Joseph

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:biomet:v:60:y:2004:i:2:p:388-397
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2004.00183.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. 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.
    2. Paul Gustafson & Nhu D. Le & Refik Saskin, 2001. "Case–Control Analysis with Partial Knowledge of Exposure Misclassification Probabilities," Biometrics, The International Biometric Society, vol. 57(2), pages 598-609, June.
    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. M. Rosário Oliveira & Ana Subtil & Luzia Gonçalves, 2020. "Common Medical and Statistical Problems: The Dilemma of the Sample Size Calculation for Sensitivity and Specificity Estimation," Mathematics, MDPI, vol. 8(8), pages 1-17, August.
    2. Roldán Nofuentes, J.A. & Luna del Castillo, J.D. & Montero Alonso, M.A., 2009. "Determining sample size to evaluate and compare the accuracy of binary diagnostic tests in the presence of partial disease verification," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 742-755, January.
    3. Beavers, Daniel P. & Stamey, James D., 2012. "Bayesian sample size determination for binary regression with a misclassified covariate and no gold standard," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2574-2582.
    4. Paul Gustafson & Sander Greenland, 2006. "The Performance of Random Coefficient Regression in Accounting for Residual Confounding," Biometrics, The International Biometric Society, vol. 62(3), pages 760-768, September.
    5. Stamey, James & Gerlach, Richard, 2007. "Bayesian sample size determination for case-control studies with misclassification," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2982-2992, March.
    6. Luzia Gonçalves & M. Rosário de Oliveira & Cláudia Pascoal & Ana Pires, 2012. "Sample size for estimating a binomial proportion: comparison of different methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2453-2473, July.
    7. 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.
    8. Stamey, James D. & Boese, Doyle H. & Young, Dean M., 2008. "Confidence intervals for parameters of two diagnostic tests in the absence of a gold standard," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1335-1346, January.
    9. 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.
    10. 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.
    11. Paul Gustafson, 2007. "Measurement error modelling with an approximate instrumental variable," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 797-815, November.
    12. 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.
    13. Zhuoyu Wang & Nandini Dendukuri & Madhukar Pai & Lawrence Joseph, 2017. "Taking Costs and Diagnostic Test Accuracy into Account When Designing Prevalence Studies: An Application to Childhood Tuberculosis Prevalence," Medical Decision Making, , vol. 37(8), pages 922-929, November.

    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 & Sander Greenland, 2006. "The Performance of Random Coefficient Regression in Accounting for Residual Confounding," Biometrics, The International Biometric Society, vol. 62(3), pages 760-768, September.
    2. 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.
    3. Gustafson Paul, 2010. "Bayesian Inference for Partially Identified Models," The International Journal of Biostatistics, De Gruyter, vol. 6(2), pages 1-20, March.
    4. 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.
    5. Al-Kandari Noriah M. & Lahiri Partha, 2016. "Prediction of a Function of Misclassified Binary Data," Statistics in Transition New Series, Polish Statistical Association, vol. 17(3), pages 429-447, September.
    6. 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.
    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. Paul Gustafson & Nhu D. Le, 2002. "Comparing the Effects of Continuous and Discrete Covariate Mismeasurement, with Emphasis on the Dichotomization of Mismeasured Predictors," Biometrics, The International Biometric Society, vol. 58(4), pages 878-887, December.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. O’Neill, Donal, 2015. "Measuring obesity in the absence of a gold standard," Economics & Human Biology, Elsevier, vol. 17(C), pages 116-128.
    16. Paul Gustafson & Nhu D. Le & Refik Saskin, 2001. "Case–Control Analysis with Partial Knowledge of Exposure Misclassification Probabilities," Biometrics, The International Biometric Society, vol. 57(2), pages 598-609, June.
    17. Wang Dongxu & Shen Tian & Gustafson Paul, 2012. "Partial Identification arising from Nondifferential Exposure Misclassification: How Informative are Data on the Unlikely, Maybe, and Likely Exposed?," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-27, November.
    18. 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.
    19. 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.
    20. Scott Weichenthal & Lawrence Joseph & Patrick Bélisle & André Dufresne, 2010. "Bayesian Estimation of the Probability of Asbestos Exposure from Lung Fiber Counts," Biometrics, The International Biometric Society, vol. 66(2), pages 603-612, June.

    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:60:y:2004:i:2:p:388-397. 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.