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Accounting for Nonignorable Verification Bias in Assessment of Diagnostic Tests

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  • Andrzej S. Kosinski
  • Huiman X. Barnhart

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  • Andrzej S. Kosinski & Huiman X. Barnhart, 2003. "Accounting for Nonignorable Verification Bias in Assessment of Diagnostic Tests," Biometrics, The International Biometric Society, vol. 59(1), pages 163-171, March.
    • Handle: RePEc:bla:biomet:v:59:y:2003:i:1:p:163-171
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    File URL: http://hdl.handle.net/10.1111/1541-0420.00019
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    References listed on IDEAS

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    1. Michael J. Daniels & Joseph W. Hogan, 2000. "Reparameterizing the Pattern Mixture Model for Sensitivity Analyses Under Informative Dropout," Biometrics, The International Biometric Society, vol. 56(4), pages 1241-1248, December.
    2. Geert Molenberghs & Michael G. Kenward & Els Goetghebeur, 2001. "Sensitivity analysis for incomplete contingency tables: the Slovenian plebiscite case," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(1), pages 15-29.
    3. J.A. Knottnerus, 1987. "The Effects of Disease Verification and Referral on the Relationship Between Symptoms and Diseases," Medical Decision Making, , vol. 7(3), pages 139-148, August.
    4. J. G. Ibrahim & S. R. Lipsitz & M.‐H. Chen, 1999. "Missing covariates in generalized linear models when the missing data mechanism is non‐ignorable," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 173-190.
    5. Rothenberg, Thomas J, 1971. "Identification in Parametric Models," Econometrica, Econometric Society, vol. 39(3), pages 577-591, May.
    6. Rotnitzky Andrea & Daniel Scharfstein & Ting‐Li Su & James Robins, 2001. "Methods for Conducting Sensitivity Analysis of Trials with Potentially Nonignorable Competing Causes of Censoring," Biometrics, The International Biometric Society, vol. 57(1), pages 103-113, March.
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    Cited by:

    1. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2019. "Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data," Econometrics, MDPI, vol. 7(3), pages 1-27, September.
    2. Manuela Buzoianu & Joseph B. Kadane, 2009. "Optimal Bayesian Design for Patient Selection in a Clinical Study," Biometrics, The International Biometric Society, vol. 65(3), pages 953-961, September.
    3. Paul S. Albert, 2007. "Imputation Approaches for Estimating Diagnostic Accuracy for Multiple Tests from Partially Verified Designs," Biometrics, The International Biometric Society, vol. 63(3), pages 947-957, September.
    4. 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.
    5. Frederico Z. Poleto & Julio M. Singer & Carlos Daniel Paulino, 2011. "Comparing diagnostic tests with missing data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(6), pages 1207-1222, April.
    6. Danping Liu & Xiao-Hua Zhou, 2010. "A Model for Adjusting for Nonignorable Verification Bias in Estimation of the ROC Curve and Its Area with Likelihood-Based Approach," Biometrics, The International Biometric Society, vol. 66(4), pages 1119-1128, December.
    7. José Antonio Roldán-Nofuentes & Saad Bouh Regad, 2021. "Estimation of the Average Kappa Coefficient of a Binary Diagnostic Test in the Presence of Partial Verification," Mathematics, MDPI, vol. 9(14), pages 1-17, July.
    8. Martinez, Edson Zangiacomi & Alberto Achcar, Jorge & Louzada-Neto, Francisco, 2006. "Estimators of sensitivity and specificity in the presence of verification bias: A Bayesian approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 601-611, November.
    9. Danping Liu & Xiao-Hua Zhou, 2013. "Covariate Adjustment in Estimating the Area Under ROC Curve with Partially Missing Gold Standard," Biometrics, The International Biometric Society, vol. 69(1), pages 91-100, March.
    10. Selin Merdan & Christine L. Barnett & Brian T. Denton & James E. Montie & David C. Miller, 2021. "OR Practice–Data Analytics for Optimal Detection of Metastatic Prostate Cancer," Operations Research, INFORMS, vol. 69(3), pages 774-794, May.

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