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Assessing accuracy of a continuous screening test in the presence of verification bias

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  • Todd A. Alonzo
  • Margaret Sullivan Pepe

Abstract

Summary. In studies to assess the accuracy of a screening test, often definitive disease assessment is too invasive or expensive to be ascertained on all the study subjects. Although it may be more ethical or cost effective to ascertain the true disease status with a higher rate in study subjects where the screening test or additional information is suggestive of disease, estimates of accuracy can be biased in a study with such a design. This bias is known as verification bias. Verification bias correction methods that accommodate screening tests with binary or ordinal responses have been developed; however, no verification bias correction methods exist for tests with continuous results. We propose and compare imputation and reweighting bias‐corrected estimators of true and false positive rates, receiver operating characteristic curves and area under the receiver operating characteristic curve for continuous tests. Distribution theory and simulation studies are used to compare the proposed estimators with respect to bias, relative efficiency and robustness to model misspecification. The bias correction estimators proposed are applied to data from a study of screening tests for neonatal hearing loss.

Suggested Citation

  • Todd A. Alonzo & Margaret Sullivan Pepe, 2005. "Assessing accuracy of a continuous screening test in the presence of verification bias," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 173-190, January.
  • Handle: RePEc:bla:jorssc:v:54:y:2005:i:1:p:173-190
    DOI: 10.1111/j.1467-9876.2005.00477.x
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    Cited by:

    1. Shanshan Li & Yang Ning, 2015. "Estimation of covariate‐specific time‐dependent ROC curves in the presence of missing biomarkers," Biometrics, The International Biometric Society, vol. 71(3), pages 666-676, September.
    2. 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.
    3. Adimari Gianfranco & Chiogna Monica, 2015. "Nearest-Neighbor Estimation for ROC Analysis under Verification Bias," The International Journal of Biostatistics, De Gruyter, vol. 11(1), pages 109-124, May.
    4. Danping Liu & Xiao-Hua Zhou, 2011. "Semiparametric Estimation of the Covariate-Specific ROC Curve in Presence of Ignorable Verification Bias," Biometrics, The International Biometric Society, vol. 67(3), pages 906-916, September.
    5. Hua He & Wenjuan Wang & Wan Tang, 2017. "Prediction model-based kernel density estimation when group membership is subject to missing," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(3), pages 267-288, July.
    6. 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.
    7. Zhu, Rui & Ghosal, Subhashis, 2019. "Bayesian Semiparametric ROC surface estimation under verification bias," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 40-52.
    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. Paul S. Albert & Aiyi Liu & Tonja Nansel, 2014. "Efficient logistic regression designs under an imperfect population identifier," Biometrics, The International Biometric Society, vol. 70(1), pages 175-184, March.
    10. 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.

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