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Nearest-Neighbor Estimation for ROC Analysis under Verification Bias

Author

Listed:
  • Adimari Gianfranco

    (Department of Statistical Sciences, University of Padova, Via C. Battisti 241, Padova 35121, Italy)

  • Chiogna Monica

    (Department of Statistical Sciences, University of Padova, Via C. Battisti 241, Padova 35121, Italy)

Abstract

For a continuous-scale diagnostic test, the receiver operating characteristic (ROC) curve is a popular tool for displaying the ability of the test to discriminate between healthy and diseased subjects. In some studies, verification of the true disease status is performed only for a subset of subjects, possibly depending on the test result and other characteristics of the subjects. Estimators of the ROC curve based only on this subset of subjects are typically biased; this is known as verification bias. Methods have been proposed to correct verification bias, in particular under the assumption that the true disease status, if missing, is missing at random (MAR). MAR assumption means that the probability of missingness depends on the true disease status only through the test result and observed covariate information. However, the existing methods require parametric models for the (conditional) probability of disease and/or the (conditional) probability of verification, and hence are subject to model misspecification: a wrong specification of such parametric models can affect the behavior of the estimators, which can be inconsistent. To avoid misspecification problems, in this paper we propose a fully nonparametric method for the estimation of the ROC curve of a continuous test under verification bias. The method is based on nearest-neighbor imputation and adopts generic smooth regression models for both the probability that a subject is diseased and the probability that it is verified. Simulation experiments and an illustrative example show the usefulness of the new method. Variance estimation is also discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:ijbist:v:11:y:2015:i:1:p:109-124:n:4
    DOI: 10.1515/ijb-2014-0014
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    References listed on IDEAS

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    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

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