Nearest-Neighbor Estimation for ROC Analysis under Verification Bias

The International Journal of Biostatistics
Gianfranco Adimari, Monica Chiogna

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 ...Continue Reading

References

Jun 1, 1987·Statistics in Medicine·C B Begg
Jan 1, 1999·Statistical Methods in Medical Research·X H Zhou
Aug 20, 2003·Biostatistics·Todd A AlonzoThomas Lumley
Jul 10, 2009·Biometrical Journal. Biometrische Zeitschrift·Ronen FlussAndrea Rotnitzky

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Citations

Apr 12, 2020·Cancer Epidemiology, Biomarkers & Prevention : a Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology·Parichoy Pal ChoudhuryNilanjan Chatterjee

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