Inverse probability weighting estimation of the volume under the ROC surface in the presence of verification bias

• Published in: Biometrical journal Vol. 58; no. 6; pp. 1338 - 1356
• Main Authors: Zhang, Ying, Alonzo, Todd A.
• Format: Journal Article
• Language: English
• Published: Germany Blackwell Publishing Ltd 01.11.2016; Wiley - VCH Verlag GmbH & Co. KGaA
• Subjects: Alzheimer Disease - diagnosis; Alzheimer's disease; Bias; Biometry - methods; Data Interpretation, Statistical; Diagnostic test; Diagnostic tests; Diagnostic Tests, Routine; Humans; Inverse probability weighting; Missing at random; Performance evaluation; Probability; Reproducibility of Results; ROC Curve; Verification bias; VUS; Inverse probability weighting; Diagnostic test; Missing at random; VUS; Verification bias
• ISSN: 0323-3847; 1521-4036; 1521-4036
• DOI: 10.1002/bimj.201500225

In diagnostic medicine, the volume under the receiver operating characteristic (ROC) surface (VUS) is a commonly used index to quantify the ability of a continuous diagnostic test to discriminate between three disease states. In practice, verification of the true disease status may be performed only for a subset of subjects under study since the verification procedure is invasive, risky, or expensive. The selection for disease examination might depend on the results of the diagnostic test and other clinical characteristics of the patients, which in turn can cause bias in estimates of the VUS. This bias is referred to as verification bias. Existing verification bias correction in three‐way ROC analysis focuses on ordinal tests. We propose verification bias‐correction methods to construct ROC surface and estimate the VUS for a continuous diagnostic test, based on inverse probability weighting. By applying U‐statistics theory, we develop asymptotic properties for the estimator. A Jackknife estimator of variance is also derived. Extensive simulation studies are performed to evaluate the performance of the new estimators in terms of bias correction and variance. The proposed methods are used to assess the ability of a biomarker to accurately identify stages of Alzheimer's disease.