Semiparametric Estimation of the Covariate-Specific ROC Curve in Presence of Ignorable Verification Bias
Version of Record online: 1 MAR 2011
© 2011, The International Biometric Society No claim to original US Federal works
Volume 67, Issue 3, pages 906–916, September 2011
How to Cite
Liu, D. and Zhou, X.-H. (2011), Semiparametric Estimation of the Covariate-Specific ROC Curve in Presence of Ignorable Verification Bias. Biometrics, 67: 906–916. doi: 10.1111/j.1541-0420.2011.01562.x
- Issue online: 14 SEP 2011
- Version of Record online: 1 MAR 2011
- Received January 2010. Revised September 2010. Accepted September 2010.
- Alzheimer's disease;
- Covariate-specific ROC curve;
- Ignorable missingness;
- Verification bias;
- Weighted estimating equations
Summary Covariate-specific receiver operating characteristic (ROC) curves are often used to evaluate the classification accuracy of a medical diagnostic test or a biomarker, when the accuracy of the test is associated with certain covariates. In many large-scale screening tests, the gold standard is subject to missingness due to high cost or harmfulness to the patient. In this article, we propose a semiparametric estimation of the covariate-specific ROC curves with a partial missing gold standard. A location-scale model is constructed for the test result to model the covariates' effect, but the residual distributions are left unspecified. Thus the baseline and link functions of the ROC curve both have flexible shapes. With the gold standard missing at random (MAR) assumption, we consider weighted estimating equations for the location-scale parameters, and weighted kernel estimating equations for the residual distributions. Three ROC curve estimators are proposed and compared, namely, imputation-based, inverse probability weighted, and doubly robust estimators. We derive the asymptotic normality of the estimated ROC curve, as well as the analytical form of the standard error estimator. The proposed method is motivated and applied to the data in an Alzheimer's disease research.