Danping Liu
The receiver operating characteristic (ROC) curve is a powerful tool to evaluate the classification accuracy of a biomarker. In many large-scale screening tests, the gold standard is subject to missingness due to high cost or harmfulness to the patient. A complete-case analysis would yield biased results, known as “verification bias”. Under missing at random (MAR) assumption, several methods are proposed to correct for the verification bias based on imputation, inverse probability weighting, or both. The model framework is built on a location-scale model for the marker value, with unspecified residual distribution. Bias-corrected estimation of three different types of ROC curves are discussed: (i) the ordinary ROC curve as a plot of sensitivity and specificity, (ii) the covariate-specific ROC curve when the marker value is associated with individual covariates, and (iii) the adjusted ROC (AROC) curve as an “average” of covariate-specific ROC curves. The proposed method is applied to the data from Alzheimer’s disease research.
powerful tool to evaluate the classification accuracy of a biomarker. In
many large-scale screening tests, the gold standard is subject to
missingness due to high cost or harmfulness to the patient. A
complete-case analysis would yield biased results, known as
“verification bias”. Under missing at random (MAR) assumption, several
methods are proposed to correct for the verification bias based on
imputation, inverse probability weighting, or both. The model framework
is built on a location-scale model for the marker value, with
unspecified residual distribution. Bias-corrected estimation of three
different types of ROC curves are discussed: (i) the ordinary ROC curve
as a plot of sensitivity and specificity, (ii) the covariate-specific
ROC curve when the marker value is associated with individual
covariates, and (iii) the adjusted ROC (AROC) curve as an “average” of
covariate-specific ROC curves. The proposed method is applied to the
data from Alzheimer’s disease research.
