  Verification bias is a well known problem when the predictive ability of a
diagnostic test has to be evaluated. In this paper, we discuss how to assess
the accuracy of continuous-scale diagnostic tests in the presence of
verification bias, when a three-class disease status is considered. In
particular, we propose a fully nonparametric verification bias-corrected
estimator of the ROC surface. Our approach is based on nearest-neighbor
imputation and adopts generic smooth regression models for both the disease and
the verification processes. Consistency and asymptotic normality of the
proposed estimator are proved and its finite sample behavior is investigated by
means of several Monte Carlo simulation studies. Variance estimation is also
discussed and an illustrative example is presented.
