  Continuous biomarkers are common for disease screening and diagnosis. To
reach a dichotomous clinical decision, a threshold would be imposed to
distinguish subjects with disease from non-diseased individuals. Among various
performance metrics for a continuous biomarker, specificity at a controlled
sensitivity level (or vice versa) is often desirable for clinical utility since
it directly targets where the clinical test is intended to operate. Covariates,
such as age, race, and sample collection, could impact the controlled
sensitivity level in subpopulations and may also confound the association
between biomarker and disease status. Therefore, covariate adjustment is
important in such biomarker evaluation. In this paper, we suggest to adopt a
parsimonious quantile regression model for the diseased population, locally at
the controlled sensitivity level, and assess specificity with
covariate-specific control of the sensitivity. Variance estimates are obtained
from a sample-based approach and bootstrap. Furthermore, our proposed local
model extends readily to a global one for covariate adjustment for the receiver
operating characteristic (ROC) curve over the sensitivity continuum. We
demonstrate computational efficiency of this proposed method and restore the
inherent monotonicity in the estimated covariate-adjusted ROC curve. The
asymptotic properties of the proposed estimators are established. Simulation
studies show favorable performance of the proposal. Finally, we illustrate our
method in biomarker evaluation for aggressive prostate cancer.
