Covariate adjustment in continuous biomarker assessment
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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.
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