Evaluating classification performance of biomarkers in two-phase case-control studies.
Abstact
Biomarkers are playing an increasingly important role in disease screening, early detection, and risk prediction. The two-phase case-control sampling study design is widely used for the evaluation of candidate biomarkers. The sampling probabilities for cases and controls in the second phase can often depend on other covariates (sampling strata). This biased sampling can lead to invalid inference on a biomarker's classification accuracy if not properly accounted for. In this paper, we adopt the idea of inverse probability weighting and develop inverse probability weighting-based estimators for various measures of a biomarker's classification performance, including the points on the receiver operating characteristics (ROCs) curve, the area under the ROC curve (area under the curve), and the partial area under the curve. In particular, we consider classification accuracy estimators using sampling weights estimated conditionally on sampling strata and further improve their efficiency through the use of estimated weights that additionally take into account the auxiliary variables available from the phase-one cohort. We develop asymptotic properties of the proposed estimators and provide analytical variance for making inference. Extensive simulation studies demonstrate excellent performance of the proposed weighted estimators, while the traditional empirical estimator can be severely biased. We also investigate the advantages in efficiency gain for estimating various classification accuracy estimators through the use of auxiliary variables in addition to sampling strata and apply the proposed method to examples from a renal artery stenosis study and a prostate cancer study.