Outcome- and auxiliary-dependent subsampling and its statistical inference

The performance of a biomarker predicting clinical outcome is often evaluated in a large prospective study. Due to high costs associated with bioassay, investigators need to select a subset from all available patients for biomarker assessment. We consider an outcome- and auxiliary-dependent subsampling (OADS) scheme, in which the probability of selecting a patient into the subset depends on the patient's clinical outcome and an auxiliary variable. We proposed a semiparametric empirical likelihood method to estimate the association between biomarker and clinical outcome. Asymptotic properties of the estimator are given. Simulation study shows that the proposed method outperforms alternative methods.