title: Outcome- and auxiliary-dependent subsampling and its statistical inference.
abstract: 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.
authors.affiliation: Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina, USA.
authors.forename: Xiaofei
authors.initials: X
authors.lastname: Wang
authors.forename: Yougui
authors.initials: Y
authors.lastname: Wu
authors.forename: Haibo
authors.initials: H
authors.lastname: Zhou
mesh_terms: D015415:Biomarkers; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D018579:Patient Selection
chemical_list: D015415:Biomarkers