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.
Wang
Xiaofei
X
Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina, USA.
Wu
Yougui
Y
Zhou
Haibo
H
D015415:Biomarkers; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D018579:Patient Selection
D015415:Biomarkers