Confidence Interval Estimation for Sensitivity to the Early Diseased Stage Based on Empirical Likelihood.
Many disease processes can be divided into three stages: the non-diseased stage: the early diseased stage, and the fully diseased stage. To assess the accuracy of diagnostic tests for such diseases, various summary indexes have been proposed, such as volume under the surface (VUS), partial volume under the surface (PVUS), and the sensitivity to the early diseased stage given specificity and the sensitivity to the fully diseased stage (P2). This paper focuses on confidence interval estimation for P2 based on empirical likelihood. Simulation studies are carried out to assess the performance of the new methods compared to the existing parametric and nonparametric ones. A real dataset from Alzheimer's Disease Neuroimaging Initiative (ADNI) is analyzed.
a Department of Biostatistics , University at Buffalo , Buffalo , New York , USA.
Tuochuan
T
Dong
a Department of Biostatistics , University at Buffalo , Buffalo , New York , USA.
Lili
L
Tian
D000465:Algorithms; D000544:Alzheimer Disease / Q000175:diagnosis / Q000378:metabolism / Q000523:psychology; D015415:Biomarkers; D003198:Computer Simulation; D016001:Confidence Intervals; D004194:Disease; D018450:Disease Progression; D042241:Early Diagnosis; D006801:Humans; D016013:Likelihood Functions; D059906:Neuroimaging / Q000706:statistics & numerical data; D009483:Neuropsychological Tests
D015415:Biomarkers
Diagnostic tests; Empirical likelihood; The sensitivity to the early diseased stage.