Affiliation:
1. Department of Mathematics and Statistics Georgia State University Atlanta Georgia USA
Abstract
AbstractIn practice, a disease process might involve three ordinal diagnostic stages: the normal healthy stage, the early stage of the disease, and the stage of full development of the disease. Early detection is critical for some diseases since it often means an optimal time window for therapeutic treatments of the diseases. In this study, we propose a new influence function‐based empirical likelihood method and Bayesian empirical likelihood methods to construct confidence/credible intervals for the sensitivity of a test to patients in the early diseased stage given a specificity and a sensitivity of the test to patients in the fully diseased stage. Numerical studies are performed to compare the finite sample performances of the proposed approaches with existing methods. The proposed methods are shown to outperform existing methods in terms of coverage probability. A real dataset from the Alzheimer's Disease Neuroimaging Initiative (ANDI) is used to illustrate the proposed methods.
Funder
DoD Alzheimer's Disease Neuroimaging Initiative
National Institutes of Health
U.S. Department of Defense
National Institute on Aging
National Institute of Biomedical Imaging and Bioengineering
AbbVie
Alzheimer's Drug Discovery Foundation
BioClinica
Biogen
Bristol-Myers Squibb
Eli Lilly and Company
Roche
Genentech
Fujirebio US
GE Healthcare
H. Lundbeck A/S
Merck
Novartis Pharmaceuticals Corporation
Pfizer
Servier
Takeda Pharmaceutical Company
Canadian Institutes of Health Research
Subject
Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability
Cited by
1 articles.
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