Bayesian and influence function‐based empirical likelihoods for inference of sensitivity to the early diseased stage in diagnostic tests
Abstract
In 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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 15, 2023
- Source ID
- 10.1002/bimj.202200021
Entities
People
- For The Alzheimer's Disease Neuroimaging
- Gengsheng Qin
- Shuangfei Shi
- Yan Hai
Organizations
- AbbVie
- Alzheimer's Drug Discovery Foundation
- BioClinica
- Biogen
- Bristol-Myers Squibb
- Canadian Institutes of Health Research
- Chiron Corporation
- Eli Lilly and Company
- GE HealthCare
- Georgia State University
- Hoffmann-La Roche
- Laboratoires Servier
- Lundbeck
- Merck & Co.
- National Institute of Biomedical Imaging and Bioengineering
- National Institute on Aging
- National Institutes of Health
- Pfizer
- Roche (United States)
- Takeda Pharmaceutical Company
- United States Department of Defense