Journal
STATISTICAL METHODS IN MEDICAL RESEARCH
Volume 29, Issue 12, Pages 3457-3491Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0962280220929042
Keywords
Bayesian inference; confidence intervals; empirical likelihood; influence function; sensitivity
Categories
Funding
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health) [U01 AG024904]
- DOD ADNI (Department of Defense) [W81XWH-122-0012]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- Alzheimers Association
- Alzheimers Drug Discovery Foundation
- Araclon Biotech
- Biogen
- Bristol-Myers Squibb Company
- CereSpir, Inc.
- Cogstate
- Elan Pharmaceuticals, Inc.
- Eli Lilly and Company
- EuroImmun
- F. Hoffmann-La Roche Ltd
- Canadian Institutes of Health Research
- ADNI clinical sites in Canada
- AbbVie
- BioClinica, Inc.
- Eisai Inc.
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In medical diagnostic studies, a diagnostic test can be evaluated based on its sensitivity under a desired specificity. Existing methods for inference on sensitivity include normal approximation-based approaches and empirical likelihood (EL)-based approaches. These methods generally have poor performance when the specificity is high, and some require choosing smoothing parameters. We propose a new influence function-based empirical likelihood method and Bayesian empirical likelihood methods to overcome such problems. Numerical studies are performed to compare the finite sample performance of the proposed approaches with existing methods. The proposed methods are shown to perform better in terms of both coverage probability and interval length. A real data set from Alzheimer's Disease Neuroimaging Initiative (ANDI) is analyzed.
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