Bayesian and influence function-based empirical likelihoods for inference of sensitivity to the early diseased stage in diagnostic tests.

Bayesian inference confidence interval empirical likelihood influence function sensitivity of the early stage

Journal

Biometrical journal. Biometrische Zeitschrift
ISSN: 1521-4036
Titre abrégé: Biom J
Pays: Germany
ID NLM: 7708048

Informations de publication

Date de publication:
03 2023
Historique:
revised: 11 09 2022
received: 21 01 2022
accepted: 08 10 2022
pmc-release: 01 03 2024
pubmed: 16 1 2023
medline: 15 3 2023
entrez: 15 1 2023
Statut: ppublish

Résumé

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.

Identifiants

pubmed: 36642803
doi: 10.1002/bimj.202200021
pmc: PMC10006346
mid: NIHMS1859085
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

e2200021

Subventions

Organisme : NIA NIH HHS
ID : U01 AG024904
Pays : United States
Organisme : NIA NIH HHS
ID : U19 AG024904
Pays : United States

Informations de copyright

© 2023 Wiley-VCH GmbH.

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Auteurs

Yan Hai (Y)

Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, USA.

Shuangfei Shi (S)

Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, USA.

Gengsheng Qin (G)

Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, USA.
Department of Mathematics and Statistics, Georgia State University, Atlanta, Georgia, USA.

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Classifications MeSH