Nonparametric empirical Bayes biomarker imputation and estimation.
Stat Med
; 43(19): 3742-3758, 2024 Aug 30.
Article
en En
| MEDLINE
| ID: mdl-38897921
ABSTRACT
Biomarkers are often measured in bulk to diagnose patients, monitor patient conditions, and research novel drug pathways. The measurement of these biomarkers often suffers from detection limits that result in missing and untrustworthy measurements. Frequently, missing biomarkers are imputed so that down-stream analysis can be conducted with modern statistical methods that cannot normally handle data subject to informative censoring. This work develops an empirical Bayes g $$ g $$ -modeling method for imputing and denoising biomarker measurements. We establish superior estimation properties compared to popular methods in simulations and with real data, providing the useful biomarker measurement estimations for down-stream analysis.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Simulación por Computador
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Biomarcadores
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Teorema de Bayes
Idioma:
En
Revista:
Stat Med
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Stat. med
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Statistics in medicine
Año:
2024
Tipo del documento:
Article