Nonparametric empirical Bayes biomarker imputation and estimation

• Published in: Statistics in medicine Vol. 43; no. 19; pp. 3742 - 3758
• Main Authors: Barbehenn, Alton, Zhao, Sihai Dave
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 30.08.2024; Wiley Subscription Services, Inc
• Subjects: Bayes Theorem; Biomarkers - analysis; Computer Simulation; Data Interpretation, Statistical; empirical Bayes; Humans; left‐censored data; missing not at random; Models, Statistical; multiple imputation; nonparametric maximum likelihood; shrinkage estimation; Statistics, Nonparametric; missing not at random; multiple imputation; nonparametric maximum likelihood; shrinkage estimation; empirical Bayes; left‐censored data
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.10150

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.